diff --git a/binder/apt.txt b/binder/apt.txt new file mode 100644 index 0000000..910639e --- /dev/null +++ b/binder/apt.txt @@ -0,0 +1,8 @@ +make +git +gcc +g++ +libopenblas-dev +libfftw3-dev +libsuitesparse-dev +libboost-all-dev \ No newline at end of file diff --git a/binder/postBuild b/binder/postBuild new file mode 100644 index 0000000..ddab7bd --- /dev/null +++ b/binder/postBuild @@ -0,0 +1,7 @@ +set -ex + +git clone https://github.com/phoebe-p/S4 +cd S4 +make S4_pyext +cd .. +rm -rf S4 \ No newline at end of file diff --git a/docs/search.json b/docs/search.json index 1d82c83..91741c7 100644 --- a/docs/search.json +++ b/docs/search.json @@ -67,21 +67,21 @@ "href": "solcore-workshop/notebooks/8-grating_pyramids_OPTOS.html#setting-up", "title": "Section 8: Textured Si", "section": "Setting up", - "text": "Setting up\nFirst, importing relevant packages:\n\nimport numpy as np\nimport os\n\n# solcore imports\nfrom solcore.structure import Layer\nfrom solcore import material\nfrom solcore import si\n\nfrom rayflare.structure import Interface, BulkLayer, Structure\nfrom rayflare.matrix_formalism import process_structure, calculate_RAT\nfrom rayflare.utilities import get_savepath\nfrom rayflare.transfer_matrix_method import tmm_structure\nfrom rayflare.angles import theta_summary, make_angle_vector\nfrom rayflare.textures import regular_pyramids\nfrom rayflare.options import default_options\n\nfrom solcore.material_system import create_new_material\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport seaborn as sns\nfrom sparse import load_npz\n\nTo make sure we are using the same optical constants for Si, load the same Si n/k data used in the paper linked above:\n\ncreate_new_material(\"Si_OPTOS\", \"data/Si_OPTOS_n.txt\", \"data/Si_OPTOS_k.txt\",\n overwrite=True)\n\nMaterial created with optical constants n and k only.\n\n\nYou only need to do this one time, then the material will be stored in Solcore’s material database.\nSetting options (taking the default options for everything not specified explicitly):\n\nangle_degrees_in = 8\n\nwavelengths = np.linspace(900, 1200, 30) * 1e-9\n\nSi = material(\"Si_OPTOS\")()\nAir = material(\"Air\")()\n\noptions = default_options()\noptions.wavelengths = wavelengths\noptions.theta_in = angle_degrees_in * np.pi / 180 # incidence angle (polar angle)\noptions.n_theta_bins = 100\noptions.c_azimuth = 0.25\noptions.n_rays = 25 * 25 * 1300 # number of rays per wavelength in ray-tracing\noptions.project_name = \"OPTOS_comparison\"\noptions.orders = 60 # number of RCWA orders to use (more = better convergence, but slower)\noptions.pol = \"u\" # unpolarized light" + "text": "Setting up\nFirst, importing relevant packages:\n\nimport numpy as np\nimport os\n\n# solcore imports\nfrom solcore.structure import Layer\nfrom solcore import material\nfrom solcore import si\n\nfrom rayflare.structure import Interface, BulkLayer, Structure\nfrom rayflare.matrix_formalism import process_structure, calculate_RAT\nfrom rayflare.utilities import get_savepath\nfrom rayflare.transfer_matrix_method import tmm_structure\nfrom rayflare.angles import theta_summary, make_angle_vector\nfrom rayflare.textures import regular_pyramids\nfrom rayflare.options import default_options\n\nfrom solcore.material_system import create_new_material\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport seaborn as sns\nfrom sparse import load_npz\n\nTo make sure we are using the same optical constants for Si, load the same Si n/k data used in the paper linked above:\n\ncreate_new_material(\"Si_OPTOS\", \"data/Si_OPTOS_n.txt\", \"data/Si_OPTOS_k.txt\",\n overwrite=True)\n\nMaterial created with optical constants n and k only.\n\n\nYou only need to do this one time, then the material will be stored in Solcore’s material database.\nSetting options (taking the default options for everything not specified explicitly):\n\nangle_degrees_in = 8 # same as in Fraunhofer paper\n\nwavelengths = np.linspace(900, 1200, 20) * 1e-9\n\nSi = material(\"Si_OPTOS\")()\nAir = material(\"Air\")()\n\noptions = default_options()\noptions.wavelengths = wavelengths\noptions.theta_in = angle_degrees_in * np.pi / 180 # incidence angle (polar angle)\noptions.n_theta_bins = 50\noptions.c_azimuth = 0.25\noptions.n_rays = 5e5 # number of rays per wavelength in ray-tracing\noptions.project_name = \"OPTOS_comparison\"\noptions.orders = 60 # number of RCWA orders to use (more = better convergence, but slower)\noptions.pol = \"u\" # unpolarized light\noptions.only_incidence_angle = False" }, { "objectID": "solcore-workshop/notebooks/8-grating_pyramids_OPTOS.html#defining-the-structures", "href": "solcore-workshop/notebooks/8-grating_pyramids_OPTOS.html#defining-the-structures", "title": "Section 8: Textured Si", "section": "Defining the structures", - "text": "Defining the structures\nNow, set up the grating basis vectors for the RCWA calculations and define the grating structure. These are squares, rotated by 45 degrees. The halfwidth is calculated based on the area fill factor of the etched pillars given in the paper.\n\nx = 1000\n\nd_vectors = ((x, 0), (0, x))\narea_fill_factor = 0.36\nhw = np.sqrt(area_fill_factor) * 500\n\nback_materials = [\n Layer(width=si(\"120nm\"), material=Si,\n geometry=[{\"type\": \"rectangle\", \"mat\": Air, \"center\": (x / 2, x / 2),\n \"halfwidths\": (hw, hw), \"angle\": 45}],\n )]\n\nNow we define the pyramid texture for the front surface in case (2) and (3) and make the four possible different surfaces: planar front and rear, front with pyramids, rear with grating. We specify the method to use to calculate the redistribution matrices in each case and create the bulk layer.\n\nsurf = regular_pyramids(elevation_angle=55, upright=False)\n\nfront_surf_pyramids = Interface(\n \"RT_Fresnel\",\n texture=surf,\n layers=[],\n name=\"inv_pyramids_front_\" + str(options[\"n_rays\"]),\n)\n\nfront_surf_planar = Interface(\"TMM\", layers=[], name=\"planar_front\")\n\nback_surf_grating = Interface(\n \"RCWA\",\n layers=back_materials,\n name=\"crossed_grating_back\",\n d_vectors=d_vectors,\n rcwa_orders=60,\n)\n\nback_surf_planar = Interface(\"TMM\", layers=[], name=\"planar_back\")\n\nbulk_Si = BulkLayer(201.8e-6, Si, name=\"Si_bulk\")\n\nNow we create the different structures and ‘process’ them (this will calculate the relevant matrices if necessary, or do nothing if it finds the matrices have previously been calculated and the files already exist). We don’t need to process the final structure because it will use matrices calculated for SC_fig6 and SC_fig7.\n\nSC_fig6 = Structure(\n [front_surf_planar, bulk_Si, back_surf_grating], incidence=Air, transmission=Air\n)\nSC_fig7 = Structure(\n [front_surf_pyramids, bulk_Si, back_surf_planar], incidence=Air, transmission=Air\n)\nSC_fig8 = Structure(\n [front_surf_pyramids, bulk_Si, back_surf_grating], incidence=Air, transmission=Air\n)\n\nprocess_structure(SC_fig6, options)\nprocess_structure(SC_fig7, options)\n\nMaking matrix for planar surface using TMM for element 0 in structure\nExisting angular redistribution matrices found\nExisting angular redistribution matrices found\nRCWA calculation for element 2 in structure\nExisting angular redistribution matrices found\nRay tracing with Fresnel equations for element 0 in structure\nExisting angular redistribution matrices found\nExisting angular redistribution matrices found\nMaking matrix for planar surface using TMM for element 2 in structure\nExisting angular redistribution matrices found" + "text": "Defining the structures\nNow, set up the grating basis vectors for the RCWA calculations and define the grating structure. These are squares, rotated by 45 degrees. The halfwidth is calculated based on the area fill factor of the etched pillars given in the paper.\n\nx = 1000\n\nd_vectors = ((x, 0), (0, x))\narea_fill_factor = 0.36\nhw = np.sqrt(area_fill_factor) * 500\n\nback_materials = [\n Layer(width=si(\"120nm\"), material=Si,\n geometry=[{\"type\": \"rectangle\", \"mat\": Air, \"center\": (x / 2, x / 2),\n \"halfwidths\": (hw, hw), \"angle\": 45}],\n )]\n\nNow we define the pyramid texture for the front surface in case (2) and (3) and make the four possible different surfaces: planar front and rear, front with pyramids, rear with grating. We specify the method to use to calculate the redistribution matrices in each case and create the bulk layer.\n\nsurf = regular_pyramids(elevation_angle=55, upright=False)\n\nfront_surf_pyramids = Interface(\n \"RT_Fresnel\",\n texture=surf,\n layers=[],\n name=\"inv_pyramids_front_\" + str(options[\"n_rays\"]),\n)\n\nfront_surf_planar = Interface(\"TMM\", layers=[], name=\"planar_front\")\n\nback_surf_grating = Interface(\n \"RCWA\",\n layers=back_materials,\n name=\"crossed_grating_back\",\n d_vectors=d_vectors,\n rcwa_orders=20,\n)\n\nback_surf_planar = Interface(\"TMM\", layers=[], name=\"planar_back\")\n\nbulk_Si = BulkLayer(201.8e-6, Si, name=\"Si_bulk\")\n\nfixed h 0.7140740033710572\n\n\nNow we create the different structures and ‘process’ them (this will calculate the relevant matrices if necessary, or do nothing if it finds the matrices have previously been calculated and the files already exist). We don’t need to process the final structure because it will use matrices calculated for SC_fig6 and SC_fig7.\n\nSC_fig6 = Structure(\n [front_surf_planar, bulk_Si, back_surf_grating], incidence=Air, transmission=Air\n)\nSC_fig7 = Structure(\n [front_surf_pyramids, bulk_Si, back_surf_planar], incidence=Air, transmission=Air\n)\nSC_fig8 = Structure(\n [front_surf_pyramids, bulk_Si, back_surf_grating], incidence=Air, transmission=Air\n)\n\nprocess_structure(SC_fig6, options, save_location='current')\nprocess_structure(SC_fig7, options, save_location='current')\n\nMaking matrix for planar surface using TMM for element 0 in structure\nRCWA calculation for element 2 in structure\nRay tracing with Fresnel equations for element 0 in structure\nMaking matrix for planar surface using TMM for element 2 in structure\n\n\n<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast" }, { "objectID": "solcore-workshop/notebooks/8-grating_pyramids_OPTOS.html#calculating-rat", "href": "solcore-workshop/notebooks/8-grating_pyramids_OPTOS.html#calculating-rat", "title": "Section 8: Textured Si", "section": "Calculating R/A/T", - "text": "Calculating R/A/T\nThen we ask RayFlare to calculate the reflection, transmission and absorption through matrix multiplication, and get the required result out (absorption in the bulk) for each cell. We also load the results from the reference paper to compare them to the ones calculated with RayFlare.\n\nresults_fig6 = calculate_RAT(SC_fig6, options)\nresults_fig7 = calculate_RAT(SC_fig7, options)\nresults_fig8 = calculate_RAT(SC_fig8, options)\n\nRAT_fig6 = results_fig6[0]\nRAT_fig7 = results_fig7[0]\nRAT_fig8 = results_fig8[0]\n\nsim_fig6 = np.loadtxt(\"data/optos_fig6_sim.csv\", delimiter=\",\")\nsim_fig7 = np.loadtxt(\"data/optos_fig7_sim.csv\", delimiter=\",\")\nsim_fig8 = np.loadtxt(\"data/optos_fig8_sim.csv\", delimiter=\",\")\n\nFinally, we use TMM to calculate the absorption in a structure with a planar front and planar rear, as a reference.\n\nstruc = tmm_structure([Layer(si(\"200um\"), Si)], incidence=Air, transmission=Air)\noptions.coherent = False\noptions.coherency_list = [\"i\"]\nRAT = tmm_structure.calculate(struc, options)" + "text": "Calculating R/A/T\nThen we ask RayFlare to calculate the reflection, transmission and absorption through matrix multiplication, and get the required result out (absorption in the bulk) for each cell. We also load the results from the reference paper to compare them to the ones calculated with RayFlare.\n\nresults_fig6 = calculate_RAT(SC_fig6, options, save_location='current')\nresults_fig7 = calculate_RAT(SC_fig7, options, save_location='current')\nresults_fig8 = calculate_RAT(SC_fig8, options, save_location='current')\n\nRAT_fig6 = results_fig6[0]\nRAT_fig7 = results_fig7[0]\nRAT_fig8 = results_fig8[0]\n\nsim_fig6 = np.loadtxt(\"data/optos_fig6_sim.csv\", delimiter=\",\")\nsim_fig7 = np.loadtxt(\"data/optos_fig7_sim.csv\", delimiter=\",\")\nsim_fig8 = np.loadtxt(\"data/optos_fig8_sim.csv\", delimiter=\",\")\n\nFinally, we use TMM to calculate the absorption in a structure with a planar front and planar rear, as a reference.\n\nstruc = tmm_structure([Layer(si(\"200um\"), Si)], incidence=Air, transmission=Air)\noptions.coherent = False\noptions.coherency_list = [\"i\"]\nRAT = tmm_structure.calculate(struc, options)" }, { "objectID": "solcore-workshop/notebooks/8-grating_pyramids_OPTOS.html#plotting", @@ -116,7 +116,7 @@ "href": "solcore-workshop/notebooks/7-InGaP_Si_planar.html#defining-materials-layers-and-junctions", "title": "Section 7: Planar GaInP//Si tandem cell", "section": "Defining materials, layers and junctions", - "text": "Defining materials, layers and junctions\nThe paper referenced above uses a double-layer anti-reflection coating (ARC) made of MgF\\(_2\\) and ZnS. As in the previous example, we use the interface to the refractiveindex.info database to select optical constant data from specific sources, and define Solcore materials using this data. The III-V materials are taken from Solcore’s own material database.\nNote that for the epoxy/glass layer, we use only a single material (BK7 glass). The epoxy and glass used in the paper have the same refractive index (n = 1.56), so we can use a single material with an appropriate refractive index to represent them.\n\n# download_db() # uncomment to download database\n\nMgF2_pageid = search_db(os.path.join(\"MgF2\", \"Rodriguez-de Marcos\"))[0][0];\nZnS_pageid = search_db(os.path.join(\"ZnS\", \"Querry\"))[0][0];\nMgF2 = material(str(MgF2_pageid), nk_db=True)();\nZnS = material(str(ZnS_pageid), nk_db=True)();\n\nwindow = material(\"AlInP\")(Al=0.52)\nGaInP = material(\"GaInP\")\nBSF = material(\"AlGaAs\")(Al=0.5)\n\nepoxy = material(\"BK7\")()\n\nFor the Si cell, the front surface has both a low-index and high-index SiN\\(x\\) layer. The rear surface uses Al\\(2\\)O\\(3\\), and the cell has Al at the rear surface.\n\nSiOx = material(\"SiO\")()\nSiN_191_pageid = search_db(\"Vogt-1.91\")[0][0];\nSiN_213_pageid = search_db(\"Vogt-2.13\")[0][0];\nSiN_191 = material(str(SiN_191_pageid), nk_db=True)();\nSiN_213 = material(str(SiN_213_pageid), nk_db=True)();\n\nSi = material(\"Si\")\n\nAl2O3 = material(\"Al2O3P\")()\nAl = material(\"Al\")()\n\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\n1 results found.\npageid shelf book page filepath hasrefractive hasextinction rangeMin rangeMax points\n2816 other SiN Vogt-1.91 anti-reflective coatings/SiN/Vogt-1.91.yml 1 1 0.25 1.7 146\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\n1 results found.\npageid shelf book page filepath hasrefractive hasextinction rangeMin rangeMax points\n2818 other SiN Vogt-2.13 anti-reflective coatings/SiN/Vogt-2.13.yml 1 1 0.25 1.7 146\n\n\nWe now define the layers used in the top cell stack: the ARC and window layer for the top cell, and the GaInP junction itself. The ARC and window layers are not involved in the electrical calculation using the depletion approximation, so they are defined as simple layers, while the GaInP emitter and base are defined as part of a Junction object.