def set_dd_parameters(device,
region,
permittivity=11.1,
n_i=1E10,
T=300,
mu_n=400,
mu_p=200,
taun=1E-5,
taup=1E-5):
names = [
'permittivity', 'q', 'n_i', 'T', 'k', 'kT', 'V_t', 'mobility_n',
'mobility_p', 'n1', 'p1', 'taun', 'taup'
]
values = [
permittivity * eps_0, q, n_i, T, k, k * T, k * T / q, mu_n, mu_p, n_i,
n_i, taun, taup
]
for name, value in zip(names, values):
ds.set_parameter(device=device, region=region, name=name, value=value)
# Setup the solutions for the potential, electron and hole densities
pot = 'potential'
electron_density = 'electron_density'
hole_density = 'hole_density'
for var in [pot, electron_density, hole_density]:
create_solution(device=device, region=region, name=var)
# Now for some poisson's equation
# Create some nodemodels
n_ie = 'n_ie', f"n_i*exp(q*{pot}/k*T)" # Intrinsic electron density
n_ih = 'n_ih', f'n_i^2/{n_ie[0]}' # Intrinsic hole density
net_n_i = 'net_n_i', f'kahan4(-{n_ie[0]}, {n_ih[0]}, p_doping, -n_doping)' # Net intrinsic charge
net_n_i_charge = 'net_n_i_charge', f'q*{net_n_i[0]}' #PotentialIntrinsicCharge
for name, eq in [n_ie, n_ih, net_n_i, net_n_i_charge]:
ds.node_model(device=device, region=region, name=name, equation=eq)
create_derivatives(device, region, name, eq, pot)
E_field = 'E_field', f'({pot}@n0-{pot}@n1)*EdgeInverseLength'
D_field = 'D_field', 'E_field * permittivity' #PotentialEdgeFlux ?? wtf
# Initialize the electron and hole densities
for carrier, init in zip([electron_density, hole_density], [n_ie, n_ih]):
ds.set_node_values(device=device,
region=region,
name=carrier,
init_from=init[0])
# setup edge nodes
for edge_name, eq in [E_field, D_field]:
ds.edge_model(device=device,
region=region,
name=edge_name,
equation=eq)
create_derivatives(device, region, edge_name, eq, pot)
# Create PE
poisson_RHS = 'PoissonRHS', f'({hole_density} - {electron_density} + p_doping - n_doping)' #*q/(permittivity)'
# AKA pne
ds.node_model(device=device,
region=region,
name=poisson_RHS[0],
equation=poisson_RHS[1])
create_derivatives(device, region, poisson_RHS[0], poisson_RHS[1],
hole_density, electron_density)
ds.equation(device=device,
region=region,
name="PoissonEquation",
variable_name=pot,
node_model=poisson_RHS[0],
edge_model=D_field[0])
# Use stupid bernouli formalism
# Check if exists?
ds.edge_from_node_model(device=device, region=region, node_model=pot)
beta_vdiff = 'beta_vdiff', f'({pot}@n0 - {pot}@n1)*q/kT'
ds.edge_model(device=device,
region=region,
name=beta_vdiff[0],
equation=beta_vdiff[1])
bernouli = 'bern', f'B({beta_vdiff[0]})'
ds.edge_model(device=device,
region=region,
name=bernouli[0],
equation=bernouli[1])
# Create continuity equations
E_qf_n = (
'quasi_fermi_n',
f'q*({pot}-electron_affinity) + k*T*log({electron_density}/N_cond)')
E_qf_p = (
'quasi_fermi_p',
f'q*({pot}-electron_affinity) - k*T*log({hole_density}/N_val) - band_gap'
)
J_e = 'e_current', f'q*mobility_n*{electron_density}*EdgeInverseLength*({E_qf_n[0]}@n0-{E_qf_n[0]}@n1)'
J_h = 'h_current', f'q*mobility_p*{hole_density}*EdgeInverseLength*({E_qf_p[0]}@n0-{E_qf_p[0]}@n1)'
for J in [E_qf_n, E_qf_p, J_e, J_h]:
ds.edge_model(device=device, region=region, name=J[0], equation=J[1])
ds.node_model(device=device, region=region, name=J[0], equation=J[1])
for node_model in [pot, electron_density, hole_density]:
