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Main Code.py
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Main Code.py
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# -*- coding: utf-8 -*-
"""
Created on Sun Jun 7 18:48:00 2020
@author: apoor
"""
###to clear variable in spyder console#####
from IPython import get_ipython
get_ipython().magic('reset -sf')
############################################
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from decimal import Decimal
import math
import xlsxwriter
import csv
import matplotlib.ticker as mticker
############################### Lifetime functions #####################################################
class Lifetime:
def __init__(self,sig_p, sig_n, Eg_minus_E_defects, Impurity, T, n0, N_defects,J0,Jl,Rs, sweep_dn=False): #do all calulations
#Eg=1.124 #### in eV
N0_eeh=3.3e17 #### in cm-3
N0_ehh=7e17 #### in cm-3
kb=1.3807e-23 ###### in (J/K)
k = 8.61758e-5 #[eV/K]
q=1.6021765e-19 ######### in C
self.Eg=1.1785-((9.025e-5)*T)-((3.05e-7)*(T**2))
#self.Eg=1.170-((0.0004734*(T**2))/(T+636))
self.E_defects = self.Eg - Eg_minus_E_defects
self.ni=2.9135*(10**15)*(T**1.6)*np.exp((-self.Eg)/(2*k*T))
self.V = np.arange(0.2, 0.95, 0.0002)
self.Vlist = self.V.tolist() ################# NEEDS CORRECTION
self.J01 = J0*(T/300)**3*(np.exp(-self.Eg/(k*T)))/(np.exp(-1.124/(k*300)))
#self.J01 = J0*(np.exp((-q*self.Eg)/(k*T)))/(np.exp((-q*1.124)/(k*300)))
Nd=n0
Na=1
p0 = self.ni**2/Nd
dn1 = (- Nd + np.sqrt((Nd**2)+4*((self.ni**2)*np.exp(self.V/(k*T)))))/2
n1=(n0 + dn1)
p1=(p0 + dn1)
###### Schenk #############
Ry = 1.655e-2
a_ex = 3.7185e-7
Tn = T*k/Ry
#model parameters
alpha_e = 0.5187
alpha_h = 0.4813
b_e = 8
b_h = 1
c_e = 1.3346
c_h = 1.2365
d_e = 0.893
d_h = 1.153
g_e = 12
g_h = 4
h_e = 3.91
h_h = 4.2
j_e = 2.8585
j_h = 2.9307
k_e = 0.012
k_h = 0.19
p_e = 7/30
p_h = 7/30
q_e = 0.75
q_h = 0.25
#normalization to Bohr radius
p_n = p1*a_ex**3
n_n = n1*a_ex**3
c = p_n + n_n
n_p = alpha_e*n_n + alpha_h*p_n
Nd_n = Nd*a_ex**3
Na_n = Na*a_ex**3
Nc = Nd_n + Na_n
#exchange-correlation
xcn = ( (4*np.pi)**3*c**2*((48*n_n/(np.pi*g_e))**(1/3)+c_e*np.log(1+d_e*n_p**p_e)) + 8*np.pi*alpha_e/g_e*n_n*Tn**2 + np.sqrt(8*np.pi*c)*Tn**(5/2) ) / ( (4*np.pi)**3*c**2 + Tn**3 + b_e*np.sqrt(c)*Tn**2 + 40*c**(3/2)*Tn ) * Ry * 1000
xcp = ( (4*np.pi)**3*c**2*((48*p_n/(np.pi*g_h))**(1/3)+c_h*np.log(1+d_h*n_p**p_h)) + 8*np.pi*alpha_h/g_h*p_n*Tn**2 + np.sqrt(8*np.pi*c)*Tn**(5/2) ) / ( (4*np.pi)**3*c**2 + Tn**3 + b_h*np.sqrt(c)*Tn**2 + 40*c**(3/2)*Tn ) * Ry * 1000
#ionic
iD = Nc *(1+c**2/Tn**3) / ( np.sqrt(Tn*c/(2*np.pi))*(1+ h_e*np.log(1+np.sqrt(c)/Tn)) + j_e*c**2/Tn**3*n_p**(3/4)*(1+k_e*n_p**q_e) ) *Ry*1000
iA = Nc *(1+c**2/Tn**3) / ( np.sqrt(Tn*c/(2*np.pi))*(1+ h_h*np.log(1+np.sqrt(c)/Tn)) + j_h*c**2/Tn**3*n_p**(3/4)*(1+k_h*n_p**q_h) ) *Ry*1000
self.dEg = (xcn + xcp + iD + iA)*1e-3
##################################################################################
#############################################################
self.ni_eff=self.ni*np.exp((self.dEg*q)/(2*kb*T))
self.dn = (- n0 + np.sqrt(n0**2+4*(self.ni_eff**2*np.exp(self.V/(k*T)))) )/2
if sweep_dn:
self.dn = np.logspace(10,17,1775)
self.p0=(self.ni_eff**2)/n0
self.geeh=1+13*(1-np.tanh((n0/N0_eeh)**0.66))
self.gehh=1+7.5*(1-np.tanh((self.p0/N0_ehh)**0.63))
self.Vth_e=0.06761*(T**3)-98.513*(T**2)+72696*T+5709700
self.Vth_h=-25.507*(T**2) + 45810*T + 5E+06
self.tau_n0=1/(self.Vth_e*N_defects*sig_n)
self.tau_p0=1/(self.Vth_h*N_defects*sig_p)
# self.tau_n0=1/(self.Vth_e*N_defects*sig_n)
# self.tau_p0=1/(self.Vth_h*N_defects*sig_p)
#
#
# self.Vth=0.06761*(T**3)-98.513*(T**2)+72696*T+5709700
# self.tau_n0=1/(self.Vth*N_defects*sig_nT)
# self.tau_p0=1/(self.Vth*N_defects*sig_pT)
self.Nc=2.86*(10**19)*((T/300)**1.58)
self.Nv=3.1*(10**19)*((T/300)**1.85)
self.n1=self.Nc*np.exp((-q*Eg_minus_E_defects)/(kb*T))
self.p1=self.Nv*np.exp((-q*self.E_defects)/(kb*T))
############### B(Alterman 2005) ####################
bmax=1
rmax=0.2
rmin=0
smax=1.5e18
smin=1e07
wmax=4e18
wmin=1e9
b2=0.54
b4=1.25
r1=320
r2=2.5
s1=550
s2=3
w1=365
w2=3.54
b1=smax+(smin-smax)/(1+(T/s1)**s2)
b3=wmax+(wmin-wmax)/(1+(T/w1)**w2)
bmin=rmax+(rmin-rmax)/(1+(T/r1)**r2)
self.Blow=10**(-9.65614-((8.05258e-2)*T)+(6.02695e-4*(T**2))-((2.29844E-6)*(T**3))+(4.31934E-9*(T**4))-(3.16154E-12*(T**5)))
self.n=n0+self.dn
