# First find the equilibrium solution
result = sesame.solve_equilibrium(sys)
# Add illumination
phi0 = 1e17 # incoming flux [1/(cm^2 sec)]
alpha = 2.3e4 # absorbtion coefficient [1/cm]
# Define a function for illumination profile
f = lambda x,y: phi0*alpha*np.exp(-x*alpha) # f is an "inline" function
# This function adds generation to the simulation
sys.generation(f)
# Specify the applied voltage values
voltages = np.linspace(0,1,21)
# Perform I-V calculation
j = sesame.IVcurve(sys, voltages, result, '1dhetero_V')
j = j * sys.scaling.current
result = {'v':voltages, 'j':j}
np.save('IV_values', result)
# plot I-V curve
try:
import matplotlib.pyplot as plt
plt.plot(voltages, j,'-o')
plt.xlabel('Voltage [V]')
plt.ylabel('Current [A/cm^2]')
plt.grid() # add grid
plt.show() # show the plot on the screen
# no matplotlib installed
except ImportError:
# add donor defect along GB
sys.add_line_defects([p1, p2], rho_GB, S_GB, E=E_GB, transition=(1, 0))
# add acceptor defect along GB
sys.add_line_defects([p1, p2], rho_GB, S_GB, E=E_GB, transition=(0, -1))
# Solve equilibirum problem first
solution = sesame.solve_equilibrium(sys)
# define a function for generation profile
f = lambda x, y: 2.3e21 * np.exp(-2.3e4 * x)
# add generation to the system
sys.generation(f)
# Solve problem under short-circuit current conditions
solution = sesame.solve(sys, solution)
# Get analyzer object to compute observables
az = sesame.Analyzer(sys, solution)
# Compute short-circuit current
j = az.full_current()
# Print Jsc
print(j * sys.scaling.current * sys.scaling.length * 1e3)
# specify applied voltages
voltages = np.linspace(0, .1, 1)
# find j-v
j = sesame.IVcurve(sys, voltages, solution, '2dGB_V')
# save the result
result = {'voltages': voltages, 'j': j}
np.save('IV_values', result)
# cycle over all parameter sets
for myjobcounter in my_param_indices:
# Get system for given set of parameters
params = paramlist[myjobcounter]
sys = system(params)
# Get equilibrium solution
eqsolution = sesame.solve(sys, 'Poisson')
# Define a function for generation profile
f = lambda x, y: 2.3e21 * np.exp(-2.3e4 * x)
# add generation to the system
sys.generation(f)
# Specify output filename for given parameter set
outputfile = ''
for paramvalue in params:
outputfile = outputfile + '{0}_'.format(paramvalue)
# Compute J-V curve
jv = sesame.IVcurve(sys, voltages, eqsolution, outputfile)
# Save computed J-V in array
jvset_local[myjobcounter, :] = jv
# Gather results from all processors
mpi_comm.Reduce(jvset_local, jvset)
# Save J-V data for all parameter sets
np.savez("JVset", jvset, paramlist)
# Define a function for the generation rate
phi = 1e17 # photon flux [1/(cm^2 s)]
alpha = 2.3e4 # absorption coefficient [1/cm]
# Define a function for the generation rate
def gfcn(x):
return phi * alpha * np.exp(-alpha * x)
# add generation to system
sys.generation(gfcn)
# IV curve
voltages = np.linspace(0, 0.95, 40)
j = sesame.IVcurve(sys, voltages, solution, '1dhomo_V')
# convert dimensionless current to dimension-ful current
j = j * sys.scaling.current
# save voltage and current values to dictionary
result = {'v': voltages, 'j': j}
# save data to python data file
np.save('IV_values', result)
# save data to an ascii txt file
np.savetxt('IV_values.txt', (voltages, j))
# save data to a matlab data file
try:
from scipy.io import savemat
# define a function for generation profile
f = lambda x, y: 2.3e21 * np.exp(-2.3e4 * x)
# add generation to the system
sys.generation(f)
# Solve problem under short-circuit current conditions
solution = sesame.solve(sys, guess=solution)
# Get analyzer object to compute observables
az = sesame.Analyzer(sys, solution)
# Compute short-circuit current
j = az.full_current()
# Print Jsc
print(j * sys.scaling.current * sys.scaling.length * 1e3)
# specify applied voltages
voltages = np.linspace(0, .9, 10)
# find j-v
j = sesame.IVcurve(sys, voltages, '2dGB_V', guess=solution)
j = j * sys.scaling.current * sys.scaling.length * 1e3
import matplotlib.pyplot as plt
plt.plot(voltages, j, '-o')
# save the result
result = {'voltages': voltages, 'j': j}
np.save('IV_values', result)
# plot results
sys, results = sesame.load_sim('2dGB_V_0.gzip')
sesame.plot(sys, results['v'])
sys.contact_S(Sn_left, Sp_left, Sn_right, Sp_right)
# Define a function for the generation rate
phi = 1e17 # photon flux [1/(cm^2 s)]
alpha = 2.3e4 # absorption coefficient [1/cm]
# Define a function for the generation rate
def gfcn(x,y):
return phi * alpha * np.exp(-alpha * x)
# add generation to system
sys.generation(gfcn)
# IV curve
voltages = np.linspace(0, 0.95, 10)
j = sesame.IVcurve(sys, voltages, '1dhomo_V')
# convert dimensionless current to dimension-ful current
j = j * sys.scaling.current
# save voltage and current values to dictionary
result = {'v':voltages, 'j':j}
# save data to python data file
np.save('IV_values', result)
# save data to an ascii txt file
np.savetxt('IV_values.txt', (voltages, j))
# save data to a matlab data file
try:
from scipy.io import savemat
