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test_2d.py
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import devsim as ds
from dg_physics import *
from dg_common import *
import moscap
import numpy
import ramp
import matplotlib.pyplot as plt
import matplotlib
from matplotlib.backends.backend_pdf import PdfPages
import sys
#import moscap2d
def latex_float(f):
float_str = "{0:.2g}".format(f)
if "e" in float_str:
base, exponent = float_str.split("e")
float_str = ''
if base == '-1':
float_str = r"-$10^{{{0}}}$".format(int(exponent))
elif base == '1':
float_str = r"$10^{{{0}}}$".format(int(exponent))
else:
float_str = r"${0} \cdot 10^{{{1}}}$".format(base, int(exponent))
return float_str
def format_doping(f):
s = latex_float(f)
if s[0] == '-':
s = r'N$_A$=' + s[1:]
else:
s = r'N$_D$=' + s
s = s + r' (#/cm$^3$)'
return s
def simulate_charge(device, contact, equation, solver_params):
#charge_factor=1e7 #from F/cm^2 to fF/um^2
dv = 0.001
v1 = ds.get_parameter(device=device, name=GetContactBiasName(contact))
q1 = ds.get_contact_charge(device=device, contact=contact, equation="PotentialEquation")
v2 = v1 + dv
ds.set_parameter(name=GetContactBiasName(contact), value=v2)
ds.solve(**solver_params)
q2 = ds.get_contact_charge(device=device, contact=contact, equation="PotentialEquation")
return (v1, (charge_factor*(q2-q1)/dv))
#assume body nodes have two edges
def debug_plot(device, region):
nodes = numpy.array(get_node_model_values(device=device, region=region, name='node_index'))
node_volume = numpy.array(get_node_model_values(device=device, region=region, name='NodeVolume'))
num_nodes = len(nodes)
flux_term = numpy.zeros(num_nodes)
edge_vol_term = numpy.zeros(num_nodes)
x = numpy.array(get_node_model_values(device=device, region=region, name="x"))*1e7
node_vol_term = numpy.array(get_node_model_values(device=device, region=region, name="volume_term"))
pos_edge = []
neg_edge = []
for i in range(num_nodes):
pos_edge.append([])
neg_edge.append([])
# edge based quantities
edge_from_node_model(device=device, region=region, node_model='node_index')
n0_indexes = get_edge_model_values(device=device, region=region, name='node_index@n0')
n1_indexes = get_edge_model_values(device=device, region=region, name='node_index@n1')
# need to get sign based on index
num_edges = len(n0_indexes)
zero_earray = [0.0]*num_edges
# populate edges
for ei in range(num_edges):
pos_edge[int(n0_indexes[ei])].append(ei)
neg_edge[int(n1_indexes[ei])].append(ei)
ft = get_edge_model_values(device=device, region=region, name="surface_term")
st = get_edge_model_values(device=device, region=region, name="edge_volume_term")
for ni in range(num_nodes):
# don't want a boundary yet
for v in pos_edge[ni]:
flux_term[ni] += ft[v]
edge_vol_term[ni] += st[v]
for v in neg_edge[ni]:
flux_term[ni] -= ft[v]
edge_vol_term[ni] += st[v]
node_vol_term = node_vol_term / node_volume
edge_vol_term = edge_vol_term / node_volume
flux_term = flux_term / node_volume
net_sum = flux_term + edge_vol_term+ node_vol_term
fig=plt.figure()
plt.plot(x, node_vol_term, label="node vol")
plt.plot(x, edge_vol_term, label="edge vol")
plt.plot(x, flux_term, label="flux")
plt.plot(x, net_sum, 'k+-', label="sum")
plt.legend(loc='best')
plt.ylim(-1, 1.0)
plt.xlim(0, 20)
plt.xlabel(xstring)
plt.ylabel('eq terms (eV)')
return fig
def setup_dd_si(device, region):
# this is our solution variable
CreateSolution(device, region, "Potential")
CreateSolution(device, region, "Electrons")
CreateSolution(device, region, "Holes")
#These are needed for the Arora model
CreateBandEdgeModels(device, region, ("Potential","Le","Lh"))