self.p=self.p0+self.dn
self.B=self.Blow*(bmin+(bmax-bmin)/(1+((self.n+self.p)/(2*b1))**b2+((self.n+self.p)/(2*b3))**b4))
self.tau_rad=(self.dn*1000)/((self.n*self.p-(self.ni_eff**2))*self.B)
self.tau_aug=(self.dn*1000)/((self.n*self.p-(self.ni_eff**2))*(2.5e-31*self.geeh*n0+8.5e-32*self.gehh*self.p0+3e-29*self.dn**0.92))
self.tau_srh=(self.dn*1000)/((self.n*self.p-(self.ni_eff**2))/(self.tau_p0*(n0+self.n1+self.dn)+self.tau_n0*(self.p0+self.p1+self.dn)))
self.tau_eff_no_surf=1/((1/self.tau_srh)+(1/self.tau_aug)+(1/self.tau_rad))
####################
self.U_aug= ((self.n*self.p-(self.ni_eff**2))*(2.5e-31*self.geeh*n0+8.5e-32*self.gehh*self.p0+3e-29*self.dn**0.92))
self.U_rad= ((self.n*self.p-(self.ni_eff**2))*self.B)
self.U_srh=((self.n*self.p-(self.ni_eff**2))/(self.tau_p0*(n0+self.n1+self.dn)+self.tau_n0*(self.p0+self.p1+self.dn)))
####################
self.Js = self.J01*(np.exp(self.V/(n02*k*T))-1)*1e3 #Surface recombination current
self.Us = self.J01*(np.exp(self.V/(n02*k*T))-1)/q/width #rate
self.tau_surf=(self.dn*1000)/self.Us
self.tau_eff=1/((1/self.tau_srh)+(1/self.tau_aug)+(1/self.tau_rad)+(1/self.tau_surf))
#Auger, radiative calculated according to Richter parameterization
#SRH calculated according to SRH eqn above
self.Jaug = q*width*self.U_aug*1e3 #Auger
self.Jrad = q*width*self.U_rad*1e3 #Radiative
self.Jsrh = q*width*self.U_srh*1e3 #SRH
#self.Jint = q*width*self.U_int*1e3 #Intrinsic (Radiative + Auger)
self.Jr = Jl-self.Jrad-self.Jaug-self.Jsrh-self.Js #### Current density of everything
ind = np.where(np.diff(np.sign(self.Jr)))[0] #Voc injection level index, where Jr crosses 0
self.Voc = self.V[ind]*1000
self.P = self.Jr*self.V
ind2 = np.where(self.P==np.max(self.P)) #MPP injection level index
self.Vmp=self.V[ind2]*1000
self.dn_mpp = self.dn[ind2]#MPP injection level
self.dn_oc=self.dn[ind]
self.FF = (max(self.P)/(self.Voc/1000)/max(self.Jr))*100
self.Eff = max(self.P)
self.Jsc = max(self.Jr)
self.Rch = self.Voc/(self.Jsc*1e-3);
self.rs = Rs/self.Rch
self.FFr = self.FF*(1-1.1*self.rs)+self.rs**2/5.4
self.Effr = self.Jsc*self.Voc*self.FFr
self.Eff_check = self.Jsc*self.Voc*self.FF;
### Intrincisc + SRH-limited values
self.Jr_no_surf=Jl-self.Jrad-self.Jaug-self.Jsrh
ind_no_surf = np.where(np.diff(np.sign(self.Jr_no_surf)))[0] #Voc injection level index, where Jr crosses 0, Andre add -Js
self.Voc_no_surf = (self.V[ind_no_surf])*1000
self.P_no_surf = (self.Jr_no_surf)*self.V # André add - Js
ind2_no_surf = np.where(self.P_no_surf==np.max(self.P_no_surf)) #MPP injection level index
self.Vmp_no_surf=(self.V[ind2_no_surf])*1000
self.Eff_no_surf = (max(self.P_no_surf))
self.FF_no_surf = (max(self.P_no_surf)/(self.Voc_no_surf/1000)/max(self.Jr_no_surf))*100
self.dn_oc_no_surf=self.dn[ind_no_surf]
self.dn_mpp_no_surf = self.dn[ind2_no_surf]
##### Intrinsic - aug+rad
self.Jr_intrinsic=Jl-self.Jrad-self.Jaug
ind_intrinsic = np.where(np.diff(np.sign(self.Jr_intrinsic)))[0] #Voc injection level index, where Jr crosses 0, Andre add -Js
self.Voc_intrinsic = (self.V[ind_intrinsic])*1000
self.P_intrinsic = (self.Jr_intrinsic)*self.V # André add - Js
ind2_intrinsic = np.where(self.P_intrinsic==np.max(self.P_intrinsic)) #MPP injection level index
self.Vmp_intrinsic=(self.V[ind2_intrinsic])*1000
self.Eff_intrinsic = (max(self.P_intrinsic))
self.FF_intrinsic = (max(self.P_intrinsic)/(self.Voc_intrinsic/1000)/max(self.Jr_intrinsic))*100
self.dn_intrinsic=self.dn[ind_intrinsic]
self.dn_mpp_intrinsic = self.dn[ind2_intrinsic]
def plot_lifetime(tau_rad_list, tau_aug_list, tau_srh_list, tau_eff_no_surf_list, tau_eff_list,tau_surf_list, dn_list, plotlabel, savelabel, color,dash=False):
f1 = plt.figure(1)
#plt.plot(dn_list,tau_rad_list,'-',label = 'T ='"{:.0f}\u00b0C".format(param-273.15))
if dash==False:
plt.plot(dn_list,tau_rad_list,'-',label = plotlabel, color = color,linewidth=3)
else:
plt.plot(dn_list,tau_rad_list,'--',label = plotlabel, color = color,linewidth=3)
plt.yscale('log')
plt.xscale('log')
# plt.title("Radiative Lifetime vs Injection level \n/n("+savelabel+")")
plt.ylabel('Radiative lifetime (ms)',fontsize = 19)
plt.xlabel(r'Excess carrier density ($\mathregular{cm^{-3}}$)',fontsize = 18)
# ax =plt.gca()
#
# ticks=[]
# for i in ax.get_yticks().tolist():
# if int(i)>0:
# ticks.append('{:,}'.format(int(i)))
# else:
# ticks.append('{:,}'.format(float(i)))
# ax.set_yticklabels(ticks)
#ax.tick_params(direction = 'in', which = 'both')
