Example of a quasi-3D modelling of a 3J solar cell ================================================== (code shown for 1J, but the extension is immediate) .. image:: square_mask_V1_3.png :width: 40% .. image:: square_mask_IV.png :width: 40% .. image:: circular_mask_V1_3.png :width: 40% .. image:: circular_mask_IV.png :width: 40% - Required extra files, available in `Solcore's Github repository (Examples folder) `_: - masks_illumination.png - masks_sq.png - masks_cl.png .. code-block:: Python import numpy as np import matplotlib.pyplot as plt from solcore.structure import Junction from solcore.solar_cell import SolarCell from solcore.light_source import LightSource from solcore.spice import solve_quasi_3D # First we load the masks defining the illumination pattern and the contacts. Both must be greyscale images # The solver expect images with values between 0 and 255 and imread of a PNG image is between 0 and 1, even when # it is in grey, so we scale it multiplying by 255. If the image were JPG, the result would be already in (0,255). illuminationMask = (plt.imread('masks_illumination.png') * 255).astype(np.int) contactsMask = (plt.imread('masks_sq.png') * 255).astype(np.int) nx, ny = illuminationMask.shape # For symmetry arguments (not completely true for the illumination), we can mode just 1/4 of the device and then # multiply the current by 4 illuminationMask = illuminationMask[int(nx / 2):, int(ny / 2):] contactsMask = contactsMask[int(nx / 2):, int(ny / 2):] # Size of the pixels (m) Lx = 10e-6 Ly = 10e-6 # Height of the metal fingers (m) h = 2.2e-6 # Contact resistance (Ohm m2) Rcontact = 3e-10 # Resistivity metal fingers (Ohm m) Rline = 2e-8 # Bias (V) vini = 0 vfin = 1.3 step = 0.01 T = 298 db_junction = Junction(kind='2D', T=T, reff=1, jref=300, Eg=0.66, A=1, R_sheet_top=100, R_sheet_bot=1e-16, R_shunt=1e16, n=3.5) db_junction2 = Junction(kind='2D', T=T, reff=1, jref=300, Eg=1.4, A=1, R_sheet_top=100, R_sheet_bot=1e-16, R_shunt=1e16, n=3.5) db_junction3 = Junction(kind='2D', T=T, reff=0.5, jref=300, Eg=1.8, A=1, R_sheet_top=100, R_sheet_bot=100, R_shunt=1e16, n=3.5) # For a single junction, this will have >28800 nodes and for the full 3J it will be >86400, so it is worth to # exploit symmetries whenever possible. A smaller number of nodes also makes the solver more robust. my_solar_cell = SolarCell([db_junction2], T=T) wl = np.linspace(350, 2000, 301) * 1e-9 light_source = LightSource(source_type='standard', version='AM1.5g', x=wl, output_units='photon_flux_per_m', concentration=100) options = {'light_iv': True, 'wavelength': wl, 'light_source': light_source} V, I, Vall, Vmet = solve_quasi_3D(my_solar_cell, illuminationMask, contactsMask, options=options, Lx=Lx, Ly=Ly, h=h, R_back=1e-16, R_contact=Rcontact, R_line=Rline, bias_start=vini, bias_end=vfin, bias_step=step) # Since we model 1/4 of the device, we multiply the current by 4 I = I * 4 # This will plot the voltage at the last external voltage point V=1.3 V for the top layer of the top junction. plt.figure(1) plt.imshow(Vall[:, :, -2, -1]) plt.figure(2) plt.semilogy(V, abs(I)) plt.show()