# Check if exists?!
ds.edge_from_node_model(device=device,
region=region,
node_model=node_model)
create_derivatives(device, region, J[0], J[1], node_model)
for node_model in [n_ie, n_ih, net_n_i, net_n_i_charge]:
ds.print_node_values(device=device, region=region, name=node_model[0])
for edge_model in [E_qf_n, E_qf_p, J_e, J_h]:
ds.print_edge_values(device=device, region=region, name=edge_model[0])
##matplotlib.pyplot.savefig("diode_1d_current.eps")
##print ymin
##print ymax
device = "MyDevice"
region = "MyRegion"
CreateMesh(device=device, region=region)
SetParameters(device=device, region=region)
set_parameter(device=device, region=region, name="taun", value=1e-8)
set_parameter(device=device, region=region, name="taup", value=1e-8)
SetNetDoping(device=device, region=region)
print_node_values(device=device, region=region, name="NetDoping")
InitialSolution(device, region)
# Initial DC solution
solve(type="dc",
absolute_error=1.0,
relative_error=1e-10,
maximum_iterations=30)
DriftDiffusionInitialSolution(device, region)
###
### Drift diffusion simulation at equilibrium
###
solve(type="dc",
absolute_error=1e10,
def setup_drift_diffusion(self,
dielectric_const=11.9,
intrinsic_carriers=1E10,
work_function=4.05,
band_gap=1.124,
Ncond=3E19,
Nval=3E19,
mobility_n=1107,
mobility_p=424.6,
Nacceptors=0,
Ndonors=0):
"""
Sets up equations for a drift-diffusion style of dc current transport.
kwargs here are device-level parameters, imbuing all regions with these properties
If a specific region or material is to have a different set of parameters, they can be set through the
Region constructor.
:param dielectric_const:
:param intrinsic_carriers:
:param work_function:
:param band_gap:
:param Ncond:
:param Nval:
:param mobility_n:
:param mobility_p:
:param Nacceptors:
:param Ndonors:
:return:
"""
# Begin legacy copy-paste job
# Set silicon parameters
device = self.name
region = self.regions[0].name
eps_si = dielectric_const
n_i = 1E10
k = kb
mu_n = mobility_n
mu_p = mobility_p
set_parameter(device=device,
region=region,
name="Permittivity",
value=eps_si * eps_0)
set_parameter(device=device,
region=region,
name="ElectronCharge",
value=q)
set_parameter(device=device, region=region, name="n_i", value=n_i)
set_parameter(device=device, region=region, name="T", value=T)
set_parameter(device=device, region=region, name="kT", value=k * T)
set_parameter(device=device,
region=region,
name="V_t",
value=k * T / q)
set_parameter(device=device, region=region, name="mu_n", value=mu_n)
set_parameter(device=device, region=region, name="mu_p", value=mu_p)
# default SRH parameters
set_parameter(device=device, region=region, name="n1", value=n_i)
set_parameter(device=device, region=region, name="p1", value=n_i)
set_parameter(device=device, region=region, name="taun", value=1e-5)
set_parameter(device=device, region=region, name="taup", value=1e-5)
# CreateNodeModel 3 times
for name, value in [('Acceptors', "1.0e18*step(0.5e-5-x)"),
('Donors', "1.0e18*step(x-0.5e-5)"),
('NetDoping', "Donors-Acceptors")]:
result = node_model(device=device,
region=region,
name=name,
equation=value)
logger.debug(f"NODEMODEL {device} {region} {name} '{result}'")
print_node_values(device=device, region=region, name="NetDoping")
model_name = "Potential"
node_solution(name=model_name, device=device, region=region)
edge_from_node_model(node_model=model_name,
device=device,
region=region)
# Create silicon potentialOnly
if model_name not in get_node_model_list(device=device, region=region):
logger.debug("Creating Node Solution Potential")
node_solution(device=device, region=region, name=model_name)