plt.legend(fontsize=16,labelspacing=0.3)
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.ylim(1e-1,1e3)
#
plt.xlim(1e14,1e17)
f1.savefig("Radiative ("+savelabel+").png", bbox_inches='tight', bbox_to_anchor = (0,0), dpi = 300)
f2 = plt.figure(2)
if dash==False:
plt.plot(dn_list,tau_aug_list,'-',label = plotlabel, color=color,linewidth=3)
else:
plt.plot(dn_list,tau_aug_list,'--',label = plotlabel, color=color,linewidth=3)
plt.yscale('log')
plt.xscale('log')
# plt.title("Auger Lifetime vs Injection level ("+savelabel+")")
plt.ylabel('Auger lifetime (ms)', fontsize = 19)
plt.xlabel(r'Excess carrier density ($\mathregular{cm^{-3}}$)',fontsize = 18)
# ax =plt.gca()
# ticks=[]
# for i in ax.get_yticks().tolist():
# if int(i)>0:
# ticks.append('{:,}'.format(int(i)))
# else:
# ticks.append('{:,}'.format(float(i)))
# ax.set_yticklabels(ticks)
#ax.tick_params(direction = 'in', which = 'both')
plt.legend(fontsize=16,labelspacing=0.3)
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.ylim(1e-1,1e3)
#
plt.xlim(1e14,1e17)
filename2='Auger ('+savelabel+').png'
f2.savefig(filename2, bbox_inches="tight", bbox_to_anchor = (0,0), loc = 'best', dpi = 300)
f3 = plt.figure(3)
if dash==False:
plt.plot(dn_list,tau_srh_list,'-',label = plotlabel, color=color,linewidth=3)
else:
plt.plot(dn_list,tau_srh_list,'--',label = plotlabel, color=color,linewidth=3)
plt.yscale('log')
plt.xscale('log')
# plt.title("SRH Lifetime vs Injection level ("+savelabel+")")
plt.ylabel('Bulk SRH lifetime (ms)',fontsize = 19)
plt.xlabel(r'Excess carrier density ($\mathregular{cm^{-3}}$)',fontsize = 18)
# ax =plt.gca()
# ticks=[]
# for i in ax.get_yticks().tolist():
# if int(i)>0:
# ticks.append('{:,}'.format(int(i)))
# else:
# ticks.append('{:,}'.format(float(i)))
# ax.set_yticklabels(ticks)
#ax.tick_params(direction = 'in', which = 'both')
plt.legend(fontsize=16,labelspacing=0.14,loc='lower right')
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
#plt.ylim(1e-1,1e3)
plt.xlim(1e14,1e17)
f3.savefig("SRH ("+savelabel+").png", bbox_inches='tight', bbox_to_anchor = (0,0), dpi = 300)
f4 = plt.figure(4)
if dash==False:
plt.plot(dn_list,tau_eff_no_surf_list,'-',label = plotlabel, color=color,linewidth=3)
else:
plt.plot(dn_list,tau_eff_no_surf_list,'--',label = plotlabel, color=color,linewidth=3)
plt.yscale('log')
plt.xscale('log')
# plt.title("Effective lifetime (No surface) vs Injection level ("+savelabel+")\n")
plt.ylabel('Effective lifetime (ms)',fontsize = 19)
# ax =plt.gca()
# ticks=[]
# for i in ax.get_yticks().tolist():
# if int(i)>0:
# ticks.append('{:,}'.format(int(i)))
# else:
# ticks.append('{:,}'.format(float(i)))
# ax.set_yticklabels(ticks)
#ax.tick_params(direction = 'in', which = 'both')
plt.xlabel(r'Excess carrier density ($\mathregular{cm^{-3}}$)',fontsize = 18)
plt.legend(fontsize=16,labelspacing=0.3,loc='best')
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.ylim(1e-1,1e3)
plt.xlim(1e14,1e17)
f4.savefig("Effective (no surface) ("+savelabel+").png", bbox_inches='tight', bbox_to_anchor=(1, 0.5), dpi = 300)
f5 = plt.figure(5)
if dash==False:
plt.plot(dn_list,tau_eff_list,'-',label = plotlabel, color=color,linewidth=3)
else:
plt.plot(dn_list,tau_eff_list,'--',label = plotlabel, color=color,linewidth=3)
plt.yscale('log')
plt.xscale('log')
# plt.title("Effective lifetime vs Injection level ("+savelabel+")\n")
plt.ylabel('Effective lifetime (ms)',fontsize = 19)
plt.xlabel(r'Excess carrier density ($\mathregular{cm^{-3}}$)',fontsize = 18)
# ax =plt.gca()
# ticks=[]
# for i in ax.get_yticks().tolist():
# if int(i)>0:
# ticks.append('{:,}'.format(int(i)))
# else:
# ticks.append('{:,}'.format(float(i)))
# ax.set_yticklabels(ticks)
#ax.tick_params(direction = 'in', which = 'both')
plt.legend(loc='center left',bbox_to_anchor=(1,0.5), fontsize=14)
plt.legend(fontsize=16,labelspacing=0.3)
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.ylim(1e-1,1e3)
#
plt.xlim(1e14,1e17)
f5.savefig("Effective ("+savelabel+").png", bbox_inches='tight', bbox_to_anchor=(1, 0.5), dpi = 300)
f6 = plt.figure(6)
if dash==False:
plt.plot(dn_list,tau_surf_list,'-',label = plotlabel, color=color,linewidth=3)
else:
plt.plot(dn_list,tau_surf_list,'--',label = plotlabel, color=color,linewidth=3)
plt.yscale('log')
plt.xscale('log')
