def nxc_from_PC(self, filt=slice(None)):
corr_index = int(self.raw.shape[0] * 0.02)
V_PC_ill = self.data()['Voc'][filt]
V_PC_dark = np.mean(self.raw['Voc'][:corr_index])
#print(V_PC_dark)
del_sigma = self.A * (V_PC_ill - self.C)**2 + self.B * (
V_PC_ill - self.C) - (self.A * (V_PC_dark - self.C)**2 + self.B *
(V_PC_dark - self.C))
# Create an instance of a mobility object
MOB = Mobility()
"""
TEMPERATURE DEPENDENCE - OK
Here the mobility_sum is calculated using the temperature data input directly from the data file name.
"""
def nxc_PC(x):
return del_sigma / (C.e * self.W * MOB.mobility_sum(
Na=self.Na, Nd=self.Nd, temp=self.T, nxc=x))
nxc_guess = del_sigma / (C.e * self.W * MOB.mobility_sum(
Na=self.Na,
Nd=self.Nd,
temp=self.T,
nxc=np.ones_like(del_sigma) * 1e15))
nxc = self.find_iteratively(nxc_guess, nxc_PC)
return nxc
def _conductance2deltan_model(conductance, Na, Nd, thickness,
mobility_sum_author, temp):
'''
returns the excess conductance for a sample
'''
assert type(mobility_sum_author) == str or mobility_sum_author is None
nxc = 1e10 * np.ones(conductance.shape[0])
i = 1
while (i > 0.0001):
mobility_sum = Mobility().mobility_sum(nxc=nxc,
Na=Na,
Nd=Nd,
temp=temp,
author=mobility_sum_author)
_temp = _conductance2deltan_array(conductance, thickness, mobility_sum)
# make sure we are not loosing carriers
_temp[_temp < 0.] = 0.
# ignore points where there are no carriers
index = np.nonzero(nxc)
i = np.average(abs(_temp[index] - nxc[index]) / nxc[index])
nxc = _temp
# return the value
return nxc
def _get_available_models(self):
ni = NI().available_models()
mobility = Mobility().available_models()
ionisation = Ion().available_models()
B = Radiative().available_models()
values = locals()
del values['self']
self.available_models = values
return self.available_models
def nxc_from_PC(self, filt=slice(None)):
corr_index = int(self.raw.shape[0]*0.02)
V_PC_ill = self.data()['Voc'][filt]
V_PC_dark = np.mean(self.raw['Voc'][:corr_index])
#print(V_PC_dark)
del_sigma = self.A*(V_PC_ill-self.C)**2 + self.B*(V_PC_ill-self.C) - (self.A*(V_PC_dark-self.C)**2 + self.B*(V_PC_dark-self.C))
MOB = Mobility()
def nxc_PC(x):
return del_sigma / (C.e * self.W * MOB.mobility_sum(Na=self.Na, Nd=self.Nd, temp=self.T, nxc=x))
nxc_guess = del_sigma / (C.e * self.W * MOB.mobility_sum(Na=self.Na, Nd=self.Nd, temp=self.T, nxc=np.ones_like(del_sigma)*1e15))
nxc = self.find_iteratively(nxc_guess, nxc_PC)
return nxc
def _update_update(self):
'''
creates a dictionary that holds
the required semiconudctor models for easy
calling
'''
self.update = {
'ni':
NI(material=self.material,
author=self.selected_model['ni']).update,
'mobility':
Mobility(material=self.material,
author=self.selected_model['mobility']).mobility_sum,
'ionisation':
Ion(material=self.material,
author=self.selected_model['ionisation']).
update_dopant_ionisation,
'B':
Radiative(
material=self.material,
author=self.selected_model['B'],
)._get_B
}
