def _init_links(self):
self.Dos = DOS(material=self._cal_dts['material'],
temp=self._cal_dts['temp'],
author=None
)
def _init_links(self):
self.Dos = DOS(material=self._cal_dts['material'],
temp=self._cal_dts['temp'],
author=None
)
class Ionisation(HelperFunctions):
'''
Depending on a dopant level from a band, and the thermal
energy available, a dopant is electrical active (donating or
accepting an electron to the band) or inactive.
'''
_cal_dts = {
'material': 'Si',
'temp': 300,
'author': None,
'ni_author': None
}
author_list = 'ionisation.models'
def __init__(self, **kwargs):
# update any values in cal_dts
# that are passed
self.calculationdetails = kwargs
self._init_links()
# get the address of the authors list
author_file = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
self._cal_dts['material'],
self.author_list)
# get the models ready
self._int_model(author_file)
# initiate the first model
self.change_model(self._cal_dts['author'])
def _init_links(self):
self.Dos = DOS(material=self._cal_dts['material'],
temp=self._cal_dts['temp'],
author=None
)
def update(self, N_imp, ne, nh, impurity, **kwargs):
'''
Calculates the number of ionisied impurities
Inputs:
N_imp: (float or numpy array)
number of the impurity in the material
ne: (float or numpy array)
number of electrons
nh: (float or numpy array)
number of holes
impurity: (str)
the element name of the impurity
temp: (optional)
the temperature in Kelvin to be evaluated
author: (optional str)
the author of the impurity model to use
output:
the number of ionised impurities
'''
self.calculationdetails = kwargs
# a check to make sure the model hasn't changed
if 'author' in kwargs.keys():
self.change_model(self._cal_dts['author'])
# checks if and get the required density of states model
if 'dos_author' in self.vals.keys():
Nc, Nv = self.Dos.update(material=self._cal_dts['material'],
temp=self._cal_dts['temp'],
author=self.vals['dos_author']
)
else:
Nc, Nv = 0, 0
if impurity in self.vals.keys():
# get the ionisation fraction
iN_imp = getattr(IIm, self.model)(
self.vals, N_imp, ne, nh,
self._cal_dts['temp'], Nc, Nv,
self.vals[impurity])
# multiply it by the number of dopants
iN_imp *= N_imp
else:
print('''\nWarning:\n\t'''
'''No such impurity, please check your model'''
'''and spelling.\n\tReturning zero array\n''')
iN_imp = np.zeros(np.asarray(N_imp).flatten().shape[0])
return iN_imp
def update_dopant_ionisation(self, N_dop, nxc, impurity, **kwargs):
'''
This is a special function used to determine the number of
ionised dopants given a number of excess carriers, and a
single dopant type.
inputs:
N_dop: (float; cm^-3)
The dopant density
nxc: (float; cm^-3)
The excess carrier density
impurity: (str)
The name of the dopant used e.g. boron, phosphorous. The
dopants available depend on the model used
output:
N_idop: (float cm^-2)
The number of ionised dopants
'''
self.calculationdetails = kwargs
# a check to make sure the model hasn't changed
if 'author' in kwargs.keys():
self.change_model(self._cal_dts['author'])
N_idop = N_dop
if not isinstance(N_idop, np.ndarray):
N_idop = np.asarray([N_idop])
if not isinstance(N_dop, np.ndarray):
N_dop = np.asarray([N_dop])
if impurity in self.vals.keys():
# TO DO, change this from just running 10 times to a proper check
for i in range(10):
if self.vals['tpe_' + self.vals[impurity]] == 'donor':
Nd = np.copy(N_idop)
Na = np.zeros(Nd.shape)
elif self.vals['tpe_' + self.vals[impurity]] == 'acceptor':
Na = np.copy(N_idop)
Nd = np.zeros(Na.shape)
else:
print('something went wrong in ionisation model')
ne, nh = CF.get_carriers(
Na,
Nd,
nxc,
temp=self._cal_dts['temp'],
material=self._cal_dts['material'],
ni=self._cal_dts['ni_author'])
N_idop = self.update(
N_dop, ne, nh, impurity)
else:
print(r'Not a valid impurity, returning 100% ionisation')
return N_idop
def check_models(self):
'''
Plots a check of the modeled data against Digitised data from either
papers or from other implementations of the model.
'''
plt.figure('Ionised impurities')
iN_imp = N_imp = np.logspace(15, 20)
dn = 1e10
temp = 300
for impurity in ['phosphorous', 'arsenic']:
iN_imp = self.update_dopant_ionisation(N_imp,
dn,
impurity,
temp, author=None)
if not np.all(iN_imp == 0):
plt.plot(
N_imp, iN_imp / N_imp * 100, label='Altermatt: ' + impurity)
test_file = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
'Si', 'check data', 'donors.csv')
data = np.genfromtxt(test_file, delimiter=',', skip_header=1)
for i in range(0, (data.shape[1] + 2) / 2, 2):
# print i
plt.plot(
data[:, i], data[:, i + 1] * 100, 'r.',
label='Digitised data')
plt.semilogx()
plt.xlabel('Impurity (cm$^{-3}$)')
plt.ylabel('Fraction of Ionised impurities (%)')
plt.legend(loc=0)
class Ionisation(BaseModelClass):
'''
Depending on a dopant level from a band, and the thermal
energy available, a dopant is electrical active (donating or
accepting an electron to the band) or inactive.