\n\nARC_window = [\n Layer(97e-9, MgF2),\n Layer(41e-9, ZnS),\n Layer(17e-9, window, role=\"window\"),\n]\n\nGaInP_junction = Junction([\n Layer(200e-9, GaInP(In=0.50, Nd=si(\"2e18cm-3\"), hole_diffusion_length=si(\"300nm\")),\n role=\"emitter\"),\n Layer(750e-9, GaInP(In=0.50, Na=si(\"1e17cm-3\"), electron_diffusion_length=si(\n \"800nm\")),\n role=\"base\"),\n Layer(500e-9, BSF, role=\"bsf\")], kind=\"DA\", sn=1, sp=1\n)\n\nWe now define the spacer layer, with and without a ZnS anti-reflection coating, so we can compare their performance in the cell stack. Note that we set the epoxy thickness here to be 10 microns, although the real thickness is much higher - this is because the epoxy/glass is not absorbing at the wavelengths which are able to reach it (which are not absorbed in the GaInP top cell), and we will treat it incoherently (no thin-film interference), so the exact thickness does not matter.\n\nspacer = [\n Layer(82e-9, ZnS),\n Layer(10e-6, epoxy), # real thickness is much higher, but since this layer is\n # non-absorbing at the relevant wavelength (> 650 nm) and treated incoherently,\n # this does not matter\n]\n\nspacer_noARC = [\n Layer(10e-6, epoxy),\n]\n\nNow we define the layer stacks for the Si cell, including the front SiO\\(_x\\)/SiN\\(_x\\) stack, the junction itself, and the back dielectric layers.\n\nSi_front_surf = [\n Layer(100e-9, SiOx),\n Layer(70e-9, SiN_191),\n Layer(15e-9, SiN_213),\n ]\n\nSi_junction = Junction([\n Layer(1e-6, Si(Nd=si(\"2e18cm-3\"), hole_diffusion_length=2e-6), role=\"emitter\"),\n Layer(150e-6, Si(Na=si(\"2e15cm-3\"), electron_diffusion_length=150e-6), role=\"base\"),\n], kind=\"DA\", sn=0.1, sp=0.1)\n\nSi_back_surf = [\n Layer(15e-9, Al2O3),\n Layer(120e-9, SiN_191)\n]" + "text": "Defining materials, layers and junctions\nThe paper referenced above uses a double-layer anti-reflection coating (ARC) made of MgF\\(_2\\) and ZnS. As in the previous example, we use the interface to the refractiveindex.info database to select optical constant data from specific sources, and define Solcore materials using this data. The III-V materials are taken from Solcore’s own material database.\nNote that for the epoxy/glass layer, we use only a single material (BK7 glass). The epoxy and glass used in the paper have the same refractive index (n = 1.56), so we can use a single material with an appropriate refractive index to represent them.\n\ndownload_db() # uncomment to download database\n\nMgF2_pageid = search_db(os.path.join(\"MgF2\", \"Rodriguez-de Marcos\"))[0][0];\nZnS_pageid = search_db(os.path.join(\"ZnS\", \"Querry\"))[0][0];\nMgF2 = material(str(MgF2_pageid), nk_db=True)();\nZnS = material(str(ZnS_pageid), nk_db=True)();\n\nwindow = material(\"AlInP\")(Al=0.52)\nGaInP = material(\"GaInP\")\nBSF = material(\"AlGaAs\")(Al=0.5)\n\nepoxy = material(\"BK7\")()\n\nFor the Si cell, the front surface has both a low-index and high-index SiN\\(x\\) layer. The rear surface uses Al\\(2\\)O\\(3\\), and the cell has Al at the rear surface.\n\nSiOx = material(\"SiO\")()\nSiN_191_pageid = search_db(\"Vogt-1.91\")[0][0];\nSiN_213_pageid = search_db(\"Vogt-2.13\")[0][0];\nSiN_191 = material(str(SiN_191_pageid), nk_db=True)();\nSiN_213 = material(str(SiN_213_pageid), nk_db=True)();\n\nSi = material(\"Si\")\n\nAl2O3 = material(\"Al2O3P\")()\nAl = material(\"Al\")()\n\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\n1 results found.\npageid shelf book page filepath hasrefractive hasextinction rangeMin rangeMax points\n2816 other SiN Vogt-1.91 anti-reflective coatings/SiN/Vogt-1.91.yml 1 1 0.25 1.7 146\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\n1 results found.\npageid shelf book page filepath hasrefractive hasextinction rangeMin rangeMax points\n2818 other SiN Vogt-2.13 anti-reflective coatings/SiN/Vogt-2.13.yml 1 1 0.25 1.7 146\n\n\nWe now define the layers used in the top cell stack: the ARC and window layer for the top cell, and the GaInP junction itself. The ARC and window layers are not involved in the electrical calculation using the depletion approximation, so they are defined as simple layers, while the GaInP emitter and base are defined as part of a Junction object.\n\nARC_window = [\n Layer(97e-9, MgF2),\n Layer(41e-9, ZnS),\n Layer(17e-9, window, role=\"window\"),\n]\n\nGaInP_junction = Junction([\n Layer(200e-9, GaInP(In=0.50, Nd=si(\"2e18cm-3\"), hole_diffusion_length=si(\"300nm\")),\n role=\"emitter\"),\n Layer(750e-9, GaInP(In=0.50, Na=si(\"1e17cm-3\"), electron_diffusion_length=si(\n \"800nm\")),\n role=\"base\"),\n Layer(500e-9, BSF, role=\"bsf\")], kind=\"DA\", sn=1, sp=1\n)\n\nWe now define the spacer layer, with and without a ZnS anti-reflection coating, so we can compare their performance in the cell stack. Note that we set the epoxy thickness here to be 10 microns, although the real thickness is much higher - this is because the epoxy/glass is not absorbing at the wavelengths which are able to reach it (which are not absorbed in the GaInP top cell), and we will treat it incoherently (no thin-film interference), so the exact thickness does not matter.\n\nspacer = [\n Layer(82e-9, ZnS),\n Layer(10e-6, epoxy), # real thickness is much higher, but since this layer is\n # non-absorbing at the relevant wavelength (> 650 nm) and treated incoherently,\n # this does not matter\n]\n\nspacer_noARC = [\n Layer(10e-6, epoxy),\n]\n\nNow we define the layer stacks for the Si cell, including the front SiO\\(_x\\)/SiN\\(_x\\) stack, the junction itself, and the back dielectric layers.\n\nSi_front_surf = [\n Layer(100e-9, SiOx),\n Layer(70e-9, SiN_191),\n Layer(15e-9, SiN_213),\n ]\n\nSi_junction = Junction([\n Layer(1e-6, Si(Nd=si(\"2e18cm-3\"), hole_diffusion_length=2e-6), role=\"emitter\"),\n Layer(150e-6, Si(Na=si(\"2e15cm-3\"), electron_diffusion_length=150e-6), role=\"base\"),\n], kind=\"DA\", sn=0.1, sp=0.1)\n\nSi_back_surf = [\n Layer(15e-9, Al2O3),\n Layer(120e-9, SiN_191)\n]" }, { "objectID": "solcore-workshop/notebooks/7-InGaP_Si_planar.html#comparing-the-optical-performance-with-and-without-intermediate-arc", @@ -172,14 +172,14 @@ "href": "solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating.html#planar-cell", "title": "Section 9a: Planar III-V on planar Si, with rear grating", "section": "Planar cell", - "text": "Planar cell\nNow we define the planar cell, and options for the solver:\n\ncell_planar = tmm_structure(\n ARC + GaInP_junction + tunnel_1 + GaAs_junction + tunnel_2 + Si_junction,\n incidence=Air,\n transmission=Ag,\n)\n\nn_layers = cell_planar.layer_stack.num_layers\n\ncoherency_list = [\"c\"]*coh_layers + [\"i\"]*(n_layers-coh_layers)\n\noptions = default_options()\n\nwl = np.arange(300, 1201, 10) * 1e-9\nAM15G = LightSource(source_type=\"standard\", version=\"AM1.5g\", x=wl,\n output_units=\"photon_flux_per_m\")\n\noptions.wavelengths = wl\noptions.coherency_list = coherency_list\noptions.coherent = False\noptions.project_name = \"III_V_Si_cell\"\n\nRun the TMM calculation for the planar cell, and then extract the relevant layer absorptions. These are used to calculate limiting currents (100% internal quantum efficiency), which are displayed on the plot with the absorption in each layer.\n\ntmm_result = cell_planar.calculate(options=options)\n\nGaInP_A = tmm_result['A_per_layer'][:,3]\nGaAs_A = tmm_result['A_per_layer'][:,8]\nSi_A = tmm_result['A_per_layer'][:,coh_layers]\n\nJmax_GaInP = q*np.trapz(GaInP_A*AM15G.spectrum()[1], x=wl)/10\nJmax_GaAs = q*np.trapz(GaAs_A*AM15G.spectrum()[1], x=wl)/10\nJmax_Si = q*np.trapz(Si_A*AM15G.spectrum()[1], x=wl)/10\n\nR_spacer_ARC = tmm_result['R']\n\nplt.figure(figsize=(6,4))\nplt.plot(wl * 1e9, GaInP_A, \"-k\", label=\"GaInP\")\nplt.plot(wl * 1e9, GaAs_A, \"-b\", label=\"GaAs\")\nplt.plot(wl * 1e9, Si_A, \"-r\", label=\"Si\")\nplt.plot(wl * 1e9, 1 - R_spacer_ARC, '-y', label=\"1 - R\")\n\nplt.text(450, 0.55, r\"{:.1f} mA/cm$^2$\".format(Jmax_GaInP))\nplt.text(670, 0.55, r\"{:.1f} mA/cm$^2$\".format(Jmax_GaAs))\nplt.text(860, 0.55, r\"{:.1f} mA/cm$^2$\".format(Jmax_Si))\nplt.xlabel(\"Wavelength (nm)\")\nplt.ylabel(\"Absorptance\")\nplt.tight_layout()\nplt.legend(loc='upper right')\nplt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\nplt.show()\n\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\nMaterial main/Ag/Jiang.yml loaded.\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\nMaterial main/Ag/Jiang.yml loaded.\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\nMaterial main/MgF2/Rodriguez-de Marcos.yml loaded.\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\nMaterial main/MgF2/Rodriguez-de Marcos.yml loaded.\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\nMaterial main/Ta2O5/Rodriguez-de Marcos.yml loaded.\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\nMaterial main/Ta2O5/Rodriguez-de Marcos.yml loaded." + "text": "Planar cell\nNow we define the planar cell, and options for the solver:\n\ncell_planar = tmm_structure(\n ARC + GaInP_junction + tunnel_1 + GaAs_junction + tunnel_2 + Si_junction,\n incidence=Air,\n transmission=Ag,\n)\n\nn_layers = cell_planar.layer_stack.num_layers\n\ncoherency_list = [\"c\"]*coh_layers + [\"i\"]*(n_layers-coh_layers)\n\noptions = default_options()\n\nwl = np.arange(300, 1201, 10) * 1e-9\nAM15G = LightSource(source_type=\"standard\", version=\"AM1.5g\", x=wl,\n output_units=\"photon_flux_per_m\")\n\noptions.wavelengths = wl\noptions.coherency_list = coherency_list\noptions.coherent = False\n\nRun the TMM calculation for the planar cell, and then extract the relevant layer absorptions. These are used to calculate limiting currents (100% internal quantum efficiency), which are displayed on the plot with the absorption in each layer.\n\ntmm_result = cell_planar.calculate(options=options)\n\nGaInP_A = tmm_result['A_per_layer'][:,3]\nGaAs_A = tmm_result['A_per_layer'][:,8]\nSi_A = tmm_result['A_per_layer'][:,coh_layers]\n\nJmax_GaInP = q*np.trapz(GaInP_A*AM15G.spectrum()[1], x=wl)/10\nJmax_GaAs = q*np.trapz(GaAs_A*AM15G.spectrum()[1], x=wl)/10\nJmax_Si = q*np.trapz(Si_A*AM15G.spectrum()[1], x=wl)/10\n\nR_spacer_ARC = tmm_result['R']\n\nplt.figure(figsize=(6,4))\nplt.plot(wl * 1e9, GaInP_A, \"-k\", label=\"GaInP\")\nplt.plot(wl * 1e9, GaAs_A, \"-b\", label=\"GaAs\")\nplt.plot(wl * 1e9, Si_A, \"-r\", label=\"Si\")\nplt.plot(wl * 1e9, 1 - R_spacer_ARC, '-y', label=\"1 - R\")\n\nplt.text(450, 0.55, r\"{:.1f} mA/cm$^2$\".format(Jmax_GaInP))\nplt.text(670, 0.55, r\"{:.1f} mA/cm$^2$\".format(Jmax_GaAs))\nplt.text(860, 0.55, r\"{:.1f} mA/cm$^2$\".format(Jmax_Si))\nplt.xlabel(\"Wavelength (nm)\")\nplt.ylabel(\"Absorptance\")\nplt.tight_layout()\nplt.legend(loc='upper right')\nplt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\nplt.show()\n\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\nMaterial main/Ag/Jiang.yml loaded.\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\nMaterial main/Ag/Jiang.yml loaded.\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\nMaterial main/MgF2/Rodriguez-de Marcos.yml loaded.\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\nMaterial main/MgF2/Rodriguez-de Marcos.yml loaded.\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\nMaterial main/Ta2O5/Rodriguez-de Marcos.yml loaded.\nDatabase file found at /Users/phoebe/.solcore/nk/nk.db\nMaterial main/Ta2O5/Rodriguez-de Marcos.yml loaded." }, { "objectID": "solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating.html#cell-with-rear-grating", "href": "solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating.html#cell-with-rear-grating", "title": "Section 9a: Planar III-V on planar Si, with rear grating", "section": "Cell with rear grating", - "text": "Cell with rear grating\nNow, for the cell with a grating on the rear, we have a multi-scale problem where we must combine the calculation of absorption in a very thick (compared to the wavelengths of light) layer of Si with the effect of a wavelength-scale (1000 nm pitch) diffraction grating. For this, we will use the Angular Redistribution Matrix Method (ARMM) which was also used in Example 8.\nThe front surface of the cell (i.e. all the layers on top of Si) are planar, and can be treated using TMM. The rear surface of the cell, which has a crossed grating consisting of silver and SU8, must be treated with RCWA to account for diffraction. The thick Si layer will be the bulk coupling layer between these two interfaces.\nFirst, we set up the rear grating surface; we must define its lattice vectors, and place the Ag rectangle in the unit cell of the grating. More details on how unit cells of different shapes can be defined for the RCWA solver can be found here.\n\nx = 1000\n\nd_vectors = ((x, 0), (0, x))\narea_fill_factor = 0.4\nhw = np.sqrt(area_fill_factor) * 500\n\nback_materials = [Layer(width=si(\"250nm\"),\n material=SU8,\n geometry=[{\"type\": \"rectangle\", \"mat\": Ag, \"center\": (x / 2, x / 2),\n \"halfwidths\": (hw, hw), \"angle\": 0}],\n )]\n\nNow, we define the Si bulk layer, and the III-V layers which go in the front interface. Finally, we put everything together into the ARMM Structure, also giving the incidence and transmission materials.\n\nbulk_Si = BulkLayer(280e-6, Si(), name=\"Si_bulk\")\n\nIII_V_layers = ARC + GaInP_junction + tunnel_1 + GaAs_junction + tunnel_2\n\nfront_surf_planar = Interface(\"TMM\", layers=III_V_layers, name=\"III_V_front\",\n coherent=True)\n\nback_surf_grating = Interface(\n \"RCWA\",\n layers=back_materials,\n name=\"crossed_grating_back\",\n d_vectors=d_vectors,\n rcwa_orders=30,\n)\n\ncell_grating = Structure(\n [front_surf_planar, bulk_Si, back_surf_grating],\n incidence=Air,\n transmission=Ag,\n)\n\nBecause RCWA calculations are very slow compared to TMM, it makes sense to only carry out the RCWA calculation at wavelengths where the grating has any effect. Depending on the wavelength, all the incident light may be absorbed in the III-V layers or in its first pass through the Si, so it never reaches the grating. We check this by seeing which wavelengths have even a small amount of transmission into the silver back mirror, and only doing the new calculation at these wavelengths. At shorter wavelengths, the results previously calculated using TMM can be used.\n\nwl_rcwa = wl[tmm_result['T'] > 1e-4] # check where transmission fraction is bigger\n# than 1E-4\n\noptions.wavelengths = wl_rcwa\n\nprocess_structure(cell_grating, options, overwrite=True)\nresults_armm = calculate_RAT(cell_grating, options)\nRAT = results_armm[0]" + "text": "Cell with rear grating\nNow, for the cell with a grating on the rear, we have a multi-scale problem where we must combine the calculation of absorption in a very thick (compared to the wavelengths of light) layer of Si with the effect of a wavelength-scale (1000 nm pitch) diffraction grating. For this, we will use the Angular Redistribution Matrix Method (ARMM) which was also used in Example 8.