# plt.title("Surf Lifetime vs Injection level ("+savelabel+")")
plt.ylabel('Surface lifetime (ms)',fontsize = 19)
plt.xlabel(r'Excess carrier density ($\mathregular{cm^{-3}}$)',fontsize = 18)
# ax =plt.gca()
# ticks=[]
# for i in ax.get_yticks().tolist():
# if int(i)>0:
# ticks.append('{:,}'.format(int(i)))
# else:
# ticks.append('{:,}'.format(float(i)))
# ax.set_yticklabels(ticks)
#ax.tick_params(direction = 'in', which = 'both')
plt.legend(fontsize=16,labelspacing=0.3, loc='upper right')
plt.xticks(fontsize = 15)
plt.yticks(fontsize = 15)
plt.ylim(1e-1,1e3)
#
plt.xlim(1e14,1e17)
f6.savefig("Surf ("+savelabel+").png", bbox_inches='tight', bbox_to_anchor = (0,0), dpi = 300)
#####################################################################################################################################################################
def plot_contour(Rho_list,Y_data,Eff_no_surf,FF_no_surf,Voc_no_surf,Vmp_no_surf,Y_label, _log=True):
f6 = plt.figure(6)
contour = plt.contourf(Rho_list,Y_data,Eff_no_surf, cmap = 'plasma_r', levels = np.linspace(np.min(Eff_no_surf), np.max(Eff_no_surf),100))
plt.xscale('log')
if _log:
plt.yscale('log')
plt.xticks(fontsize = 13)
plt.yticks(fontsize = 13)
# plt.title("Efficiency Vs Wafer resistivity \n (N-type Wafer) ",fontsize=14)
plt.ylabel(Y_label,fontsize=14)
plt.xlabel(r'Wafer resistivity ($\Omega$ cm)',fontsize=14)
cbar = plt.colorbar(contour,format='%.0f')
cbar.set_label('Efficiency (%)',fontsize=13, labelpad=+6)
f6.savefig('Efficiency Vs Wafer resistivity - N-type.png', bbox_inches="tight")
f7 = plt.figure(7)
contour2 = plt.contourf(Rho_list,Y_data,FF_no_surf, cmap = 'plasma_r',levels = np.linspace(np.min(FF_no_surf), np.max(FF_no_surf),100))
plt.xscale('log')
if _log:
plt.yscale('log')
plt.xticks(fontsize = 13)
plt.yticks(fontsize = 13)
#plt.title("FF Vs Wafer resistivity \n (N-type Wafer) ",fontsize=14)
plt.ylabel(Y_label,fontsize=14)
plt.ylabel(r'Bulk lifetime $\tau$$\mathregular{_{bulk}}$ (ms)',fontsize=14)
plt.xlabel(r'Wafer resistivity ($\Omega$ cm)',fontsize=14)
cbar2 = plt.colorbar(contour2,format='%.0f')
cbar2.set_label('FF (%)',fontsize=13,labelpad=+6)
f7.savefig('FF Vs Wafer resistivity - N-type.png', bbox_inches="tight")
f8 = plt.figure(8)
contour3 = plt.contourf(Rho_list,Y_data,Voc_no_surf, cmap = 'plasma_r', levels = np.linspace(np.min(Voc_no_surf), np.max(Voc_no_surf),100))
plt.xscale('log')
if _log:
plt.yscale('log')
plt.xticks(fontsize = 13)
plt.yticks(fontsize = 13)
# plt.title("Voc Vs Wafer resistivity \n (N-type Wafer) ",fontsize=14)
plt.ylabel(Y_label,fontsize=14)
plt.xlabel(r'Wafer resistivity ($\Omega$ cm)',fontsize=14)
cbar3 = plt.colorbar(contour3,format='%.0f')
cbar3.set_label('$\mathregular{V_{oc}}$ (mV)',fontsize=13, labelpad=+6)
f8.savefig('Voc Vs Wafer resistivity - N-type.png', bbox_inches="tight")
f9 = plt.figure(9)
contour4 = plt.contourf(Rho_list,Y_data,Vmp_no_surf, cmap = 'plasma_r', levels = np.linspace(np.min(Vmp_no_surf), np.max(Vmp_no_surf),100))
plt.xscale('log')
if _log:
plt.yscale('log')
plt.xticks(fontsize = 13)
plt.yticks(fontsize = 13)
# plt.title("Vmp Vs Wafer resistivity \n (N-type Wafer) ",fontsize=14)
plt.ylabel(Y_label,fontsize=14)
plt.xlabel(r'Wafer resistivity ($\Omega$ cm)',fontsize=14)
cbar4 = plt.colorbar(contour4,format='%.0f')
cbar4.set_label('$\mathregular{V_{mp}}$ (mV)',fontsize=13, labelpad=+6)
f9.savefig('Vmp Vs Wafer resistivity - N-type.png', bbox_inches="tight")
#######################################################################################################################################################
################## Lambertian light trapping ###################
#computes Lambertian light absorption from wafer thickness
df_jl = pd.read_csv(r'lambertian light inputs.csv')
import scipy.integrate as it
W_list = np.linspace(20,400,39) #list of wafer thicknesses
alpha = df_jl['alpha cSi']
n = df_jl['n']
Jl_list=[]
for w in W_list:
w = w*1e-6
lambertian = (1-np.exp(-4*alpha*w)+np.exp(-4*alpha*w)*(1-1/n**2)*(1-np.exp(-4*alpha*w))/(1-np.exp(-4*alpha*w)+1/n**2*np.exp(-4*alpha*w)))
lambertian_w_r = (1 - df_jl['primary R'])*lambertian #reflection is typical of SHJ cell
jl = it.simps(lambertian*df_jl['AM1.5g(l2)']*df_jl['WL'])*2*0.000040328 #integrate lambertian absorpbtion with AM1.5 spectrum
#This seems suspect, but it gives the same result as the EPFL loss equations spreadsheet
Jl_list.append(jl)