#these are needed for velocity saturation
#CreateEField(device, region)
#CreateDField(device, region)
opts = CreateAroraMobilityLF(device, region)
#opts = CreateHFMobility(device, region, **opts)
CreateSiliconDriftDiffusion(device, region, **opts)
for i in get_contact_list(device=device):
r=get_region_list(device=device, contact=i)[0]
if r == region:
ds.set_parameter(name=GetContactBiasName(i), value=0.0)
CreateSiliconDriftDiffusionContact(device, region, i, opts['Jn'], opts['Jp'])
return opts
def ActivateLe():
ds.set_parameter(device=device, region=region_si, name="Gamman", value=3)
ds.set_parameter(device=device, region=region_ox, name="Gamman", value=1)
#setup_dg_equation(device, region_si, 'Lambda_e', 'Le', '', '', 'Le_eqn')
#setup_dg_equation(device, region_ox, 'Lambda_e', 'Le', '', '', 'Le_eqn')
#setup_dg_equation(device, region_si, 'Lambda_e', 'Le', 'del_log_n1', '', 'Le_eqn')
#setup_dg_equation(device, region_ox, 'Lambda_e', 'Le', 'del_log_n1', '', 'Le_eqn')
setup_dg_equation(device, region_si, 'Lambda_e', 'Le', 'del_log_n1', 'del_log_n2', 'Le_eqn')
setup_dg_equation(device, region_ox, 'Lambda_e', 'Le', 'del_log_n1', 'del_log_n2', 'Le_eqn')
for c in ('top', 'bot'):
setup_dg_contact(device, c, 'Lambda_e', 'Le')
setup_dg_interface(device, interface_siox, 'Lambda_e', 'Le')
def ActivateLh():
ds.set_parameter(device=device, region=region_si, name="Gammap", value=3)
ds.set_parameter(device=device, region=region_ox, name="Gammap", value=1)
#setup_dg_equation(device, region_si, 'Lambda_h', 'Lh', 'del_log_p1', '', 'Lh_eqn')
#setup_dg_equation(device, region_ox, 'Lambda_h', 'Lh', 'del_log_p1', '', 'Lh_eqn')
setup_dg_equation(device, region_si, 'Lambda_h', 'Lh', 'del_log_p1', 'del_log_p2', 'Lh_eqn')
setup_dg_equation(device, region_ox, 'Lambda_h', 'Lh', 'del_log_p1', 'del_log_p2', 'Lh_eqn')
for c in ('top', 'bot'):
setup_dg_contact(device, c, 'Lambda_h', 'Lh')
setup_dg_interface(device, interface_siox, 'Lambda_h', 'Lh')
def ActivateLe2():
ds.set_parameter(device=device, region=region_si, name="Gamman", value=3)
ds.set_parameter(device=device, region=region_ox, name="Gamman", value=1)
setup_dg_equation(device, region_si, 'Lambda_e', 'Le', 'del_log_n1', 'del_log_n2', 'Le_eqn')
setup_dg_contact(device, 'bot', 'Lambda_e', 'Le')
def ActivateLh2():
ds.set_parameter(device=device, region=region_si, name="Gammap", value=3)
ds.set_parameter(device=device, region=region_ox, name="Gammap", value=1)
setup_dg_equation(device, region_si, 'Lambda_h', 'Lh', 'del_log_p1', 'del_log_p2', 'Lh_eqn')
setup_dg_contact(device, 'bot', 'Lambda_h', 'Lh')
def set_default_models():
for r in (region_si, region_ox):
ds.edge_from_node_model(device=device, region=r, node_model="x")
CreateEdgeModel(device=device, region=r, model="xmid", expression="0.5*(x@n0 + x@n1)")
ds.set_parameter(name="T", value=300.0)
CreateSolution(device=device, region=region_si, name="Potential")
CreateSolution(device=device, region=region_ox, name="Potential")
#
# need Le and Lh for the Si Equation
#
setup_dg_variable(device, region_si, "Le")
setup_dg_variable(device, region_si, "Lh")
setup_dg_variable(device, region_ox, "Le")
setup_dg_variable(device, region_ox, "Lh")
CreateNodeModel(device=device, region=region_si, model="NetDoping", expression="Nconstant")
#set the bias
ds.set_parameter(name=GetContactBiasName("bot"), value=0.0)
ds.set_parameter(name=GetContactBiasName("top"), value=0.0)
#available solution reset
ds.node_solution(name='zero', device=device, region=region_ox)
ds.node_solution(name='zero', device=device, region=region_si)
def setup_potential_only():