\nThe front surface of the cell (i.e. all the layers on top of Si) are planar, and can be treated using TMM. The rear surface of the cell, which has a crossed grating consisting of silver and SU8, must be treated with RCWA to account for diffraction. The thick Si layer will be the bulk coupling layer between these two interfaces.\nFirst, we set up the rear grating surface; we must define its lattice vectors, and place the Ag rectangle in the unit cell of the grating. More details on how unit cells of different shapes can be defined for the RCWA solver can be found here.\n\nx = 1000\n\nd_vectors = ((x, 0), (0, x))\narea_fill_factor = 0.4\nhw = np.sqrt(area_fill_factor) * 500\n\nback_materials = [Layer(width=si(\"250nm\"),\n material=SU8,\n geometry=[{\"type\": \"rectangle\", \"mat\": Ag, \"center\": (x / 2, x / 2),\n \"halfwidths\": (hw, hw), \"angle\": 0}],\n )]\n\nNow, we define the Si bulk layer, and the III-V layers which go in the front interface. Finally, we put everything together into the ARMM Structure, also giving the incidence and transmission materials.\n\nbulk_Si = BulkLayer(280e-6, Si(), name=\"Si_bulk\")\n\nIII_V_layers = ARC + GaInP_junction + tunnel_1 + GaAs_junction + tunnel_2\n\nfront_surf_planar = Interface(\"TMM\", layers=III_V_layers, name=\"III_V_front\",\n coherent=True)\n\nback_surf_grating = Interface(\n \"RCWA\",\n layers=back_materials,\n name=\"crossed_grating_back\",\n d_vectors=d_vectors,\n rcwa_orders=60,\n)\n\ncell_grating = Structure(\n [front_surf_planar, bulk_Si, back_surf_grating],\n incidence=Air,\n transmission=Ag,\n)\n\nBecause RCWA calculations are very slow compared to TMM, it makes sense to only carry out the RCWA calculation at wavelengths where the grating has any effect. Depending on the wavelength, all the incident light may be absorbed in the III-V layers or in its first pass through the Si, so it never reaches the grating. We check this by seeing which wavelengths have even a small amount of transmission into the silver back mirror, and only doing the new calculation at these wavelengths. At shorter wavelengths, the results previously calculated using TMM can be used.\n\nwl_rcwa = wl[tmm_result['T'] > 1e-4] # check where transmission fraction is bigger\n# than 1E-4\n\noptions.wavelengths = wl_rcwa\noptions.project_name = \"III_V_Si_cell\"\noptions.n_theta_bins = 50\noptions.c_azimuth = 0.25\n\nprocess_structure(cell_grating, options, save_location='current')\nresults_armm = calculate_RAT(cell_grating, options)\nRAT = results_armm[0]" }, { "objectID": "solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating.html#comparison-of-planar-and-grating-cell", @@ -193,7 +193,7 @@ "href": "solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating.html#questions", "title": "Section 9a: Planar III-V on planar Si, with rear grating", "section": "Questions", - "text": "Questions\n\nWhy does the grating only affect the absorption in Si at long wavelengths?" + "text": "Questions\n\nWhy does the grating only affect the absorption in Si at long wavelengths?\nWhat is the reason for using the angular redistribution matrix method, rather than defining an RCWA-only structure (rcwa_structure)?" }, { "objectID": "solcore-workshop/workshop2023.html", @@ -711,7 +711,7 @@ "href": "solcore-workshop/notebooks/2-Efficiency_limits.html#limits-to-the-short-circuit-current", "title": "Section 2: Integration for limiting current, limiting voltage model, efficiency limit", "section": "Limits to the short-circuit current", - "text": "Limits to the short-circuit current\n\nSolar Spectrum\nThe solar spectrum defines the ultimate current that a solar cell can produce. First we will plot the AM1.5G solar spectrum \\(b(\\lambda)\\) as a spectral irradiance, meaning that the y-axis has units of \\(W.m^{-2}.nm^{-1}\\)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom solcore.light_source import LightSource\nimport seaborn as sns\n\n# Setup the AM1.5G solar spectrum\nwl = np.linspace(300, 4000, 4000) * 1e-9 # wl contains the x-coordinate in wavelength\nam15g = LightSource(source_type='standard', x=wl*1e9, version='AM1.5g',\n outputs=\"power_density_per_nm\")\n\nplt.figure()\nplt.title('Spectral Irradiance')\nplt.plot(*am15g.spectrum(wl*1e9), label='AM1.5G')\nplt.xlim(300, 3000)\nplt.xlabel('Wavelength (nm)')\nplt.ylabel('Power density (Wm$^{-2}$nm$^{-1}$)')\nplt.legend()\n\n<matplotlib.legend.Legend at 0x2be9e7410>\n\n\n\n\n\nLet us now integrate the solar spectrum to provide the total irradiance in units of [\\(W.m^{-2}\\)]. The code below performs the following operation \\(b=\\int^{\\infty}_{0} b(\\lambda) d\\lambda\\)\n\n# Since .spectrum function returns a tuple (x,y) but np.trapz requires data in format (y,x) these are extracted into separate 1D np arrays.\nyval = am15g.spectrum()[1]\nxval = am15g.spectrum()[0]\nintegrated_value = np.trapz(yval,xval) # Perform integration using trapezium rule\nb = integrated_value # Save the integrated power density for the sun for later.\nprint('b = ', integrated_value)\n\nb = 1000.3974821197136\n\n\nLet’s take the opportunity to learn how to format numbers nicely in Python. Here we use the command “%.0f” % to display the value to zero decimal places.\n\nprint('b = ',\"%.0f\" % integrated_value,\"W.m-2\")\n\nb = 1000 W.m-2\n\n\nSolcore performs this integration for us internally. Let’s try the same exercise but for the extraterrestrial solar spectrum, AM0\n\nam0 = LightSource(source_type='standard', x=wl*1e9, version='AM0')\nprint(\"AM0 integrates to\", \"%.0f\" % am0.power_density, \"W.m-2\")\n\nAM0 integrates to 1348 W.m-2\n\n\n\n\nSpectral Photon Flux\nTo calculate a short-circuit current it is convenient to change the units. Two changes are necessary : 1. Since we specify band-gap energies in electron volts (eV) we need to transform the x-axis from nm to eV 2. Photocurrent is proportional to the incident photon flux (number of photons per second) not the irradiance (watts) so we need to convert the y-axis from energy to photon number.\nNote: The conversion is performed internally within the software but be aware that because the transformation from wavelength is non-linear, changing the x-axis from nm to eV also changes the y-values of the data. This is known as a Jacobian transformation and discussed in more detail in an article “Getting the basics right: Jacobian Conversion of Wavelength and Energy Scales for Quantatitive Analysis of Emission Spectra”, Journal of Physical Chemistry, 4(19) 3316 (2013)\n\nev = np.linspace(0.02,4,4000)\nflux = LightSource(source_type='standard', version='AM1.5g', x=ev, output_units='photon_flux_per_ev')\n\nplt.figure()\nplt.title('Spectral Photon Flux')\nplt.plot(*flux.spectrum(), label='AM1.5G')\nplt.xlim(0.2, 4)\nplt.xlabel('Photon Energy (eV)')\nplt.ylabel('Photon flux N ($ph.s^{-1}m^{-2}eV^{-1}$)')\nplt.legend()\n\n<matplotlib.legend.Legend at 0x2be967410>\n\n\n\n\n\n\n\nCalculating the short-circuit current\nIntegrating the photon flux can provide an upper limit to the short-circuit current [A.m-2]. We can integrate the spectrum over the entire spectral range using \\(J_{sc}=q\\int_{0}^{\\infty}N(E)dE\\)\n\nq = 1.60217662E-19\nyval = flux.spectrum()[1]\nxval = flux.spectrum()[0]\nyint = q*np.trapz(yval,xval) # Perform integration using trapezium rule\n\nprint(\"%.0f\" % yint)\n\n690\n\n\nA more useful calculation is to calculate the current that a solar cell would produce with a particular band-gap energy. To do this requires a bit more coding, since we now wish to integrate between limits: \\(J_{sc}=q\\int_{Eg}^{\\infty}N(E)dE\\)\nLet’s do this for a band-gap of 1.42 eV:\n\nq = 1.60217662E-19\neg = 1.42\nyval = flux.spectrum()[1]\nxval = flux.spectrum()[0]\n\nyval[xval < eg] = 0 # set photon flux to zero for photon energies below the band-gap\n\nyint = q*np.trapz(yval,xval) # Perform integration using trapezium rule\n\nprint(\"%.0f\" % yint)\n\n321\n\n\nLet’s reproduce the \\(J_{sc}\\) vs \\(E_g\\) graph that is shown on p. 87 of Martin Green’s Solar Cells book:\n\nq = 1.60217662E-19\n\ndef getJsc(eg):\n yval = flux.spectrum()[1] # Start with the solar spectrum in yval & xval\n xval = flux.spectrum()[0]\n yval[xval < eg] = 0 # set photon flux to zero for photon energies below the band-gap\n return q*np.trapz(yval,xval) # return the integrated value\n\neg = np.linspace(0.5,2.5,100)\njsc = np.vectorize(getJsc)(eg)\n\nplt.figure()\nplt.title('Limit to the short-circuit current $J_{sc}$')\nplt.plot(eg, jsc/10, label='AM1.5G') # Divide by 10 to convert from A.m^-2 to mA.cm^-2\nplt.xlim(0.5, 2.5)\nplt.xlabel('Band Gap energy (eV)')\nplt.ylabel('$J_{sc}$ ($mA.cm^{-2}$)')\nplt.legend()\n\n<matplotlib.legend.Legend at 0x2c8071d10>\n\n\n\n\n\n\n\nCalculating the Trivich-Flinn Efficiency limit\nNow that the limit to \\(J_{sc}\\) is known, we can estimate the power of delivered by the solar cell by evaluating \\(\\frac{Eg J_{sc}}{b}\\)\n\nplt.figure()\nplt.title('Trivich-Flinn Single Junction Efficiency Limit')\nplt.plot(eg, 100*eg*jsc/b,label='AM1.5G') # Divide by 10 to convert from A.m^-2 to mA.cm^-2\nplt.xlim(0.5, 2.5)\nplt.xlabel('Band Gap energy (eV)')\nplt.ylabel('Efficiency (%)')\nplt.legend()\n\n<matplotlib.legend.Legend at 0x2c80e0650>" + "text": "Limits to the short-circuit current\n\nSolar Spectrum\nThe solar spectrum defines the ultimate current that a solar cell can produce. First we will plot the AM1.5G solar spectrum \\(b(\\lambda)\\) as a spectral irradiance, meaning that the y-axis has units of \\(W.m^{-2}.nm^{-1}\\)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom solcore.light_source import LightSource\nimport seaborn as sns\n\n# Setup the AM1.5G solar spectrum\nwl = np.linspace(300, 4000, 4000) * 1e-9 # wl contains the x-coordinate in wavelength\nam15g = LightSource(source_type='standard', x=wl*1e9, version='AM1.5g',\n output_units=\"power_density_per_nm\")\n\nplt.figure()\nplt.title('Spectral Irradiance')\nplt.plot(*am15g.spectrum(wl*1e9), label='AM1.5G')\nplt.xlim(300, 3000)\nplt.xlabel('Wavelength (nm)')\nplt.ylabel('Power density (Wm$^{-2}$nm$^{-1}$)')\nplt.legend()\n\n<matplotlib.legend.Legend at 0x2be9e7410>\n\n\n\n\n\nLet us now integrate the solar spectrum to provide the total irradiance in units of [\\(W.m^{-2}\\)]. The code below performs the following operation \\(b=\\int^{\\infty}_{0} b(\\lambda) d\\lambda\\)\n\n# Since .spectrum function returns a tuple (x,y) but np.trapz requires data in format (y,x) these are extracted into separate 1D np arrays.\nyval = am15g.spectrum()[1]\nxval = am15g.spectrum()[0]\nintegrated_value = np.trapz(yval,xval) # Perform integration using trapezium rule\nb = integrated_value # Save the integrated power density for the sun for later.\nprint('b = ', integrated_value)\n\nb = 1000.3974821197136\n\n\nLet’s take the opportunity to learn how to format numbers nicely in Python. Here we use the command “%.0f” % to display the value to zero decimal places.\n\nprint('b = ',\"%.0f\" % integrated_value,\"W.m-2\")\n\nb = 1000 W.m-2\n\n\nSolcore performs this integration for us internally. Let’s try the same exercise but for the extraterrestrial solar spectrum, AM0\n\nam0 = LightSource(source_type='standard', x=wl*1e9, version='AM0')\nprint(\"AM0 integrates to\", \"%.0f\" % am0.power_density, \"W.m-2\")\n\nAM0 integrates to 1348 W.m-2\n\n\n\n\nSpectral Photon Flux\nTo calculate a short-circuit current it is convenient to change the units. Two changes are necessary : 1. Since we specify band-gap energies in electron volts (eV) we need to transform the x-axis from nm to eV 2. Photocurrent is proportional to the incident photon flux (number of photons per second) not the irradiance (watts) so we need to convert the y-axis from energy to photon number.\nNote: The conversion is performed internally within the software but be aware that because the transformation from wavelength is non-linear, changing the x-axis from nm to eV also changes the y-values of the data. This is known as a Jacobian transformation and discussed in more detail in an article “Getting the basics right: Jacobian Conversion of Wavelength and Energy Scales for Quantatitive Analysis of Emission Spectra”, Journal of Physical Chemistry, 4(19) 3316 (2013)\n\nev = np.linspace(0.02,4,4000)\nflux = LightSource(source_type='standard', version='AM1.5g', x=ev, output_units='photon_flux_per_ev')\n\nplt.figure()\nplt.title('Spectral Photon Flux')\nplt.plot(*flux.spectrum(), label='AM1.5G')\nplt.xlim(0.2, 4)\nplt.xlabel('Photon Energy (eV)')\nplt.ylabel('Photon flux N ($ph.s^{-1}m^{-2}eV^{-1}$)')\nplt.legend()\n\n<matplotlib.legend.Legend at 0x2be967410>\n\n\n\n\n\n\n\nCalculating the short-circuit current\nIntegrating the photon flux can provide an upper limit to the short-circuit current [A.m-2]. We can integrate the spectrum over the entire spectral range using \\(J_{sc}=q\\int_{0}^{\\infty}N(E)dE\\)\n\nq = 1.60217662E-19\nyval = flux.spectrum()[1]\nxval = flux.spectrum()[0]\nyint = q*np.trapz(yval,xval) # Perform integration using trapezium rule\n\nprint(\"%.0f\" % yint)\n\n690\n\n\nA more useful calculation is to calculate the current that a solar cell would produce with a particular band-gap energy. To do this requires a bit more coding, since we now wish to integrate between limits: \\(J_{sc}=q\\int_{Eg}^{\\infty}N(E)dE\\)\nLet’s do this for a band-gap of 1.42 eV:\n\nq = 1.60217662E-19\neg = 1.42\nyval = flux.spectrum()[1]\nxval = flux.spectrum()[0]\n\nyval[xval < eg] = 0 # set photon flux to zero for photon energies below the band-gap\n\nyint = q*np.trapz(yval,xval) # Perform integration using trapezium rule\n\nprint(\"%.0f\" % yint)\n\n321\n\n\nLet’s reproduce the \\(J_{sc}\\) vs \\(E_g\\) graph that is shown on p. 87 of Martin Green’s Solar Cells book:\n\nq = 1.60217662E-19\n\ndef getJsc(eg):\n yval = flux.spectrum()[1] # Start with the solar spectrum in yval & xval\n xval = flux.spectrum()[0]\n yval[xval < eg] = 0 # set photon flux to zero for photon energies below the band-gap\n return q*np.trapz(yval,xval) # return the integrated value\n\neg = np.linspace(0.5,2.5,100)\njsc = np.vectorize(getJsc)(eg)\n\nplt.figure()\nplt.title('Limit to the short-circuit current $J_{sc}$')\nplt.plot(eg, jsc/10, label='AM1.5G') # Divide by 10 to convert from A.m^-2 to mA.cm^-2\nplt.xlim(0.5, 2.5)\nplt.xlabel('Band Gap energy (eV)')\nplt.ylabel('$J_{sc}$ ($mA.cm^{-2}$)')\nplt.legend()\n\n<matplotlib.legend.Legend at 0x2c8071d10>\n\n\n\n\n\n\n\nCalculating the Trivich-Flinn Efficiency limit\nNow that the limit to \\(J_{sc}\\) is known, we can estimate the power of delivered by the solar cell by evaluating \\(\\frac{Eg J_{sc}}{b}\\)\n\nplt.figure()\nplt.title('Trivich-Flinn Single Junction Efficiency Limit')\nplt.plot(eg, 100*eg*jsc/b,label='AM1.5G')\nplt.xlim(0.5, 2.5)\nplt.xlabel('Band Gap energy (eV)')\nplt.ylabel('Efficiency (%)')\nplt.legend()\n\n<matplotlib.legend.Legend at 0x2c80e0650>" }, { "objectID": "solcore-workshop/notebooks/2-Efficiency_limits.html#the-shockley-queisser-efficiency-limit", diff --git a/docs/solcore-workshop/notebooks/2-Efficiency_limits.html b/docs/solcore-workshop/notebooks/2-Efficiency_limits.html index 3a325d9..74533f0 100644 --- a/docs/solcore-workshop/notebooks/2-Efficiency_limits.html +++ b/docs/solcore-workshop/notebooks/2-Efficiency_limits.html @@ -307,7 +307,7 @@