####### Calculate Jr for a given J0, and a given thickness #########
W_list = W_list #list of wafer thicknesses from above in cm
Jl_dict = dict(zip(W_list, Jl_list)) #Jl from above
############################################################################################
##################### MAIN - PLOTS ########################
################ Inputs #############
n02=1
Rs=0.9
W=180 ######### Thickness of the cell - 180 um ############
kb=1.3807e-23 ###### in (J/K)
k = 8.61758e-5 #[eV/K]
Jl = Jl_dict[W]
width = W*1e-4 #(in cm)
J0=10e-15
#####data from paper######
fname='impurity.txt'
impurities=[]
with open(fname, newline = '') as games:
filedata = csv.reader(games, delimiter=',')
for data in filedata:
impurities.append(data)
colormap = plt.cm.gist_ncar
colors = [colormap(i) for i in np.linspace(0, 0.95, 8)] #play with the linsapce to vary the color gradients
#plt.gca().set_color_cycle(colors)
###############################################################################
############## CHOOSE ###################
if 1:
########## Sweep temperature ##########
defect_num = 12 ### see defect list text file or defect list from paaper file
N_defects=5e9 ##### impurity defect density in cm-3
n0=1.549e15 ##### doping density in cm-3 FOR 3 OHM -CM
#n0=2.203e11 ## 20k ohm-cm
init_t = 30 # in Celcius
fin_t = 80 #i n Celcius
div=10 # interval of the temp needed for output
ini_T=init_t+273.15 ######## lowest temp in the range in Kelvins K
fin_T=fin_t+273.15######## highest temp in the range in Kelvins K
T_div=(fin_T-ini_T)/(div)+1
T_list= np.linspace(ini_T,fin_T,T_div) #### list of temp in the range in Kelvins K
J01_list=[]
tau_surf_list=[]
workbook = xlsxwriter.Workbook('data.xlsx')
for i in range(len(T_list)):
T = T_list[i]
sig_n = eval(impurities[defect_num][3])
sig_p = eval(impurities[defect_num][4])
Eg_minus_E_defects = float(impurities[defect_num][2])
defect = impurities[defect_num][1]
#calc =Lifetime(sig_p[defect_num-1], sig_n[defect_num-1], Eg_minus_E_defects[defect_num-1],defect[defect_num-1], T_list[i], n0, N_defects, J0, Jl, Rs, sweep_dn=False)
calc =Lifetime(sig_p, sig_n, Eg_minus_E_defects,defect, T_list[i], n0, N_defects, J0, Jl, Rs, sweep_dn=False)
print('Calculating for '+'T ='"{:.0f}\u00b0C".format(T_list[i]-273.15))
ind1=min(range(len(calc.dn)), key=lambda i: abs(calc.dn[i]-1e13))
ind2=min(range(len(calc.dn)), key=lambda i: abs(calc.dn[i]-1e15))
ind3=min(range(len(calc.dn)), key=lambda i: abs(calc.dn[i]-1e17))
dn_list = [calc.dn[ind1], calc.dn[ind2],calc.dn[ind3]]
tau_rad_List=[calc.tau_rad[ind1], calc.tau_rad[ind2],calc.tau_rad[ind3]]
tau_srh_List=[calc.tau_srh[ind1], calc.tau_srh[ind2],calc.tau_srh[ind3]]
tau_surf_List=[calc.tau_surf[ind1], calc.tau_surf[ind2],calc.tau_surf[ind3]]
tau_eff_no_surf_List=[calc.tau_eff_no_surf[ind1], calc.tau_eff_no_surf[ind2],calc.tau_eff_no_surf[ind3]]
tau_eff_List=[calc.tau_eff[ind1], calc.tau_eff[ind2],calc.tau_eff[ind3]]
p0_List=[calc.p0[ind1], calc.p0[ind2],calc.p0[ind3]]
n_List=[calc.n[ind1], calc.n[ind2],calc.n[ind3]]
p_List=[calc.p[ind1], calc.p[ind2],calc.p[ind3]]
tau_n0_List=[calc.tau_n0]
tau_p0_List=[calc.tau_p0]
n1_List=[calc.n1]
p1_List=[calc.p1]
J01_List=[calc.J01]
ni_eff_List=[calc.ni_eff[ind1], calc.ni_eff[ind2],calc.ni_eff[ind3]]
# sig_nT_list=[calc.sig_nT[ind1], calc.sig_nT[ind2],calc.sig_nT[ind3]]
# sig_pT_list=[calc.sig_pT[ind1], calc.sig_pT[ind2],calc.sig_pT[ind3]]
worksheet = workbook.add_worksheet(str(T_list[i]))
worksheet.write(0,0,'dn')
worksheet.write(0,1,'tau_rad')
worksheet.write(0,2,'tau_srh')
worksheet.write(0,3,'tau_surf')
worksheet.write(0,4,'tau_eff_no_surf')
worksheet.write(0,5,'tau_eff')
worksheet.write(0,6,'tau_n0')
worksheet.write(0,7,'tau_p0')
worksheet.write(0,8,'n0')
worksheet.write(0,9,'p0')
worksheet.write(0,10,'n1')
worksheet.write(0,11,'p1')
worksheet.write(0,12,'n')
worksheet.write(0,13,'p')
worksheet.write(0,14,'J01')
worksheet.write(0,15,'ni_eff')
worksheet.write_column(1,0,dn_list)
worksheet.write_column(1,1,tau_rad_List)
worksheet.write_column(1,2,tau_srh_List)
worksheet.write_column(1,3,tau_surf_List)
worksheet.write_column(1,4,tau_eff_no_surf_List)
worksheet.write_column(1,5,tau_eff_List)
worksheet.write_column(1,6,tau_n0_List)
worksheet.write_column(1,7,tau_p0_List)
# worksheet.write_column(1,8,n0_List)
worksheet.write_column(1,9,p0_List)
worksheet.write_column(1,10,n1_List)