It can take the inputs:
input
material: (string)
The elemental symbol for the material e.g 'Si'.
temp: (float)
The temperature of the sample in kelvin. This can not be an array.
author: (string)
The author of the ionisation model being used
ni_author: (string)
The author of the intrinsic carrier concentraion being used.
'''
_cal_dts = {
'material': 'Si',
'temp': 300,
'author': None,
'ni_author': None,
'N_imp': np.array([1e16]),
'ne': 1e16,
'nh': 0,
'impurity': 'boron',
}
author_list = 'ionisation.yaml'
def __init__(self, **kwargs):
# update any values in cal_dts
# that are passed
self.calculationdetails = kwargs
self._init_links()
# get the address of the authors list
author_file = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
self._cal_dts['material'],
self.author_list)
# get the models ready
self._int_model(author_file)
# initiate the first model
self.change_model(self._cal_dts['author'])
def _init_links(self):
self.Dos = DOS(material=self._cal_dts['material'],
temp=self._cal_dts['temp'],
author=None
)
def update(self, **kwargs):
'''
Calculates the number of ionisied impurities
Inputs:
N_imp: (float or numpy array)
number of the impurity in the material
ne: (float or numpy array)
number of electrons
nh: (float or numpy array)
number of holes
impurity: (str)
the element name of the impurity
temp: (optional)
the temperature in Kelvin to be evaluated
author: (optional str)
the author of the impurity model to use
output:
the number of ionised impurities
'''
self.calculationdetails = kwargs
# a check to make sure the model hasn't changed
if 'author' in kwargs.keys():
self.change_model(self._cal_dts['author'])
# checks if and get the required density of states model
if 'dos_author' in self.vals.keys():
Nc, Nv = self.Dos.update(material=self._cal_dts['material'],
temp=self._cal_dts['temp'],
author=self.vals['dos_author']
)
else:
Nc, Nv = 0, 0
if self._cal_dts['impurity'] in self.vals.keys():
# get the ionisation fraction
iN_imp = getattr(IIm, self.model)(
self.vals, self._cal_dts['N_imp'], self._cal_dts[
'ne'], self._cal_dts['nh'],
self._cal_dts['temp'], Nc, Nv,
self.vals[self._cal_dts['impurity']])
# multiply it by the number of dopants
iN_imp *= self._cal_dts['N_imp']
else:
print('''\nWarning:\n\t'''
'''No such impurity, please check your model'''
'''and spelling.\n\tReturning zero array\n''')
iN_imp = np.zeros(np.asarray(
self._cal_dts['N_imp']).flatten().shape[0])
return iN_imp
def update_dopant_ionisation(self, N_dop, nxc, impurity, **kwargs):
'''
This is a special function used to determine the number of
ionised dopants given a number of excess carriers, and a
single dopant type.
inputs:
N_dop: (float; cm^-3)
The dopant density
nxc: (float; cm^-3)
The excess carrier density
impurity: (str)
The name of the dopant used e.g. boron, phosphorous. The
dopants available depend on the model used
output:
N_idop: (float cm^-2)
The number of ionised dopants
'''
self.calculationdetails = kwargs
# a check to make sure the model hasn't changed
if 'author' in kwargs.keys():
self.change_model(self._cal_dts['author'])
if not isinstance(N_dop, np.ndarray):
N_dop = np.asarray([N_dop])
N_idop = np.copy(N_dop)
if impurity in self.vals.keys():
# TO DO, change this from just running 10 times to a proper check
for i in range(10):
if self.vals['tpe_' + self.vals[impurity]] == 'donor':
Nd = np.copy(N_idop)
Na = np.zeros(Nd.shape)
elif self.vals['tpe_' + self.vals[impurity]] == 'acceptor':
Na = np.copy(N_idop)
Nd = np.zeros(Na.shape)
else:
print('something went wrong in ionisation model')
ne, nh = CF.get_carriers(
Na,
Nd,
nxc,
temp=self._cal_dts['temp'],
material=self._cal_dts['material'],
ni=self._cal_dts['ni_author'])
N_idop = self.update(
N_imp=N_dop, ne=ne, nh=nh, impurity=impurity)
else:
print(r'Not a valid impurity, returning 100% ionisation')
return N_idop
def check_models(self):
'''
Plots a check of the modeled data against Digitised data from either
papers or from other implementations of the model.
'''
plt.figure('Ionised impurities')
iN_imp = N_imp = np.logspace(15, 20)
dn = 1e10
temp = 300
for impurity in ['phosphorous', 'arsenic']:
iN_imp = self.update_dopant_ionisation(N_imp,
dn,
impurity,
temp, author=None)
if not np.all(iN_imp == 0):
plt.plot(
N_imp, iN_imp / N_imp * 100, label='Altermatt: ' + impurity)
test_file = os.path.join(
os.path.dirname(os.path.realpath(__file__)),
'Si', 'check data', 'donors.csv')
data = np.genfromtxt(test_file, delimiter=',', skip_header=1)
for i in range(0, (data.shape[1] + 2) / 2, 2):
# print i
plt.plot(
data[:, i], data[:, i + 1] * 100, 'r.',
label='Digitised data')
plt.semilogx()
plt.xlabel('Impurity (cm$^{-3}$)')
plt.ylabel('Fraction of Ionised impurities (%)')
plt.legend(loc=0)