Solar Spectrum

# Setup the AM1.5G solar spectrum wl = np.linspace(300, 4000, 4000) * 1e-9 # wl contains the x-coordinate in wavelength am15g = LightSource(source_type='standard', x=wl*1e9, version='AM1.5g', - outputs="power_density_per_nm") + output_units="power_density_per_nm") plt.figure() plt.title('Spectral Irradiance') @@ -439,7 +439,7 @@ 

plt.figure()
 plt.title('Trivich-Flinn Single Junction Efficiency Limit')
-plt.plot(eg, 100*eg*jsc/b,label='AM1.5G')  # Divide by 10 to convert from A.m^-2 to mA.cm^-2
+plt.plot(eg, 100*eg*jsc/b,label='AM1.5G')
 plt.xlim(0.5, 2.5)
 plt.xlabel('Band Gap energy (eV)')
 plt.ylabel('Efficiency (%)')
diff --git a/docs/solcore-workshop/notebooks/7-InGaP_Si_planar.html b/docs/solcore-workshop/notebooks/7-InGaP_Si_planar.html
index eaf9c5e..70aafe8 100644
--- a/docs/solcore-workshop/notebooks/7-InGaP_Si_planar.html
+++ b/docs/solcore-workshop/notebooks/7-InGaP_Si_planar.html
@@ -281,7 +281,7 @@ 
De
 
The paper referenced above uses a double-layer anti-reflection coating (ARC) made of MgF\(_2\) and ZnS. As in the previous example, we use the interface to the refractiveindex.info database to select optical constant data from specific sources, and define Solcore materials using this data. The III-V materials are taken from Solcore’s own material database.

 
Note that for the epoxy/glass layer, we use only a single material (BK7 glass). The epoxy and glass used in the paper have the same refractive index (n = 1.56), so we can use a single material with an appropriate refractive index to represent them.

 

-
# download_db() # uncomment to download database
+
download_db() # uncomment to download database
 
 MgF2_pageid = search_db(os.path.join("MgF2", "Rodriguez-de Marcos"))[0][0];
 ZnS_pageid = search_db(os.path.join("ZnS", "Querry"))[0][0];
diff --git a/docs/solcore-workshop/notebooks/8-grating_pyramids_OPTOS.html b/docs/solcore-workshop/notebooks/8-grating_pyramids_OPTOS.html
index db62741..dcf7f28 100644
--- a/docs/solcore-workshop/notebooks/8-grating_pyramids_OPTOS.html
+++ b/docs/solcore-workshop/notebooks/8-grating_pyramids_OPTOS.html
@@ -271,7 +271,7 @@ 
Section 8: Textured Si

 

 
Setting up

 
First, importing relevant packages:

-

+

 
import numpy as np
 import os
 
@@ -295,7 +295,7 @@ 
Setting up

 from sparse import load_npz

 

 
To make sure we are using the same optical constants for Si, load the same Si n/k data used in the paper linked above:

-

+

 
create_new_material("Si_OPTOS", "data/Si_OPTOS_n.txt", "data/Si_OPTOS_k.txt",
                     overwrite=True)

 

@@ -304,10 +304,10 @@ 
Setting up

 

 
You only need to do this one time, then the material will be stored in Solcore’s material database.

 
Setting options (taking the default options for everything not specified explicitly):

-

-
angle_degrees_in = 8
+

+
angle_degrees_in = 8 # same as in Fraunhofer paper
 
-wavelengths = np.linspace(900, 1200, 30) * 1e-9
+wavelengths = np.linspace(900, 1200, 20) * 1e-9
 
 Si = material("Si_OPTOS")()
 Air = material("Air")()
@@ -315,18 +315,19 @@ 
Setting up

 options = default_options()
 options.wavelengths = wavelengths
 options.theta_in = angle_degrees_in * np.pi / 180 # incidence angle (polar angle)
-options.n_theta_bins = 100
+options.n_theta_bins = 50
 options.c_azimuth = 0.25
-options.n_rays = 25 * 25 * 1300 # number of rays per wavelength in ray-tracing
+options.n_rays = 5e5 # number of rays per wavelength in ray-tracing
 options.project_name = "OPTOS_comparison"
 options.orders = 60 # number of RCWA orders to use (more = better convergence, but slower)
-options.pol = "u" # unpolarized light

+options.pol = "u" # unpolarized light
+options.only_incidence_angle = False

 

 

 

 
Defining the structures

 
Now, set up the grating basis vectors for the RCWA calculations and define the grating structure. These are squares, rotated by 45 degrees. The halfwidth is calculated based on the area fill factor of the etched pillars given in the paper.

-

+

 
x = 1000
 
 d_vectors = ((x, 0), (0, x))
@@ -340,7 +341,7 @@ 
Defining the struc
     )]

 

 
Now we define the pyramid texture for the front surface in case (2) and (3) and make the four possible different surfaces: planar front and rear, front with pyramids, rear with grating. We specify the method to use to calculate the redistribution matrices in each case and create the bulk layer.

-

+

 
surf = regular_pyramids(elevation_angle=55, upright=False)
 
 front_surf_pyramids = Interface(
@@ -357,119 +358,128 @@ 
Defining the struc
     layers=back_materials,
     name="crossed_grating_back",
     d_vectors=d_vectors,
-    rcwa_orders=60,
+    rcwa_orders=20,
 )
 
 back_surf_planar = Interface("TMM", layers=[], name="planar_back")
 
 bulk_Si = BulkLayer(201.8e-6, Si, name="Si_bulk")

+

+
fixed h 0.7140740033710572

+

 

 
Now we create the different structures and ‘process’ them (this will calculate the relevant matrices if necessary, or do nothing if it finds the matrices have previously been calculated and the files already exist). We don’t need to process the final structure because it will use matrices calculated for SC_fig6 and SC_fig7.

-

-
SC_fig6 = Structure(
-    [front_surf_planar, bulk_Si, back_surf_grating], incidence=Air, transmission=Air
-)
-SC_fig7 = Structure(
-    [front_surf_pyramids, bulk_Si, back_surf_planar], incidence=Air, transmission=Air
-)
-SC_fig8 = Structure(
-    [front_surf_pyramids, bulk_Si, back_surf_grating], incidence=Air, transmission=Air
-)
-
-process_structure(SC_fig6, options)
-process_structure(SC_fig7, options)

+

+
SC_fig6 = Structure(
+    [front_surf_planar, bulk_Si, back_surf_grating], incidence=Air, transmission=Air
+)
+SC_fig7 = Structure(
+    [front_surf_pyramids, bulk_Si, back_surf_planar], incidence=Air, transmission=Air
+)
+SC_fig8 = Structure(
+    [front_surf_pyramids, bulk_Si, back_surf_grating], incidence=Air, transmission=Air
+)
+
+process_structure(SC_fig6, options, save_location='current')
+process_structure(SC_fig7, options, save_location='current')

 

 
Making matrix for planar surface using TMM for element 0 in structure
-Existing angular redistribution matrices found
-Existing angular redistribution matrices found
 RCWA calculation for element 2 in structure
-Existing angular redistribution matrices found
 Ray tracing with Fresnel equations for element 0 in structure
-Existing angular redistribution matrices found
-Existing angular redistribution matrices found
-Making matrix for planar surface using TMM for element 2 in structure
-Existing angular redistribution matrices found

+Making matrix for planar surface using TMM for element 2 in structure

+

+

+
<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
+<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
+<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
+<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
+<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
+<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
+<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
+<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
+<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast
+<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast

 

 

 
 

 
Calculating R/A/T

 
Then we ask RayFlare to calculate the reflection, transmission and absorption through matrix multiplication, and get the required result out (absorption in the bulk) for each cell. We also load the results from the reference paper to compare them to the ones calculated with RayFlare.

-

-
results_fig6 = calculate_RAT(SC_fig6, options)
-results_fig7 = calculate_RAT(SC_fig7, options)
-results_fig8 = calculate_RAT(SC_fig8, options)
-
-RAT_fig6 = results_fig6[0]
-RAT_fig7 = results_fig7[0]
-RAT_fig8 = results_fig8[0]
-
-sim_fig6 = np.loadtxt("data/optos_fig6_sim.csv", delimiter=",")
-sim_fig7 = np.loadtxt("data/optos_fig7_sim.csv", delimiter=",")
-sim_fig8 = np.loadtxt("data/optos_fig8_sim.csv", delimiter=",")

+

+
results_fig6 = calculate_RAT(SC_fig6, options, save_location='current')
+results_fig7 = calculate_RAT(SC_fig7, options, save_location='current')
+results_fig8 = calculate_RAT(SC_fig8, options, save_location='current')
+
+RAT_fig6 = results_fig6[0]
+RAT_fig7 = results_fig7[0]
+RAT_fig8 = results_fig8[0]
+
+sim_fig6 = np.loadtxt("data/optos_fig6_sim.csv", delimiter=",")
+sim_fig7 = np.loadtxt("data/optos_fig7_sim.csv", delimiter=",")
+sim_fig8 = np.loadtxt("data/optos_fig8_sim.csv", delimiter=",")

 

 
Finally, we use TMM to calculate the absorption in a structure with a planar front and planar rear, as a reference.