worksheet.write_column(1,11,p1_List)
worksheet.write_column(1,12,n_List)
worksheet.write_column(1,13,p_List)
worksheet.write_column(1,14,J01_List)
worksheet.write_column(1,15,ni_eff_List)
plot_lifetime(calc.tau_rad, calc.tau_aug, calc.tau_srh, calc.tau_eff_no_surf,calc.tau_eff,calc.tau_surf, calc.dn, 'T ='"{:.0f}\u00b0C".format(T_list[i]-273.15), defect, colors[i])
dn=calc.dn
B = calc.B
Blow=calc.Blow
ni_eff=calc.ni_eff
dEg=calc.dEg
J01_list.append(calc.J01)
tau_surf_list.append(calc.tau_surf)
workbook.close()
###############################################################################
############## CHOOSE ###################
########################## sweep all defects ####################
if 0:
N_defects=1e9 ##### impurity defect density in cm-3
#n0=1.549e15
n0=2.203e11##### doping density in cm-3
t = 25 ### temp in C
defects_list=[1,2,6,7,14,10,11]
T =t+273.15
i=0
for defect_num in defects_list:
calc = Lifetime(eval(impurities[defect_num][3]), eval(impurities[defect_num][4]), float(impurities[defect_num][2]),impurities[defect_num][1], T, n0, N_defects, J0, Jl, Rs, sweep_dn=False)
print('Calculating for '+impurities[defect_num][1])
plot_lifetime(calc.tau_rad, calc.tau_aug, calc.tau_srh, calc.tau_eff_no_surf, calc.tau_eff,calc.tau_surf, calc.dn, impurities[defect_num][1], 'T ='"{:.0f}\u00b0C".format(T-273.15),colors[i])
i+=1
##################################################################################
############## CHOOSE ###################
########################## sweep N_defect - number of defects or defect concentration ####################
if 0:
t = 25
defect_num = 10### see defect list text file
##### doping density in cm-3
n0=1.549e15
N_defect_list = np.logspace(8,10,3)
T = t+273.15
colors = ['limegreen','orangered','gold']
f = mticker.ScalarFormatter(useOffset=False, useMathText=True)
g = lambda x,pos : "${}$".format(f._formatSciNotation('%1.10e' % x))
fmt = mticker.FuncFormatter(g)
Eff_no_surf_result=[]
Eff_no_surf_result2=[]
Eff_result=[]
Eff_result2=[]
Eff_intrinsic_result=[]
Eff_intrinsic_result2=[]
for i in range(len(N_defect_list)):
calc = Lifetime(eval(impurities[defect_num][3]), eval(impurities[defect_num][4]), float(impurities[defect_num][2]),impurities[defect_num][1], T, n0, N_defect_list[i], J0, Jl, Rs, sweep_dn=False)
print('Calculating for number of defects = '+"{:.0E}".format(Decimal(N_defect_list[i])))
plot_lifetime(calc.tau_rad, calc.tau_aug, calc.tau_srh, calc.tau_eff_no_surf,calc.tau_eff, calc.tau_surf,calc.dn, r"{} ".format(fmt(N_defect_list[i])), impurities[defect_num][1]+' at '+'T ='"{:.0f}\u00b0C".format(T-273.15),colors[i])
Eff_no_surf_result.append(calc.Eff_no_surf)
Eff_result.append(calc.Eff)
Eff_intrinsic_result.append(calc.Eff_intrinsic)
n0=2.203e11
for i in range(len(N_defect_list)):
calc = Lifetime(eval(impurities[defect_num][3]), eval(impurities[defect_num][4]), float(impurities[defect_num][2]),impurities[defect_num][1], T, n0, N_defect_list[i], J0, Jl, Rs, sweep_dn=False)
print('Calculating for number of defects = '+"{:.0E}".format(Decimal(N_defect_list[i])))
plot_lifetime(calc.tau_rad, calc.tau_aug, calc.tau_srh, calc.tau_eff_no_surf,calc.tau_eff, calc.tau_surf,calc.dn, r"{}".format(fmt(N_defect_list[i])), impurities[defect_num][1]+' at '+'T ='"{:.0f}\u00b0C".format(T-273.15),colors[i],dash=True)
Eff_no_surf_result2.append(calc.Eff_no_surf)
Eff_result2.append(calc.Eff)
Eff_intrinsic_result2.append(calc.Eff_intrinsic)
################################################################################
############## CHOOSE ###################
# ######################### sweep n0 - doping concentration ####################
if 0:
t = 25 #### temp in C
defect_num = 3 ### see defect list text file
N_defects=1e9 ##### impurity defect density in cm-3
n0_list = np.logspace(11,16,6)
T = t+273.15
for i in range(len(n0_list)):
calc = Lifetime(sig_p[defect_num-1], sig_n[defect_num-1], Eg_minus_E_defects[defect_num-1],defect[defect_num-1], T, n0_list[i], N_defects, J0, Jl, Rs, sweep_dn=True)
print('Calculating for doping concentration = '+"{:.0E}".format(Decimal(n0_list[i])))
plot_lifetime(calc.tau_rad, calc.tau_aug, calc.tau_srh, calc.tau_eff_no_surf, calc.dn, "{:.0E}".format(Decimal(n0_list[i])), defect[defect_num-1].decode()+' at '+'T ='"{:.0f}\u00b0C".format(T-273.15),colors[i])
#######################################################