edge_from_node_model(node_model=model_name,
device=device,
region=region)
# require NetDoping
for name, eq in (
("IntrinsicElectrons", "n_i*exp(Potential/V_t)"),
("IntrinsicHoles", "n_i^2/IntrinsicElectrons"),
("IntrinsicCharge",
"kahan3(IntrinsicHoles, -IntrinsicElectrons, NetDoping)"),
("PotentialIntrinsicCharge", "-ElectronCharge * IntrinsicCharge")):
node_model(device=device, region=region, name=name, equation=eq)
node_model(device=device,
region=region,
name=f"{name}:{model_name}",
equation=f"simplify(diff({eq},{model_name}))")
# CreateNodeModelDerivative(device, region, name, eq, model_name)
### TODO: Edge Average Model
for name, eq in (("ElectricField",
"(Potential@n0-Potential@n1)*EdgeInverseLength"),
("PotentialEdgeFlux",
"Permittivity * ElectricField")):
edge_model(device=device, region=region, name=name, equation=eq)
edge_model(device=device,
region=region,
name=f"{name}:{model_name}@n0",
equation=f"simplify(diff({eq}, {model_name}@n0))")
edge_model(device=device,
region=region,
name=f"{name}:{model_name}@n1",
equation=f"simplify(diff({eq}, {model_name}@n1))")
equation(device=device,
region=region,
name="PotentialEquation",
variable_name=model_name,
node_model="PotentialIntrinsicCharge",
edge_model="PotentialEdgeFlux",
variable_update="log_damp")
# Set up the contacts applying a bias
is_circuit = False
for contact_name in get_contact_list(device=device):
set_parameter(device=device,
name=f"{contact_name}_bias",
value=0.0)
# CreateSiliconPotentialOnlyContact(device, region, contact_name)
# Start
# Means of determining contact charge
# Same for all contacts
if not InNodeModelList(device, region, "contactcharge_node"):
create_node_model(device, region, "contactcharge_node",
"ElectronCharge*IntrinsicCharge")
#### TODO: This is the same as D-Field
if not InEdgeModelList(device, region, "contactcharge_edge"):
CreateEdgeModel(device, region, "contactcharge_edge",
"Permittivity*ElectricField")
create_edge_model_derivatives(device, region,
"contactcharge_edge",
"Permittivity*ElectricField",
"Potential")
# set_parameter(device=device, region=region, name=GetContactBiasName(contact), value=0.0)
contact_bias_name = f"{contact_name}_bias"
contact_model_name = f"{contact_name}nodemodel"
contact_model = f"Potential -{contact_bias_name} + ifelse(NetDoping > 0, -V_t*log({CELEC_MODEL!s}/n_i), V_t*log({CHOLE_MODEL!s}/n_i))"\
CreateContactNodeModel(device, contact_name, contact_model_name,
contact_model)
# Simplify it too complicated
CreateContactNodeModel(
device, contact_name, "{0}:{1}".format(contact_model_name,
"Potential"), "1")
if is_circuit:
CreateContactNodeModel(
device, contact_name,
"{0}:{1}".format(contact_model_name,
contact_bias_name), "-1")
if is_circuit:
contact_equation(device=device,
contact=contact_name,
name="PotentialEquation",
variable_name="Potential",
node_model=contact_model_name,
edge_model="",
node_charge_model="contactcharge_node",
edge_charge_model="contactcharge_edge",
node_current_model="",
edge_current_model="",
circuit_node=contact_bias_name)
else:
contact_equation(device=device,
contact=contact_name,
name="PotentialEquation",
variable_name="Potential",
node_model=contact_model_name,
edge_model="",
node_charge_model="contactcharge_node",
edge_charge_model="contactcharge_edge",
node_current_model="",
edge_current_model="")
# Biggie
# Initial DC solution
solve(type="dc",
absolute_error=1.0,
relative_error=1e-10,
maximum_iterations=30)
drift_diffusion_initial_solution(device, region)
solve(type="dc",
absolute_error=1e10,
relative_error=1e-10,
maximum_iterations=30)