-

-
struc = tmm_structure([Layer(si("200um"), Si)], incidence=Air, transmission=Air)
-options.coherent = False
-options.coherency_list = ["i"]
-RAT = tmm_structure.calculate(struc, options)

+

+
struc = tmm_structure([Layer(si("200um"), Si)], incidence=Air, transmission=Air)
+options.coherent = False
+options.coherency_list = ["i"]
+RAT = tmm_structure.calculate(struc, options)

 

 

 

 
Plotting

 
Plot everything together, including data from the reference paper for comparison:

-

-
palhf = sns.color_palette("hls", 4)
-
-fig = plt.figure()
-plt.plot(sim_fig6[:, 0], sim_fig6[:, 1],
-    "--", color=palhf[0], label="OPTOS - rear grating (1)")
-plt.plot(wavelengths * 1e9, RAT_fig6["A_bulk"][0],
-    "-o", color=palhf[0], label="RayFlare - rear grating (1)", fillstyle="none")
-plt.plot(sim_fig7[:, 0], sim_fig7[:, 1],
-    "--", color=palhf[1], label="OPTOS - front pyramids (2)",)
-plt.plot(wavelengths * 1e9, RAT_fig7["A_bulk"][0],
-    "-o", color=palhf[1], label="RayFlare - front pyramids (2)", fillstyle="none")
-plt.plot(sim_fig8[:, 0], sim_fig8[:, 1],
-    "--", color=palhf[2], label="OPTOS - grating + pyramids (3)")
-plt.plot(wavelengths * 1e9, RAT_fig8["A_bulk"][0],
-    "-o", color=palhf[2],label="RayFlare - grating + pyramids (3)", fillstyle="none",)
-plt.plot(wavelengths * 1e9, RAT["A_per_layer"][:, 0], "-k", label="Planar")
-plt.legend(loc="lower left")
-plt.xlabel("Wavelength (nm)")
-plt.ylabel("Absorption in Si")
-plt.xlim([900, 1200])
-plt.ylim([0, 1])
-plt.show()

+

+
palhf = sns.color_palette("hls", 4)
+
+fig = plt.figure()
+plt.plot(sim_fig6[:, 0], sim_fig6[:, 1],
+    "--", color=palhf[0], label="OPTOS - rear grating (1)")
+plt.plot(wavelengths * 1e9, RAT_fig6["A_bulk"][0],
+    "-o", color=palhf[0], label="RayFlare - rear grating (1)", fillstyle="none")
+plt.plot(sim_fig7[:, 0], sim_fig7[:, 1],
+    "--", color=palhf[1], label="OPTOS - front pyramids (2)",)
+plt.plot(wavelengths * 1e9, RAT_fig7["A_bulk"][0],
+    "-o", color=palhf[1], label="RayFlare - front pyramids (2)", fillstyle="none")
+plt.plot(sim_fig8[:, 0], sim_fig8[:, 1],
+    "--", color=palhf[2], label="OPTOS - grating + pyramids (3)")
+plt.plot(wavelengths * 1e9, RAT_fig8["A_bulk"][0],
+    "-o", color=palhf[2],label="RayFlare - grating + pyramids (3)", fillstyle="none",)
+plt.plot(wavelengths * 1e9, RAT["A_per_layer"][:, 0], "-k", label="Planar")
+plt.legend(loc="lower left")
+plt.xlabel("Wavelength (nm)")
+plt.ylabel("Absorption in Si")
+plt.xlim([900, 1200])
+plt.ylim([0, 1])
+plt.show()

 

 

 

 

 
We can see good agreement between the reference values and our calculated values. The structure with rear grating also behaves identically to the planar TMM reference case at the short wavelengths where front surface reflection dominates the result, as expected. Clearly, the pyramids perform much better overall, giving a large boost in the absorption at long wavelengths and also reducing the reflection significantly at shorter wavelengths. Plotting reflection and transmission emphasises this:

-

-
fig = plt.figure()
-plt.plot(wavelengths * 1e9,RAT_fig6["R"][0],
-    "-o", color=palhf[0], label="RayFlare - rear grating (1)", fillstyle="none")
-plt.plot(wavelengths * 1e9, RAT_fig7["R"][0],
-    "-o", color=palhf[1], label="RayFlare - front pyramids (2)", fillstyle="none")
-plt.plot(wavelengths * 1e9, RAT_fig8["R"][0],
-    "-o", color=palhf[2], label="RayFlare - grating + pyramids (3)", fillstyle="none")
-
-plt.plot(wavelengths * 1e9, RAT_fig6["T"][0], "--o", color=palhf[0])
-plt.plot(wavelengths * 1e9, RAT_fig7["T"][0], "--o", color=palhf[1])
-plt.plot(wavelengths * 1e9, RAT_fig8["T"][0], "--o", color=palhf[2])
-
-# these are just to create the legend:
-plt.plot(-1, 0, "k-o", label="R", fillstyle="none")
-plt.plot(-1, 0, "k--o", label="T")
-
-plt.legend()
-plt.xlabel("Wavelength (nm)")
-plt.ylabel("Reflected/transmitted fraction")
-plt.xlim([900, 1200])
-plt.ylim([0, 0.6])
-plt.show()

+

+
fig = plt.figure()
+plt.plot(wavelengths * 1e9,RAT_fig6["R"][0],
+    "-o", color=palhf[0], label="RayFlare - rear grating (1)", fillstyle="none")
+plt.plot(wavelengths * 1e9, RAT_fig7["R"][0],
+    "-o", color=palhf[1], label="RayFlare - front pyramids (2)", fillstyle="none")
+plt.plot(wavelengths * 1e9, RAT_fig8["R"][0],
+    "-o", color=palhf[2], label="RayFlare - grating + pyramids (3)", fillstyle="none")
+
+plt.plot(wavelengths * 1e9, RAT_fig6["T"][0], "--o", color=palhf[0])
+plt.plot(wavelengths * 1e9, RAT_fig7["T"][0], "--o", color=palhf[1])
+plt.plot(wavelengths * 1e9, RAT_fig8["T"][0], "--o", color=palhf[2])
+
+# these are just to create the legend:
+plt.plot(-1, 0, "k-o", label="R", fillstyle="none")
+plt.plot(-1, 0, "k--o", label="T")
+
+plt.legend()
+plt.xlabel("Wavelength (nm)")
+plt.ylabel("Reflected/transmitted fraction")
+plt.xlim([900, 1200])
+plt.ylim([0, 0.6])
+plt.show()

 

 

 

@@ -478,34 +488,34 @@ 
Plotting

 

 
Redistribution matrices

 
Plot the redistribution matrix for the rear grating (summed over azimuthal angles) at 1100 nm:

-

-
theta_intv, phi_intv, angle_vector = make_angle_vector(
-    options["n_theta_bins"], options["phi_symmetry"], options["c_azimuth"])
-
-path = get_savepath("default", options.project_name)
-sprs = load_npz(os.path.join(path, SC_fig6[2].name + "frontRT.npz"))
-
-wl_to_plot = 1100e-9
-wl_index = np.argmin(np.abs(wavelengths - wl_to_plot))
-
-full = sprs[wl_index].todense()
-
-summat = theta_summary(full, angle_vector, options["n_theta_bins"], "front")
-summat_r = summat[: options["n_theta_bins"], :]
-summat_r = summat_r.rename({ r"$\theta_{in}$": r"$\sin(\theta_{in})$",
-        r"$\theta_{out}$": r"$\sin(\theta_{out})$"})
-
-summat_r = summat_r.assign_coords({r"$\sin(\theta_{in})$": np.sin(summat_r.coords[r"$\sin(\theta_{in})$"]).data,
-    r"$\sin(\theta_{out})$": np.sin(summat_r.coords[r"$\sin(\theta_{out})$"]).data})
-
-palhf = sns.cubehelix_palette(256, start=0.5, rot=-0.9)
-palhf.reverse()
-seamap = mpl.colors.ListedColormap(palhf)
-
-fig = plt.figure()
-ax = plt.subplot(111)
-ax = summat_r.plot.imshow(ax=ax, cmap=seamap, vmax=0.3)
-plt.show()

+

+
theta_intv, phi_intv, angle_vector = make_angle_vector(
+    options["n_theta_bins"], options["phi_symmetry"], options["c_azimuth"])
+
+path = get_savepath("default", options.project_name)
+sprs = load_npz(os.path.join(path, SC_fig6[2].name + "frontRT.npz"))
+
+wl_to_plot = 1100e-9
+wl_index = np.argmin(np.abs(wavelengths - wl_to_plot))
+
+full = sprs[wl_index].todense()
+
+summat = theta_summary(full, angle_vector, options["n_theta_bins"], "front")
+summat_r = summat[: options["n_theta_bins"], :]
+summat_r = summat_r.rename({ r"$\theta_{in}$": r"$\sin(\theta_{in})$",
+        r"$\theta_{out}$": r"$\sin(\theta_{out})$"})
+
+summat_r = summat_r.assign_coords({r"$\sin(\theta_{in})$": np.sin(summat_r.coords[r"$\sin(\theta_{in})$"]).data,
+    r"$\sin(\theta_{out})$": np.sin(summat_r.coords[r"$\sin(\theta_{out})$"]).data})
+
+palhf = sns.cubehelix_palette(256, start=0.5, rot=-0.9)
+palhf.reverse()
+seamap = mpl.colors.ListedColormap(palhf)
+
+fig = plt.figure()
+ax = plt.subplot(111)
+ax = summat_r.plot.imshow(ax=ax, cmap=seamap, vmax=0.3)
+plt.show()

 

 

 

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diff --git a/docs/solcore-workshop/notebooks/8-grating_pyramids_OPTOS_files/figure-html/cell-12-output-1.png b/docs/solcore-workshop/notebooks/8-grating_pyramids_OPTOS_files/figure-html/cell-12-output-1.png
index 4761b91..bb67d20 100644
Binary files a/docs/solcore-workshop/notebooks/8-grating_pyramids_OPTOS_files/figure-html/cell-12-output-1.png and b/docs/solcore-workshop/notebooks/8-grating_pyramids_OPTOS_files/figure-html/cell-12-output-1.png differ
diff --git a/docs/solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating.html b/docs/solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating.html
index 2cf828c..5e0b580 100644
--- a/docs/solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating.html
+++ b/docs/solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating.html
@@ -266,7 +266,7 @@ 
Section 9a: Planar III-V on planar Si, with rear grating

 
In this example, we will build two structures similar to those described in this paper. These are both triple-junction, two-terminal GaInP/GaAs/Si cells; one cell is planar, while the other has a diffraction grating deposited on the rear of the bottom Si cell to boost its current.

 

 
Setting up

-

+

 
from solcore import material, si
 from solcore.absorption_calculator import search_db, download_db
 import os
@@ -281,7 +281,7 @@ 
Setting up

 import matplotlib.pyplot as plt

 

 
As before, we load some materials from the refractiveindex.info database. The MgF\(_2\) and Ta\(_2\)O\(_5\) are the same as the ARC example; the SU8 is a negative photoresist which was used in the reference paper The optical constants for silver are also loaded from a reliable literature source. Note that the exact compositions of some semiconductor alloy layers (InGaP, AlInP and AlGaAs) are not given in the paper and are thus reasonable guesses.

-

+

 
# download_db() # only needs to be run once
 
 MgF2_pageid = search_db(os.path.join("MgF2", "Rodriguez-de Marcos"))[0][0];
@@ -308,7 +308,7 @@ 
Setting up

 

 
Defining the cell layers

 
Now we define the layers for the III-V top junctions, and the Si wafer, grouping them together in a logical way. In this example, we will only do optical simulations, so we will not set e.g. diffusion lengths or doping levels.

-

+

 
ARC = [
     Layer(110e-9, MgF2),
     Layer(65e-9, Ta2O5),
@@ -347,7 +347,7 @@ 
Defining the cell
 

 
Planar cell

 
Now we define the planar cell, and options for the solver:

-

+

 
cell_planar = tmm_structure(
     ARC + GaInP_junction + tunnel_1 + GaAs_junction + tunnel_2 + Si_junction,
     incidence=Air,
@@ -366,11 +366,10 @@ 
Planar cell

 
 options.wavelengths = wl
 options.coherency_list = coherency_list
-options.coherent = False
-options.project_name = "III_V_Si_cell"

+options.coherent = False

 

 
Run the TMM calculation for the planar cell, and then extract the relevant layer absorptions. These are used to calculate limiting currents (100% internal quantum efficiency), which are displayed on the plot with the absorption in each layer.

-

+

 
tmm_result = cell_planar.calculate(options=options)
 
 GaInP_A = tmm_result['A_per_layer'][:,3]
@@ -422,7 +421,7 @@ 
Cell with rear grat
 
Now, for the cell with a grating on the rear, we have a multi-scale problem where we must combine the calculation of absorption in a very thick (compared to the wavelengths of light) layer of Si with the effect of a wavelength-scale (1000 nm pitch) diffraction grating. For this, we will use the Angular Redistribution Matrix Method (ARMM) which was also used in Example 8.

 
The front surface of the cell (i.e. all the layers on top of Si) are planar, and can be treated using TMM. The rear surface of the cell, which has a crossed grating consisting of silver and SU8, must be treated with RCWA to account for diffraction. The thick Si layer will be the bulk coupling layer between these two interfaces.

 
First, we set up the rear grating surface; we must define its lattice vectors, and place the Ag rectangle in the unit cell of the grating. More details on how unit cells of different shapes can be defined for the RCWA solver can be found here.

-

+

 
x = 1000
 
 d_vectors = ((x, 0), (0, x))
@@ -436,7 +435,7 @@ 
Cell with rear grat
     )]

 

 
Now, we define the Si bulk layer, and the III-V layers which go in the front interface. Finally, we put everything together into the ARMM Structure, also giving the incidence and transmission materials.

-

+

 
bulk_Si = BulkLayer(280e-6, Si(), name="Si_bulk")
 
 III_V_layers = ARC + GaInP_junction + tunnel_1 + GaAs_junction + tunnel_2
@@ -449,7 +448,7 @@ 
Cell with rear grat
     layers=back_materials,
     name="crossed_grating_back",
     d_vectors=d_vectors,
-    rcwa_orders=30,
+    rcwa_orders=60,
 )
 
 cell_grating = Structure(
@@ -459,21 +458,24 @@ 
Cell with rear grat
 )

 

 
Because RCWA calculations are very slow compared to TMM, it makes sense to only carry out the RCWA calculation at wavelengths where the grating has any effect. Depending on the wavelength, all the incident light may be absorbed in the III-V layers or in its first pass through the Si, so it never reaches the grating. We check this by seeing which wavelengths have even a small amount of transmission into the silver back mirror, and only doing the new calculation at these wavelengths. At shorter wavelengths, the results previously calculated using TMM can be used.