###################### PLot tau_bulk vs doping and vs resistivity ################
if 0:
t = 25 #### temp in C
defect_num = 10 ### see defect list text file
N_defects=1e9 ##### impurity defect density in cm-3
doping_input = pd.read_csv(r'ntype.csv')
n0_list = doping_input['Dopant concentration']
Rho_list =doping_input['Resistivity']
Rho_dict = dict(zip(n0_list, Rho_list))
# n0_list = np.logspace(11,16,20)
T = t+273.15
bulk_lifetime=[]
lifetime=[]
SRH=[]
RAD=[]
AUG=[]
surf=[]
Voc=[]
Vmp=[]
Eff=[]
FF=[]
Voc_no_surf=[]
Vmp_no_surf=[]
Eff_no_surf=[]
FF_no_surf=[]
for i in range(len(n0_list)):
# Rho = Rho_dict[Nd(i)]
calc = Lifetime(eval(impurities[defect_num][3]), eval(impurities[defect_num][4]), float(impurities[defect_num][2]),impurities[defect_num][1], T, n0_list[i], N_defects, J0, Jl, Rs, sweep_dn=False)
#calc = Lifetime(sig_p[defect_num-1], sig_n[defect_num-1], Eg_minus_E_defects[defect_num-1],defect[defect_num-1], T, n0_list[i], N_defects, J0, Jl, Rs, sweep_dn=False)
dn = list(calc.dn)
inj= min(dn, key=lambda x:abs(x-1e15))
ind = dn.index(inj)
bulk_lifetime.append(calc.tau_eff_no_surf[ind])
lifetime.append(calc.tau_eff[ind])
SRH.append(calc.tau_srh[ind])
RAD.append(calc.tau_rad[ind])
AUG.append(calc.tau_aug[ind])
surf.append(calc.tau_surf[ind])
Voc.append(calc.Voc)
Vmp.append(calc.Vmp)
FF.append(calc.FF)
Eff.append(calc.Eff)
Voc_no_surf.append(calc.Voc_no_surf)
Vmp_no_surf.append(calc.Vmp_no_surf)
Eff_no_surf.append(calc.Eff_no_surf)
FF_no_surf.append(calc.FF_no_surf)
f6 = plt.figure(6)
plt.plot(n0_list, bulk_lifetime)
plt.xscale('log')
plt.xlabel('Doping concentration ($\mathregular{cm^{-3}}$)',fontsize=14)
plt.ylabel('Bulk lifetime (ms)',fontsize=14.5)
plt.xticks(fontsize = 13)
plt.yticks(fontsize = 13)
ax =plt.gca()
#ax.set_xticklabels(['{:,}'.format(int(x)) for x in ax.get_xticks().tolist()])
ticks=[]
for i in ax.get_xticks().tolist():
if int(i)>0:
ticks.append('{:,}'.format(int(i)))
else:
ticks.append('{:,}'.format(float(i)))
ax.set_xticklabels(ticks)
ax.tick_params(direction = 'in', which = 'both')
f6.savefig('Lifetime vs doping conc',bbox_inches='tight')
f7 = plt.figure(7)
plt.plot(Rho_list, SRH,label='Bulk SRH',linestyle='-.',color='g',linewidth=3)
plt.plot(Rho_list, AUG,label='Auger',linestyle=':',color='m',linewidth=3)
plt.plot(Rho_list, RAD,label='Radiative',linestyle='--',color='b',linewidth=3 )
plt.plot(Rho_list, bulk_lifetime,label='Effective',linestyle='-',linewidth=4, color='r')
plt.xscale('log')
plt.yscale('log')
plt.legend(loc='lower right', fontsize=16)
ax=plt.gca()
ticks=[]
for i in ax.get_xticks().tolist():
if int(i)>0:
ticks.append('{:,}'.format(int(i)))
else:
ticks.append('{:,}'.format(float(i)))
ax.set_xticklabels(ticks)
plt.xlabel(r'Wafer resistivity ($\Omega$cm)',fontsize=20)
plt.ylabel('Lifetime (ms)',fontsize=23)
#plt.ylabel(r'{\fontsize{29pt}{3em}\selectfont{}{Lifetime (ms)\n}{\fontsize{18pt}{3em}\selectfont{}(At 1E15 $\mathregular{cm^{-3}}$ MCD)}')
plt.yticks(fontsize = 18)
plt.xticks(fontsize = 18)
plt.xlim(5e-2,5e3)
plt.ylim(1e-2,1e3)
ax =plt.gca()
ticks=[]
for i in ax.get_xticks().tolist():
if int(i)>0:
ticks.append('{:,}'.format(int(i)))
else:
ticks.append('{:,}'.format(float(i)))
ax.set_xticklabels(ticks)
ticks=[]
for i in ax.get_yticks().tolist():
if int(i)>0:
ticks.append('{:,}'.format(int(i)))
else:
ticks.append('{:,}'.format(float(i)))
ax.set_yticklabels(ticks)
#ax.set_yticklabels(['{:,}'.format(int(x)) for x in ax.get_yticks().tolist()])
ax.tick_params(direction = 'in', which = 'both')
f7.savefig('Lifetime (no surf)vs resistivity',bbox_inches='tight')
f8 = plt.figure(8)
plt.plot(Rho_list, SRH,label='Bulk SRH',linestyle='-.',color='g',linewidth=3)
plt.plot(Rho_list, AUG,label='Auger',linestyle=':',color='m',linewidth=3)
plt.plot(Rho_list, RAD,label='Radiative',linestyle='--',color='b',linewidth=3 )
plt.plot(Rho_list, surf,label='Surface',linestyle='-',color='c',linewidth=3 )
plt.plot(Rho_list, lifetime,label='Effective',linestyle='-',linewidth=4, color='r')
plt.xscale('log')
plt.yscale('log')
plt.legend(loc='lower right', fontsize=16)
plt.xlabel(r'Wafer resistivity ($\Omega$cm)',fontsize=20)
plt.ylabel('Lifetime (ms)',fontsize=23)
#plt.ylabel(r'{\fontsize{29pt}{3em}\selectfont{}{Lifetime (ms)\n}{\fontsize{18pt}{3em}\selectfont{}(At 1E15 $\mathregular{cm^{-3}}$ MCD)}')
plt.yticks(fontsize = 18)