-

+

 
wl_rcwa = wl[tmm_result['T'] > 1e-4] # check where transmission fraction is bigger
 # than 1E-4
 
 options.wavelengths = wl_rcwa
-
-process_structure(cell_grating, options, overwrite=True)
-results_armm = calculate_RAT(cell_grating, options)
-RAT = results_armm[0]

+options.project_name = "III_V_Si_cell"
+options.n_theta_bins = 50
+options.c_azimuth = 0.25
+
+process_structure(cell_grating, options, save_location='current')
+results_armm = calculate_RAT(cell_grating, options)
+RAT = results_armm[0]

 

 

 

 
Comparison of planar and grating cell

 
We extract the relevant absorption per layer, and use it to calculate the new limiting current for the Si junction. The plot compares the absorption in the Si with and without the grating.

-

+

 
Si_A_total = np.zeros(len(wl))
 Si_A_total[tmm_result['T'] > 1e-4] = RAT['A_bulk'][0]
 Si_A_total[tmm_result['T'] <= 1e-4] = Si_A[tmm_result['T'] <= 1e-4]
@@ -494,15 +496,13 @@ 
Comp
 
 plt.tight_layout()
 plt.show()

-

-

-

 

 

 

 
Questions

 
    
 
  • Why does the grating only affect the absorption in Si at long wavelengths?
    • 
+
  • What is the reason for using the angular redistribution matrix method, rather than defining an RCWA-only structure (rcwa_structure)?
    • 
 

 
 
diff --git a/docs/solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating_files/figure-html/cell-10-output-1.png b/docs/solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating_files/figure-html/cell-10-output-1.png
deleted file mode 100644
index 900a666..0000000
Binary files a/docs/solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating_files/figure-html/cell-10-output-1.png and /dev/null differ
diff --git a/solcore-workshop/1-Limits to short-circuit current.py b/solcore-workshop/1-Limits to short-circuit current.py
index 1c242da..7dfed33 100644
--- a/solcore-workshop/1-Limits to short-circuit current.py	
+++ b/solcore-workshop/1-Limits to short-circuit current.py	
@@ -320,8 +320,8 @@ def getPmax(eg):
 
 eg=1.42
 V = np.linspace(0, 1.3, 500)
-db_junction = Junction(kind='DB', T=300, Eg=eg, A=1, R_shunt=np.inf, n=1)
-my_solar_cell = SolarCell([db_junction], T=300, R_series=0)
+db_junction = Junction(kind='DB', T=300, Eg=eg, A=1, R_shunt=1e-1, n=1)
+my_solar_cell = SolarCell([db_junction], T=300, R_series=1e-4)
 
 solar_cell_solver(my_solar_cell, 'iv',
                       user_options={'T_ambient': 300, 'db_mode': 'top_hat', 'voltages': V, 'light_iv': True,
@@ -336,6 +336,7 @@ def getPmax(eg):
 plt.text(0.1,300,f'Jsc {my_solar_cell.iv.Isc:.2f}')
 plt.text(0.1,280,f'Voc {my_solar_cell.iv.Voc:.2f}')
 plt.text(0.1,260,f'Pmax {my_solar_cell.iv.Pmpp:.2f}')
+plt.text(0.1,240,f'FF (%) {100*my_solar_cell.iv.FF:.2f}')
 plt.xlabel('Voltage (V)')
 plt.ylabel('Current (A/m$^2$)')
 plt.show()
diff --git a/solcore-workshop/2-Efficiency_limits.py b/solcore-workshop/2-Efficiency_limits.py
index 35ab4df..97474c4 100644
--- a/solcore-workshop/2-Efficiency_limits.py
+++ b/solcore-workshop/2-Efficiency_limits.py
@@ -40,7 +40,7 @@
 # Setup the AM1.5G solar spectrum
 wl = np.linspace(300, 4000, 4000) * 1e-9    # wl contains the x-coordinate in wavelength
 am15g = LightSource(source_type='standard', x=wl*1e9, version='AM1.5g',
-                    outputs="power_density_per_nm")
+                    output_units="power_density_per_nm")
 
 plt.figure()
 plt.title('Spectral Irradiance')
@@ -176,7 +176,7 @@ def getJsc(eg):
 
 plt.figure()
 plt.title('Trivich-Flinn Single Junction Efficiency Limit')
-plt.plot(eg, 100*eg*jsc/b,label='AM1.5G')  # Divide by 10 to convert from A.m^-2 to mA.cm^-2
+plt.plot(eg, 100*eg*jsc/b,label='AM1.5G')
 plt.xlim(0.5, 2.5)
 plt.xlabel('Band Gap energy (eV)')
 plt.ylabel('Efficiency (%)')
diff --git a/solcore-workshop/7-InGaP_Si_planar.py b/solcore-workshop/7-InGaP_Si_planar.py
index c14152a..fa573bc 100644
--- a/solcore-workshop/7-InGaP_Si_planar.py
+++ b/solcore-workshop/7-InGaP_Si_planar.py
@@ -48,7 +48,7 @@
 
 
 #| output: false
-# download_db() # uncomment to download database
+download_db() # uncomment to download database
 
 MgF2_pageid = search_db(os.path.join("MgF2", "Rodriguez-de Marcos"))[0][0];
 ZnS_pageid = search_db(os.path.join("ZnS", "Querry"))[0][0];
diff --git a/solcore-workshop/8-grating_pyramids_OPTOS.py b/solcore-workshop/8-grating_pyramids_OPTOS.py
index dae7c63..3fb44fd 100644
--- a/solcore-workshop/8-grating_pyramids_OPTOS.py
+++ b/solcore-workshop/8-grating_pyramids_OPTOS.py
@@ -19,7 +19,7 @@
 # 
 # First, importing relevant packages:
 
-# In[19]:
+# In[1]:
 
 
 import numpy as np
@@ -48,7 +48,7 @@
 # To make sure we are using the same optical constants for Si, load the same Si n/k data used in the paper
 # linked above:
 
-# In[20]:
+# In[2]:
 
 
 create_new_material("Si_OPTOS", "data/Si_OPTOS_n.txt", "data/Si_OPTOS_k.txt",
@@ -59,12 +59,12 @@
 # 
 # Setting options (taking the default options for everything not specified explicitly):
 
-# In[21]:
+# In[3]:
 
 
-angle_degrees_in = 8
+angle_degrees_in = 8 # same as in Fraunhofer paper
 
-wavelengths = np.linspace(900, 1200, 30) * 1e-9
+wavelengths = np.linspace(900, 1200, 20) * 1e-9
 
 Si = material("Si_OPTOS")()
 Air = material("Air")()
@@ -72,12 +72,13 @@
 options = default_options()
 options.wavelengths = wavelengths
 options.theta_in = angle_degrees_in * np.pi / 180 # incidence angle (polar angle)
-options.n_theta_bins = 100
+options.n_theta_bins = 50
 options.c_azimuth = 0.25
-options.n_rays = 25 * 25 * 1300 # number of rays per wavelength in ray-tracing
+options.n_rays = 5e5 # number of rays per wavelength in ray-tracing
 options.project_name = "OPTOS_comparison"
 options.orders = 60 # number of RCWA orders to use (more = better convergence, but slower)
 options.pol = "u" # unpolarized light
+options.only_incidence_angle = False
 
 
 # ## Defining the structures
@@ -86,7 +87,7 @@
 # These are squares, rotated by 45 degrees. The halfwidth is calculated based on the area fill factor
 # of the etched pillars given in the paper.
 
-# In[22]:
+# In[4]:
 
 
 x = 1000
@@ -107,7 +108,7 @@
 # We specify the method to use to calculate the redistribution matrices in each case and create
 # the bulk layer.
 
-# In[23]:
+# In[5]:
 
 
 surf = regular_pyramids(elevation_angle=55, upright=False)
@@ -126,7 +127,7 @@
     layers=back_materials,
     name="crossed_grating_back",
     d_vectors=d_vectors,
-    rcwa_orders=60,
+    rcwa_orders=20,
 )
 
 back_surf_planar = Interface("TMM", layers=[], name="planar_back")
@@ -139,7 +140,7 @@
 # the files already exist). We don't need to process the final structure because it will use matrices
 # calculated for `SC_fig6` and `SC_fig7`.
 
-# In[24]:
+# In[6]:
 
 
 SC_fig6 = Structure(
@@ -152,8 +153,8 @@
     [front_surf_pyramids, bulk_Si, back_surf_grating], incidence=Air, transmission=Air
 )
 
-process_structure(SC_fig6, options)
-process_structure(SC_fig7, options)
+process_structure(SC_fig6, options, save_location='current')
+process_structure(SC_fig7, options, save_location='current')
 
 
 # ## Calculating R/A/T
@@ -162,14 +163,14 @@
 # multiplication, and get the required result out (absorption in the bulk) for each cell. We
 # also load the results from the reference paper to compare them to the ones calculated with RayFlare.
 
-# In[25]:
+# In[7]:
 
 
 #| output: false
 
-results_fig6 = calculate_RAT(SC_fig6, options)
-results_fig7 = calculate_RAT(SC_fig7, options)
-results_fig8 = calculate_RAT(SC_fig8, options)
+results_fig6 = calculate_RAT(SC_fig6, options, save_location='current')
+results_fig7 = calculate_RAT(SC_fig7, options, save_location='current')
+results_fig8 = calculate_RAT(SC_fig8, options, save_location='current')
 
 RAT_fig6 = results_fig6[0]
 RAT_fig7 = results_fig7[0]
@@ -185,7 +186,7 @@
 # Finally, we use TMM to calculate the absorption in a structure with a planar front and planar rear,
 # as a reference.
 
-# In[26]:
+# In[8]:
 
 
 struc = tmm_structure([Layer(si("200um"), Si)], incidence=Air, transmission=Air)
@@ -198,7 +199,7 @@
 # 
 # Plot everything together, including data from the reference paper for comparison:
 
-# In[27]:
+# In[9]:
 
 
 palhf = sns.color_palette("hls", 4)
@@ -231,7 +232,7 @@
 # much better overall, giving a large boost in the absorption at long wavelengths and also reducing the reflection
 # significantly at shorter wavelengths. Plotting reflection and transmission emphasises this:
 
-# In[28]:
+# In[10]:
 
 
 fig = plt.figure()
@@ -262,7 +263,7 @@
 # 
 # Plot the redistribution matrix for the rear grating (summed over azimuthal angles) at 1100 nm:
 
-# In[29]:
+# In[11]:
 
 
 theta_intv, phi_intv, angle_vector = make_angle_vector(
diff --git a/solcore-workshop/9a-GaInP_GaAs_Si_grating.py b/solcore-workshop/9a-GaInP_GaAs_Si_grating.py
index fd28eff..c18d72f 100644
--- a/solcore-workshop/9a-GaInP_GaAs_Si_grating.py
+++ b/solcore-workshop/9a-GaInP_GaAs_Si_grating.py
@@ -10,7 +10,7 @@
 # 
 # ## Setting up
 
-# In[58]:
+# In[1]:
 
 
 from solcore import material, si
@@ -34,7 +34,7 @@
 #   the exact compositions of some semiconductor alloy layers (InGaP, AlInP and AlGaAs)
 #    are not given in the paper and are thus reasonable guesses.
 
-# In[59]:
+# In[2]:
 
 
 #| output: false
@@ -68,7 +68,7 @@
 # together in a logical way. In this example, we will only do optical simulations, so
 # we will not set e.g. diffusion lengths or doping levels.
 
-# In[60]:
+# In[3]:
 
 
 ARC = [
@@ -114,7 +114,7 @@
 # 
 # Now we define the planar cell, and options for the solver:
 
-# In[61]:
+# In[4]:
 
 
 cell_planar = tmm_structure(
@@ -136,14 +136,13 @@
 options.wavelengths = wl
 options.coherency_list = coherency_list
 options.coherent = False
-options.project_name = "III_V_Si_cell"
 
 
 # Run the TMM calculation for the planar cell, and then extract the relevant layer
 # absorptions. These are used to calculate limiting currents (100% internal quantum
 # efficiency), which are displayed on the plot with the absorption in each layer.
 
-# In[62]:
+# In[5]:
 
 
 tmm_result = cell_planar.calculate(options=options)
@@ -193,7 +192,7 @@
 #  of different shapes can be defined for the RCWA solver can be found
 #  [here](https://rayflare.readthedocs.io/en/latest/Examples/rcwa_examples.html).
 
-# In[63]:
+# In[6]:
 
 
 x = 1000
@@ -213,7 +212,7 @@
 # interface. Finally, we put everything together into the ARMM `Structure`, also giving
 #  the incidence and transmission materials.
 
-# In[64]:
+# In[7]:
 
 
 bulk_Si = BulkLayer(280e-6, Si(), name="Si_bulk")
@@ -228,7 +227,7 @@
     layers=back_materials,
     name="crossed_grating_back",
     d_vectors=d_vectors,
-    rcwa_orders=30,
+    rcwa_orders=60,
 )
 
 cell_grating = Structure(
@@ -246,7 +245,7 @@
 #  back mirror, and only doing the new calculation at these wavelengths. At shorter
 #  wavelengths, the results previously calculated using TMM can be used.
 
-# In[65]:
+# In[ ]:
 
 
 #| output: false
@@ -255,8 +254,11 @@
 # than 1E-4
 
 options.wavelengths = wl_rcwa
+options.project_name = "III_V_Si_cell"
+options.n_theta_bins = 50
+options.c_azimuth = 0.25
 
-process_structure(cell_grating, options, overwrite=True)
+process_structure(cell_grating, options, save_location='current')
 results_armm = calculate_RAT(cell_grating, options)
 RAT = results_armm[0]
 
@@ -267,7 +269,7 @@
 # limiting current for the Si junction. The plot compares the absorption in the Si with
 #  and without the grating.
 
-# In[66]:
+# In[ ]:
 
 
 Si_A_total = np.zeros(len(wl))
@@ -295,3 +297,5 @@
 # ## Questions
 # 
 # - Why does the grating only affect the absorption in Si at long wavelengths?
+# - What is the reason for using the angular redistribution matrix method, rather
+#   than defining an RCWA-only structure (`rcwa_structure`)?
diff --git a/solcore-workshop/Si_cell_pyramids/int_0.nc b/solcore-workshop/Si_cell_pyramids/int_0.nc
deleted file mode 100644
index 5e878e5..0000000
Binary files a/solcore-workshop/Si_cell_pyramids/int_0.nc and /dev/null differ
diff --git a/solcore-workshop/Si_cell_pyramids/int_1.nc b/solcore-workshop/Si_cell_pyramids/int_1.nc
deleted file mode 100644
index 3a3fc11..0000000
Binary files a/solcore-workshop/Si_cell_pyramids/int_1.nc and /dev/null differ
diff --git a/solcore-workshop/data/C60_Ren_k.txt b/solcore-workshop/data/C60_Ren_k.txt
new file mode 100644
index 0000000..c6438c2
--- /dev/null
+++ b/solcore-workshop/data/C60_Ren_k.txt
@@ -0,0 +1,78 @@
+2.07251E-07	0.560641027
+2.08649E-07	0.613189371
+2.0999E-07	0.663973176
+2.11349E-07	0.718279911
+2.12724E-07	0.768359131
+2.1435E-07	0.822498656
+2.16472E-07	0.877853853
+2.18189E-07	0.912223431
+2.20475E-07	0.933981866
+2.22665E-07	0.920114892
+2.24434E-07	0.893824707
+2.27365E-07	0.840763145
+2.29188E-07	0.80295422
+2.32389E-07	0.756294588
+2.35126E-07	0.73506586
+2.38239E-07	0.743910175
+2.42583E-07	0.801494502
+2.45659E-07	0.861600935
+2.4814E-07	0.916806277
+2.50357E-07	0.97605405
+2.52774E-07	1.032664095
+2.54251E-07	1.076891912
+2.56254E-07	1.139209211
+2.58942E-07	1.176623666
+2.61699E-07	1.229726994
+2.64578E-07	1.261408307
+2.67353E-07	1.261963932
+2.70494E-07	1.234507086
+2.72827E-07	1.192590554
+2.75164E-07	1.141253979
+2.77652E-07	1.069598406
+2.79645E-07	1.017309562
+2.8159E-07	0.952739256
+2.83173E-07	0.912759005
+2.85259E-07	0.861471381
+2.87207E-07	0.809274266
+2.89047E-07	0.764819024
+2.92213E-07	0.702284472
+2.94786E-07	0.653721676
+2.97853E-07	0.610804504
+3.0336E-07	0.561521827
+3.1024E-07	0.539829819
+3.15234E-07	0.559442683
+3.19397E-07	0.586361661
+3.23315E-07	0.620016605
+3.25749E-07	0.668620265
+3.29031E-07	0.723396723
+3.32645E-07	0.773227234
+3.35359E-07	0.818465364
+3.39615E-07	0.85368935
+3.44955E-07	0.830204524
+3.4752E-07	0.795550483
+3.50543E-07	0.745921871
+3.54329E-07	0.664201041
+3.57984E-07	0.590667948
+3.61393E-07	0.51375292
+3.6576E-07	0.44809657
+3.70717E-07	0.389564153
+3.78994E-07	0.300735479
+3.86188E-07	0.252833196
+3.97743E-07	0.209917067
+4.15175E-07	0.182119839
+4.26235E-07	0.189316222
+4.42472E-07	0.195150341
+4.56172E-07	0.168347296
+4.71248E-07	0.117405328
+4.91004E-07	0.084727433
+5.20029E-07	0.064414284
+5.50878E-07	0.051481038
+5.87821E-07	0.041381218
+6.24474E-07	0.036400885
+6.75826E-07	0.031233647
+7.45124E-07	0.024265852
+8.01244E-07	0.022350957
+9.16455E-07	0.018111147
+1.10248E-06	0.014573877
+1.39346E-06	0.010962139
+1.78088E-06	0.009125449
diff --git a/solcore-workshop/data/C60_Ren_n.txt b/solcore-workshop/data/C60_Ren_n.txt
new file mode 100644
index 0000000..fade586
--- /dev/null
+++ b/solcore-workshop/data/C60_Ren_n.txt
@@ -0,0 +1,88 @@
+2.08502E-07	0.837591019
+2.10972E-07	0.857648047
+2.12872E-07	0.893074983
+2.14623E-07	0.946690846
+2.15566E-07	0.976319201
+2.16996E-07	1.024284663
+2.18447E-07	1.079295984
+2.19921E-07	1.139943991
+2.21412E-07	1.195659897
+2.22924E-07	1.252080389
+2.2471E-07	1.305339183
+2.26394E-07	1.344370522
+2.2942E-07	1.372436092
+2.32089E-07	1.370257554
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diff --git a/solcore-workshop/data/CsBr10p_1to2_k_shifted.txt b/solcore-workshop/data/CsBr10p_1to2_k_shifted.txt
new file mode 100644
index 0000000..fea748f
--- /dev/null
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diff --git a/solcore-workshop/data/CsBr10p_1to2_n_shifted.txt b/solcore-workshop/data/CsBr10p_1to2_n_shifted.txt
new file mode 100644
index 0000000..de66622
--- /dev/null
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diff --git a/solcore-workshop/data/IZO_Ballif_rO2_10pcnt_k.txt b/solcore-workshop/data/IZO_Ballif_rO2_10pcnt_k.txt
new file mode 100644
index 0000000..b601db5
--- /dev/null
+++ b/solcore-workshop/data/IZO_Ballif_rO2_10pcnt_k.txt
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diff --git a/solcore-workshop/data/IZO_Ballif_rO2_10pcnt_n.txt b/solcore-workshop/data/IZO_Ballif_rO2_10pcnt_n.txt
new file mode 100644
index 0000000..dd47f75
--- /dev/null
+++ b/solcore-workshop/data/IZO_Ballif_rO2_10pcnt_n.txt
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new file mode 100644
index 0000000..d5966a5
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+0.00000143	2.85E-13
+0.00000144	2.06E-13
+0.00000145	1.39E-13
\ No newline at end of file
diff --git a/solcore-workshop/data/Si_OPTOS_n.txt b/solcore-workshop/data/Si_OPTOS_n.txt
new file mode 100644
index 0000000..a265003
--- /dev/null
+++ b/solcore-workshop/data/Si_OPTOS_n.txt
@@ -0,0 +1,121 @@
+0.00000025	1.665
+0.00000026	1.757
+0.00000027	2.068
+0.00000028	2.959
+0.00000029	4.356
+0.0000003	4.976
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+0.00000036	6.026
+0.00000037	6.891
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+0.00000039	6.039
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+0.00000042	5.119
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+0.00000058	3.988
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+0.0000014	3.49
+0.00000141	3.489
+0.00000142	3.488
+0.00000143	3.487
+0.00000144	3.486
+0.00000145	3.485
\ No newline at end of file
diff --git a/solcore-workshop/data/model_back_ito_k.txt b/solcore-workshop/data/model_back_ito_k.txt
new file mode 100644
index 0000000..4be19ec
--- /dev/null
+++ b/solcore-workshop/data/model_back_ito_k.txt
@@ -0,0 +1,312 @@
+0.0000003	0.36784
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+0.000027005	3.0609
+0.00002946	3.2129
+0.000032406	3.3852
+0.000036006	3.5834
diff --git a/solcore-workshop/data/model_back_ito_n.txt b/solcore-workshop/data/model_back_ito_n.txt
new file mode 100644
index 0000000..124373a
--- /dev/null
+++ b/solcore-workshop/data/model_back_ito_n.txt
@@ -0,0 +1,312 @@
+0.0000003	2.1799
+0.00000031	2.2205
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diff --git a/solcore-workshop/data/model_i_a_silicon_k.txt b/solcore-workshop/data/model_i_a_silicon_k.txt
new file mode 100644
index 0000000..ab71af7
--- /dev/null
+++ b/solcore-workshop/data/model_i_a_silicon_k.txt
@@ -0,0 +1,314 @@
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diff --git a/solcore-workshop/data/model_i_a_silicon_n.txt b/solcore-workshop/data/model_i_a_silicon_n.txt
new file mode 100644
index 0000000..6b81795
--- /dev/null
+++ b/solcore-workshop/data/model_i_a_silicon_n.txt
@@ -0,0 +1,314 @@
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diff --git a/solcore-workshop/data/model_n_a_silicon_k.txt b/solcore-workshop/data/model_n_a_silicon_k.txt
new file mode 100644
index 0000000..888fa6b
--- /dev/null
+++ b/solcore-workshop/data/model_n_a_silicon_k.txt
@@ -0,0 +1,314 @@
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diff --git a/solcore-workshop/data/model_n_a_silicon_n.txt b/solcore-workshop/data/model_n_a_silicon_n.txt
new file mode 100644
index 0000000..cb15dd4
--- /dev/null
+++ b/solcore-workshop/data/model_n_a_silicon_n.txt
@@ -0,0 +1,314 @@
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diff --git a/solcore-workshop/data/model_p_a_silicon_k.txt b/solcore-workshop/data/model_p_a_silicon_k.txt
new file mode 100644
index 0000000..3f68001
--- /dev/null
+++ b/solcore-workshop/data/model_p_a_silicon_k.txt
@@ -0,0 +1,314 @@
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diff --git a/solcore-workshop/data/model_p_a_silicon_n.txt b/solcore-workshop/data/model_p_a_silicon_n.txt
new file mode 100644
index 0000000..1696d6b
--- /dev/null
+++ b/solcore-workshop/data/model_p_a_silicon_n.txt
@@ -0,0 +1,314 @@
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diff --git a/solcore-workshop/data/optos_fig6_sim.csv b/solcore-workshop/data/optos_fig6_sim.csv
new file mode 100644
index 0000000..efe6d64
--- /dev/null
+++ b/solcore-workshop/data/optos_fig6_sim.csv
@@ -0,0 +1,82 @@
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diff --git a/solcore-workshop/data/optos_fig7_sim.csv b/solcore-workshop/data/optos_fig7_sim.csv
new file mode 100644
index 0000000..6dc10d8
--- /dev/null
+++ b/solcore-workshop/data/optos_fig7_sim.csv
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diff --git a/solcore-workshop/data/optos_fig8_sim.csv b/solcore-workshop/data/optos_fig8_sim.csv
new file mode 100644
index 0000000..f5b5ac7
--- /dev/null
+++ b/solcore-workshop/data/optos_fig8_sim.csv
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+1116.6173124485338, 0.5425718872668793
+1120.2081684661716, 0.5191542942695782
+1124.02350752699, 0.4937738598896855
+1127.1656103193081, 0.4723335615966723
+1130.0831396255312, 0.4536839588345233
+1133.0016342410036, 0.42619855874202517
+1135.4711568926416, 0.4026926398899132
+1137.940535853208, 0.3805019733121662
+1140.6335404914562, 0.36419621755177
+1143.1039639755809, 0.33244467867190863
+1145.5727608030886, 0.3155824700262042
+1149.1643854539186, 0.28512932121206214
+1152.531118840322, 0.26037559892016804
+1156.1228198105855, 0.2292238728907039
+1159.7139211030928, 0.20356119725657473
+1163.305604879553, 0.17256685184113707
+1166.4477970182425, 0.15030873642880727
+1171.6097763872294, 0.11551236444492163
+1174.5275175456732, 0.09492360768851649
+1179.913655775695, 0.06113174156252499
+1187.7648696029116, 0.04453932825515183
+1195.1662787949565, 0.03954811449602347
+1199.9523182837374, 0.024512645120270404
diff --git a/solcore-workshop/notebooks/2-Efficiency_limits.ipynb b/solcore-workshop/notebooks/2-Efficiency_limits.ipynb
index 82c1e01..0cb1e09 100644
--- a/solcore-workshop/notebooks/2-Efficiency_limits.ipynb
+++ b/solcore-workshop/notebooks/2-Efficiency_limits.ipynb
@@ -426,7 +426,7 @@
    "source": [
     "plt.figure()\n",
     "plt.title('Trivich-Flinn Single Junction Efficiency Limit')\n",
-    "plt.plot(eg, 100*eg*jsc/b,label='AM1.5G')  # Divide by 10 to convert from A.m^-2 to mA.cm^-2\n",
+    "plt.plot(eg, 100*eg*jsc/b,label='AM1.5G')\n",
     "plt.xlim(0.5, 2.5)\n",
     "plt.xlabel('Band Gap energy (eV)')\n",
     "plt.ylabel('Efficiency (%)')\n",
diff --git a/solcore-workshop/notebooks/7-InGaP_Si_planar.ipynb b/solcore-workshop/notebooks/7-InGaP_Si_planar.ipynb
index 644d021..9ca044f 100644
--- a/solcore-workshop/notebooks/7-InGaP_Si_planar.ipynb
+++ b/solcore-workshop/notebooks/7-InGaP_Si_planar.ipynb
@@ -83,7 +83,7 @@
    ],
    "source": [
     "#| output: false\n",
-    "# download_db() # uncomment to download database\n",
+    "download_db() # uncomment to download database\n",
     "\n",
     "MgF2_pageid = search_db(os.path.join(\"MgF2\", \"Rodriguez-de Marcos\"))[0][0];\n",
     "ZnS_pageid = search_db(os.path.join(\"ZnS\", \"Querry\"))[0][0];\n",
diff --git a/solcore-workshop/notebooks/8-grating_pyramids_OPTOS.ipynb b/solcore-workshop/notebooks/8-grating_pyramids_OPTOS.ipynb
index 86649ab..9113b7e 100644
--- a/solcore-workshop/notebooks/8-grating_pyramids_OPTOS.ipynb
+++ b/solcore-workshop/notebooks/8-grating_pyramids_OPTOS.ipynb
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 24,
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 25,
    "outputs": [
     {
      "name": "stdout",
@@ -99,12 +99,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 26,
    "outputs": [],
    "source": [
-    "angle_degrees_in = 8\n",
+    "angle_degrees_in = 8 # same as in Fraunhofer paper\n",
     "\n",
-    "wavelengths = np.linspace(900, 1200, 30) * 1e-9\n",
+    "wavelengths = np.linspace(900, 1200, 20) * 1e-9\n",
     "\n",
     "Si = material(\"Si_OPTOS\")()\n",
     "Air = material(\"Air\")()\n",
@@ -112,12 +112,13 @@
     "options = default_options()\n",
     "options.wavelengths = wavelengths\n",
     "options.theta_in = angle_degrees_in * np.pi / 180 # incidence angle (polar angle)\n",
-    "options.n_theta_bins = 100\n",
+    "options.n_theta_bins = 50\n",
     "options.c_azimuth = 0.25\n",
-    "options.n_rays = 25 * 25 * 1300 # number of rays per wavelength in ray-tracing\n",
+    "options.n_rays = 5e5 # number of rays per wavelength in ray-tracing\n",
     "options.project_name = \"OPTOS_comparison\"\n",
     "options.orders = 60 # number of RCWA orders to use (more = better convergence, but slower)\n",
-    "options.pol = \"u\" # unpolarized light"
+    "options.pol = \"u\" # unpolarized light\n",
+    "options.only_incidence_angle = False"
    ],
    "metadata": {
     "collapsed": false
@@ -138,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 27,
    "outputs": [],
    "source": [
     "x = 1000\n",
@@ -171,8 +172,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "outputs": [],
+   "execution_count": 28,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fixed h 0.7140740033710572\n"
+     ]
+    }
+   ],
    "source": [
     "surf = regular_pyramids(elevation_angle=55, upright=False)\n",
     "\n",
@@ -190,7 +199,7 @@
     "    layers=back_materials,\n",
     "    name=\"crossed_grating_back\",\n",
     "    d_vectors=d_vectors,\n",
-    "    rcwa_orders=60,\n",
+    "    rcwa_orders=20,\n",
     ")\n",
     "\n",
     "back_surf_planar = Interface(\"TMM\", layers=[], name=\"planar_back\")\n",
@@ -215,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 29,
    "outputs": [
     {
      "name": "stdout",
@@ -245,8 +254,8 @@
     "    [front_surf_pyramids, bulk_Si, back_surf_grating], incidence=Air, transmission=Air\n",
     ")\n",
     "\n",
-    "process_structure(SC_fig6, options)\n",
-    "process_structure(SC_fig7, options)"
+    "process_structure(SC_fig6, options, save_location='current')\n",
+    "process_structure(SC_fig7, options, save_location='current')"
    ],
    "metadata": {
     "collapsed": false
@@ -267,209 +276,211 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 30,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
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+      "After iteration 6 : maximum power fraction remaining = 0.3532151851781935\n",
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+      "After iteration 35 : maximum power fraction remaining = 0.021918129322707085\n",