plt.xticks(fontsize = 18)
plt.xlim(5e-2,5e3)
plt.ylim(1e-2,1e3)
ax =plt.gca()
ticks=[]
for i in ax.get_xticks().tolist():
if int(i)>0:
ticks.append('{:,}'.format(int(i)))
else:
ticks.append('{:,}'.format(float(i)))
ax.set_xticklabels(ticks)
ticks=[]
for i in ax.get_yticks().tolist():
if int(i)>0:
ticks.append('{:,}'.format(int(i)))
else:
ticks.append('{:,}'.format(float(i)))
ax.set_yticklabels(ticks)
#ax.set_yticklabels(['{:,}'.format(int(x)) for x in ax.get_yticks().tolist()])
ax.tick_params(direction = 'in', which = 'both')
f8.savefig('Lifetime(surf) vs resistivity',bbox_inches='tight')
f9 = plt.figure(9)
# plt.plot(Rho_list, Voc_no_surf,label='Effective',linestyle='-',linewidth=4, color='b')
plt.plot(Rho_list, Voc,label='$\mathregular{iV_{OC}}$',linestyle='-',linewidth=4, color='r')
plt.xscale('log')
#plt.yscale('log')
#plt.legend(loc='lower right', fontsize=16)
plt.xlabel(r'Bulk resistivity ($\Omega$cm)',fontsize=20)
plt.ylabel('$\mathregular{iV_{OC}}$ (mV)',fontsize=23)
#plt.ylabel(r'{\fontsize{29pt}{3em}\selectfont{}{Lifetime (ms)\n}{\fontsize{18pt}{3em}\selectfont{}(At 1E15 $\mathregular{cm^{-3}}$ MCD)}')
plt.yticks(fontsize = 18)
plt.xticks(fontsize = 18)
plt.xlim(5e-2,5e3)
plt.ylim(660,760)
ax =plt.gca()
ticks=[]
for i in ax.get_xticks().tolist():
if int(i)>0:
ticks.append('{:,}'.format(int(i)))
else:
ticks.append('{:,}'.format(float(i)))
ax.set_xticklabels(ticks)
ticks=[]
for i in ax.get_yticks().tolist():
if int(i)>0:
ticks.append('{:,}'.format(int(i)))
else:
ticks.append('{:,}'.format(float(i)))
ax.set_yticklabels(ticks)
#ax.set_yticklabels(['{:,}'.format(int(x)) for x in ax.get_yticks().tolist()])
ax.tick_params(direction = 'in', which = 'both')
f9.savefig('Voc vs resistivity',bbox_inches='tight')
f10 = plt.figure(10)
# plt.plot(Rho_list, Vmp_no_surf,label='Effective',linestyle='-',linewidth=4, color='b')
plt.plot(Rho_list, Vmp,label='$\mathregular{iV_{mp}}$',linestyle='-',linewidth=4, color='r')
plt.xscale('log')
#plt.yscale('log')
#plt.legend(loc='lower right', fontsize=16)
plt.xlabel(r'Bulk resistivity ($\Omega$cm)',fontsize=20)
plt.ylabel('$\mathregular{iV_{MP}}$ (mV)',fontsize=23)
#plt.ylabel(r'{\fontsize{29pt}{3em}\selectfont{}{Lifetime (ms)\n}{\fontsize{18pt}{3em}\selectfont{}(At 1E15 $\mathregular{cm^{-3}}$ MCD)}')
plt.yticks(fontsize = 18)
plt.xticks(fontsize = 18)
plt.xlim(5e-2,5e3)
plt.ylim(580,680)
ax =plt.gca()
ticks=[]
for i in ax.get_xticks().tolist():
if int(i)>0:
ticks.append('{:,}'.format(int(i)))
else:
ticks.append('{:,}'.format(float(i)))
ax.set_xticklabels(ticks)
ticks=[]
for i in ax.get_yticks().tolist():
if int(i)>0:
ticks.append('{:,}'.format(int(i)))
else:
ticks.append('{:,}'.format(float(i)))
ax.set_yticklabels(ticks) #ax.set_yticklabels(['{:,}'.format(int(x)) for x in ax.get_yticks().tolist()])
ax.tick_params(direction = 'in', which = 'both')
f10.savefig('Vmp vs resistivity',bbox_inches='tight')
f11 = plt.figure(11)
# plt.plot(Rho_list, FF_no_surf,label='Effective',linestyle='-',linewidth=4, color='b')
plt.plot(Rho_list, FF,label='iFF',linestyle='-',linewidth=4, color='r')
plt.xscale('log')
#plt.yscale('log')
#plt.legend(loc='lower right', fontsize=16)
plt.xlabel(r'Bulk resistivity ($\Omega$cm)',fontsize=20)
plt.ylabel('iFF (%)',fontsize=23)
#plt.ylabel(r'{\fontsize{29pt}{3em}\selectfont{}{Lifetime (ms)\n}{\fontsize{18pt}{3em}\selectfont{}(At 1E15 $\mathregular{cm^{-3}}$ MCD)}')
plt.yticks(fontsize = 18)
plt.xticks(fontsize = 18)
plt.xlim(5e-2,5e3)
plt.ylim(83,88)
ax =plt.gca()
ticks=[]
for i in ax.get_xticks().tolist():
if int(i)>0:
ticks.append('{:,}'.format(int(i)))
else:
ticks.append('{:,}'.format(float(i)))
ax.set_xticklabels(ticks)
ticks=[]
for i in ax.get_yticks().tolist():
if int(i)>0:
ticks.append('{:,}'.format(int(i)))
else:
ticks.append('{:,}'.format(float(i)))
ax.set_yticklabels(ticks) #ax.set_yticklabels(['{:,}'.format(int(x)) for x in ax.get_yticks().tolist()])
ax.tick_params(direction = 'in', which = 'both')
f11.savefig('FF vs resistivity',bbox_inches='tight')
f12 = plt.figure(12)
# plt.plot(Rho_list, FF_no_surf,label='Effective',linestyle='-',linewidth=4, color='b')
plt.plot(Rho_list, Eff,label='iEfficiency',linestyle='-',linewidth=4, color='r')
plt.xscale('log')
#plt.yscale('log')
#plt.legend(loc='lower right', fontsize=16)
plt.xlabel(r'Bulk resistivity ($\Omega$cm)',fontsize=20)
plt.ylabel('iEfficiency (%)',fontsize=23)