+      "After iteration 36 : maximum power fraction remaining = 0.019923387859540505\n",
+      "After iteration 37 : maximum power fraction remaining = 0.01811096516113693\n",
+      "After iteration 38 : maximum power fraction remaining = 0.01646287574850684\n",
+      "After iteration 39 : maximum power fraction remaining = 0.01496519883766559\n",
+      "After iteration 40 : maximum power fraction remaining = 0.013603466392507535\n",
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+      "After iteration 43 : maximum power fraction remaining = 0.010218082775920897\n",
+      "After iteration 44 : maximum power fraction remaining = 0.009288379212257435\n",
+      "After iteration 1 : maximum power fraction remaining = 0.7684485778644032\n",
+      "After iteration 2 : maximum power fraction remaining = 0.6498120326619525\n",
+      "After iteration 3 : maximum power fraction remaining = 0.5556035597064046\n",
+      "After iteration 4 : maximum power fraction remaining = 0.47594302345764516\n",
+      "After iteration 5 : maximum power fraction remaining = 0.4072113153652765\n",
+      "After iteration 6 : maximum power fraction remaining = 0.34809252840971433\n",
+      "After iteration 7 : maximum power fraction remaining = 0.29738019985173136\n",
+      "After iteration 8 : maximum power fraction remaining = 0.25397645350634224\n",
+      "After iteration 9 : maximum power fraction remaining = 0.21686645646024627\n",
+      "After iteration 10 : maximum power fraction remaining = 0.1851610063676129\n",
+      "After iteration 11 : maximum power fraction remaining = 0.15808197091920137\n",
+      "After iteration 12 : maximum power fraction remaining = 0.134959244983207\n",
+      "After iteration 13 : maximum power fraction remaining = 0.11521681002977455\n",
+      "After iteration 14 : maximum power fraction remaining = 0.0983615431079143\n",
+      "After iteration 15 : maximum power fraction remaining = 0.08397166258319096\n",
+      "After iteration 16 : maximum power fraction remaining = 0.07168677942670297\n",
+      "After iteration 17 : maximum power fraction remaining = 0.06119906447612819\n",
+      "After iteration 18 : maximum power fraction remaining = 0.05224565428556535\n",
+      "After iteration 19 : maximum power fraction remaining = 0.04460210794436754\n",
+      "After iteration 20 : maximum power fraction remaining = 0.03807680511252559\n",
+      "After iteration 21 : maximum power fraction remaining = 0.03250615231987759\n",
+      "After iteration 22 : maximum power fraction remaining = 0.027750486613114383\n",
+      "After iteration 23 : maximum power fraction remaining = 0.023690576257086014\n",
+      "After iteration 24 : maximum power fraction remaining = 0.020224632476291207\n",
+      "After iteration 25 : maximum power fraction remaining = 0.017265757918946807\n",
+      "After iteration 26 : maximum power fraction remaining = 0.01473976821159397\n",
+      "After iteration 27 : maximum power fraction remaining = 0.012583332124789154\n",
+      "After iteration 28 : maximum power fraction remaining = 0.010742383790091441\n",
+      "After iteration 29 : maximum power fraction remaining = 0.009170767190112814\n"
      ]
     }
    ],
    "source": [
     "#| output: false\n",
     "\n",
-    "results_fig6 = calculate_RAT(SC_fig6, options)\n",
-    "results_fig7 = calculate_RAT(SC_fig7, options)\n",
-    "results_fig8 = calculate_RAT(SC_fig8, options)\n",
+    "results_fig6 = calculate_RAT(SC_fig6, options, save_location='current')\n",
+    "results_fig7 = calculate_RAT(SC_fig7, options, save_location='current')\n",
+    "results_fig8 = calculate_RAT(SC_fig8, options, save_location='current')\n",
     "\n",
     "RAT_fig6 = results_fig6[0]\n",
     "RAT_fig7 = results_fig7[0]\n",
@@ -502,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 31,
    "outputs": [],
    "source": [
     "struc = tmm_structure([Layer(si(\"200um\"), Si)], incidence=Air, transmission=Air)\n",
@@ -527,12 +538,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 32,
    "outputs": [
     {
      "data": {
       "text/plain": "
",
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -581,12 +592,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 33,
    "outputs": [
     {
      "data": {
       "text/plain": "
",
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -633,12 +644,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 35,
    "outputs": [
     {
      "data": {
       "text/plain": "
",
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -648,7 +659,7 @@
     "theta_intv, phi_intv, angle_vector = make_angle_vector(\n",
     "    options[\"n_theta_bins\"], options[\"phi_symmetry\"], options[\"c_azimuth\"])\n",
     "\n",
-    "path = get_savepath(\"default\", options.project_name)\n",
+    "path = get_savepath(save_location='current', project_name=options.project_name)\n",
     "sprs = load_npz(os.path.join(path, SC_fig6[2].name + \"frontRT.npz\"))\n",
     "\n",
     "wl_to_plot = 1100e-9\n",
diff --git a/solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating.ipynb b/solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating.ipynb
index 16c2d19..eafaeb8 100644
--- a/solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating.ipynb
+++ b/solcore-workshop/notebooks/9a-GaInP_GaAs_Si_grating.ipynb
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 9,
    "outputs": [],
    "source": [
     "from solcore import material, si\n",
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 10,
    "outputs": [
     {
      "name": "stdout",
@@ -83,7 +83,7 @@
    "source": [
     "#| output: false\n",
     "\n",
-    "# download_db() # only needs to be run once\n",
+    "download_db() # only needs to be run once\n",
     "\n",
     "MgF2_pageid = search_db(os.path.join(\"MgF2\", \"Rodriguez-de Marcos\"))[0][0];\n",
     "Ta2O5_pageid = search_db(os.path.join(\"Ta2O5\", \"Rodriguez-de Marcos\"))[0][0];\n",
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 11,
    "outputs": [],
    "source": [
     "ARC = [\n",
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 12,
    "outputs": [],
    "source": [
     "cell_planar = tmm_structure(\n",
@@ -203,8 +203,7 @@
     "\n",
     "options.wavelengths = wl\n",
     "options.coherency_list = coherency_list\n",
-    "options.coherent = False\n",
-    "options.project_name = \"III_V_Si_cell\""
+    "options.coherent = False"
    ],
    "metadata": {
     "collapsed": false
@@ -223,7 +222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 13,
    "outputs": [
     {
      "name": "stdout",
@@ -312,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 14,
    "outputs": [],
    "source": [
     "x = 1000\n",
@@ -344,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 15,
    "outputs": [],
    "source": [
     "bulk_Si = BulkLayer(280e-6, Si(), name=\"Si_bulk\")\n",
@@ -359,7 +358,7 @@
     "    layers=back_materials,\n",
     "    name=\"crossed_grating_back\",\n",
     "    d_vectors=d_vectors,\n",
-    "    rcwa_orders=30,\n",
+    "    rcwa_orders=60,\n",
     ")\n",
     "\n",
     "cell_grating = Structure(\n",
@@ -389,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 16,
    "outputs": [
     {
      "name": "stdout",
@@ -403,42 +402,33 @@
       "Material other/resists/Microchem SU-8 2000.yml loaded.\n",
       "Material has no extinction data.\n",
       "Material \"Unnamed\" does not have k-data defined. Returning \"zeroes\"\n",
-      "After iteration 1 : maximum power fraction remaining = 0.8351123109559769\n",
-      "After iteration 2 : maximum power fraction remaining = 0.687868269355163\n",
-      "After iteration 3 : maximum power fraction remaining = 0.5785118671610272\n",
-      "After iteration 4 : maximum power fraction remaining = 0.4962042495726413\n",
-      "After iteration 5 : maximum power fraction remaining = 0.4282941311108549\n",
-      "After iteration 6 : maximum power fraction remaining = 0.3709432688062609\n",
-      "After iteration 7 : maximum power fraction remaining = 0.32192057939581653\n",
-      "After iteration 8 : maximum power fraction remaining = 0.27978325307638774\n",
-      "After iteration 9 : maximum power fraction remaining = 0.24347512733583157\n",
-      "After iteration 10 : maximum power fraction remaining = 0.2121521921112492\n",
-      "After iteration 11 : maximum power fraction remaining = 0.18510821247738285\n",
-      "After iteration 12 : maximum power fraction remaining = 0.1617406655554604\n",
-      "After iteration 13 : maximum power fraction remaining = 0.1415322004820986\n",
-      "After iteration 14 : maximum power fraction remaining = 0.12403812788197738\n",
-      "After iteration 15 : maximum power fraction remaining = 0.10887651958987887\n",
-      "After iteration 16 : maximum power fraction remaining = 0.0957197468847883\n",
-      "After iteration 17 : maximum power fraction remaining = 0.0842870625096907\n",
-      "After iteration 18 : maximum power fraction remaining = 0.07433807735432912\n",
-      "After iteration 19 : maximum power fraction remaining = 0.06566704882073103\n",
-      "After iteration 20 : maximum power fraction remaining = 0.058097909760175676\n",
-      "After iteration 21 : maximum power fraction remaining = 0.051479966236958226\n",
-      "After iteration 22 : maximum power fraction remaining = 0.04568419163525864\n",
-      "After iteration 23 : maximum power fraction remaining = 0.04060004642003831\n",
-      "After iteration 24 : maximum power fraction remaining = 0.03613275694381556\n",
-      "After iteration 25 : maximum power fraction remaining = 0.03220099220737785\n",
-      "After iteration 26 : maximum power fraction remaining = 0.028734883659042608\n",
-      "After iteration 27 : maximum power fraction remaining = 0.025674339400019074\n",
-      "After iteration 28 : maximum power fraction remaining = 0.02296761020345328\n",
-      "After iteration 29 : maximum power fraction remaining = 0.020570070355268658\n",
-      "After iteration 30 : maximum power fraction remaining = 0.0184431813928441\n",
-      "After iteration 31 : maximum power fraction remaining = 0.01655361132569482\n",
-      "After iteration 32 : maximum power fraction remaining = 0.014872485883063004\n",
-      "After iteration 33 : maximum power fraction remaining = 0.013374751781272936\n",
-      "After iteration 34 : maximum power fraction remaining = 0.012038634984396892\n",
-      "After iteration 35 : maximum power fraction remaining = 0.010845179494726003\n",
-      "After iteration 36 : maximum power fraction remaining = 0.00977785440405516\n"
+      "After iteration 1 : maximum power fraction remaining = 0.6469057616767155\n",
+      "After iteration 2 : maximum power fraction remaining = 0.510396418404467\n",
+      "After iteration 3 : maximum power fraction remaining = 0.42294755899496594\n",
+      "After iteration 4 : maximum power fraction remaining = 0.35242765476530513\n",
+      "After iteration 5 : maximum power fraction remaining = 0.2944795570471529\n",
+      "After iteration 6 : maximum power fraction remaining = 0.2489004824250275\n",
+      "After iteration 7 : maximum power fraction remaining = 0.21054314690186315\n",
+      "After iteration 8 : maximum power fraction remaining = 0.1782441554113579\n",
+      "After iteration 9 : maximum power fraction remaining = 0.15103707339431371\n",
+      "After iteration 10 : maximum power fraction remaining = 0.1281063420037193\n",
+      "After iteration 11 : maximum power fraction remaining = 0.10876400033180816\n",
+      "After iteration 12 : maximum power fraction remaining = 0.09243244715694393\n",
+      "After iteration 13 : maximum power fraction remaining = 0.0786284360094387\n",
+      "After iteration 14 : maximum power fraction remaining = 0.06694823227066962\n",
+      "After iteration 15 : maximum power fraction remaining = 0.0570545192625161\n",
+      "After iteration 16 : maximum power fraction remaining = 0.04866525834423359\n",
+      "After iteration 17 : maximum power fraction remaining = 0.04154438342152516\n",
+      "After iteration 18 : maximum power fraction remaining = 0.03549408268779598\n",
+      "After iteration 19 : maximum power fraction remaining = 0.03034840754278627\n",
+      "After iteration 20 : maximum power fraction remaining = 0.0259679782834894\n",
+      "After iteration 21 : maximum power fraction remaining = 0.022235594007966958\n",
+      "After iteration 22 : maximum power fraction remaining = 0.0190525885113449\n",
+      "After iteration 23 : maximum power fraction remaining = 0.016335802418758056\n",
+      "After iteration 24 : maximum power fraction remaining = 0.014015064914848201\n",
+      "After iteration 25 : maximum power fraction remaining = 0.012031097231282931\n",
+      "After iteration 26 : maximum power fraction remaining = 0.010333765442069404\n",
+      "After iteration 27 : maximum power fraction remaining = 0.008880622769719997\n"
      ]
     }
    ],
@@ -449,9 +439,12 @@
     "# than 1E-4\n",
     "\n",
     "options.wavelengths = wl_rcwa\n",
+    "options.project_name = \"III_V_Si_cell\"\n",
+    "options.n_theta_bins = 40\n",
+    "options.c_azimuth = 0.25\n",
     "\n",
-    "process_structure(cell_grating, options, overwrite=True)\n",
-    "results_armm = calculate_RAT(cell_grating, options)\n",
+    "process_structure(cell_grating, options, save_location='current')\n",
+    "results_armm = calculate_RAT(cell_grating, options, save_location='current')\n",
     "RAT = results_armm[0]"
    ],
    "metadata": {
@@ -473,12 +466,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 17,
    "outputs": [
     {
      "data": {
       "text/plain": "
",
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -515,7 +508,9 @@
    "source": [
     "## Questions\n",
     "\n",
-    "- Why does the grating only affect the absorption in Si at long wavelengths?"
+    "- Why does the grating only affect the absorption in Si at long wavelengths?\n",
+    "- What is the reason for using the angular redistribution matrix method, rather\n",
+    "  than defining an RCWA-only structure (`rcwa_structure`)?"
    ],
    "metadata": {
     "collapsed": false
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