CreateOxidePotentialOnly(device=device, region=region_ox, update_type="default")
CreateSiliconPotentialOnly(device=device, region=region_si)
CreateSiliconOxideInterface(device=device, interface=interface_siox)
CreateOxideContact(device=device, region=region_ox, contact=contact_ox)
CreateSiliconPotentialOnlyContact(device=device, region=region_si, contact=contact_si)
SetSiliconParameters(device=device, region=region_si)
SetOxideParameters(device=device, region=region_ox)
device="MyDevice"
region_ox="MyOxRegion"
region_si="MySiRegion"
interface_siox="MySiOx"
contact_ox="top"
contact_si="bot"
ds.load_devices(file="moscap2d_devsim.msh")
tox=3
width=1e-7 #nm
carrier_var="Electrons"
charge_factor=1e7/1e-7 #from F/cm^2 to fF/um^2
toxstring = r' t$_{ox}$=%s (nm)' % tox
pdfname='2dresult.pdf'
#try:
# tox = float(sys.argv[1])*1e-7
# carrier_var = sys.argv[2]
# pdfname = sys.argv[3]
#except Exception as e:
# print '''args tox carrier pdfname
#tox: oxide thickness (nm)
#carrier: Electrons or Holes
#pdfname: pdf file name
#'''
# raise
#toxstring = r' t$_{ox}$=%s (nm)' % sys.argv[1]
#xstring = r'distance from interface (nm)'
#ds.set_parameter(device=device, region=region_si, name="debug_level", value="verbose")
set_default_models()
setup_potential_only()
eqfuncs=None
def gamma_ramp_n():
# ramp Gammanox
ramp.rampparam(device=device, region=region_si, param="Gammanox", stop=0.25, step_size=0.1, min_step=0.005, solver_params=dg_solver_params)
#ramp Gamman
ramp.rampparam(device=device, region=region_si, param="Gamman", stop=3.6, step_size=1, min_step=0.05, solver_params=dg_solver_params)
def gamma_ramp_p():
# ramp Gammanox
ramp.rampparam(device=device, region=region_si, param="Gammapox", stop=1, step_size=0.1, min_step=0.05, solver_params=dg_solver_params)
#ramp Gamman
ramp.rampparam(device=device, region=region_si, param="Gammap", stop=3.6, step_size=0.1, min_step=0.05, solver_params=dg_solver_params)
if carrier_var == 'Electrons':
#dopings=['-1e18',]
#dopings=['-1e19']
dopings=['-1e17', '-1e18', '-1e19']
dv=0.001
cstep=0.1
qstep=0.1
vstop=5
vneg=-1.1
eqfuncs=[ActivateLe2]
gamma_ramps=[gamma_ramp_n]
elif carrier_var == 'Holes':
dopings=['1e17', '1e18', '1e19']
dv=0.001
cstep=0.1
qstep=0.1
vstop=-5
vneg=1.1
eqfuncs=[ActivateLh2]
gamma_ramps=[gamma_ramp_p]
classical_data = [None]*len(dopings)
solver_params = {
'type' : "dc",
'relative_error' : 1e-11,
'absolute_error' : 1,
'maximum_iterations' : 50
}
for index, d in enumerate(dopings):
cdict = {
'doping' : format_doping(float(d)),
'q' : [],
'v' : [],
}
ds.set_parameter(device=device, region=region_si, name="Nconstant", value=float(d))
ds.set_node_values(device=device, region=region_si, name="Potential", init_from="zero")
ds.set_node_values(device=device, region=region_ox, name="Potential", init_from="zero")
ds.set_parameter(name=GetContactBiasName("top"), value=vneg)
ds.solve(**solver_params)
def mycb():
res = simulate_charge(device=device, contact="top", equation="PotentialEquation", solver_params=solver_params)
cdict['v'].append(res[0])
cdict['q'].append(res[1])
ramp.rampparam(device="", region="", param=GetContactBiasName("top"), stop=vstop, step_size=cstep, min_step=0.01, solver_params=solver_params, callback=mycb)
cdict['Electrons'] = numpy.array(ds.get_node_model_values(device=device, region=region_si, name="IntrinsicElectrons"))
cdict['Holes'] = numpy.array(ds.get_node_model_values(device=device, region=region_si, name="IntrinsicHoles"))
cdict['Potential'] = numpy.array(ds.get_node_model_values(device=device, region=region_si, name="Potential"))
cdict['q'] = numpy.array(cdict['q'])
cdict['v'] = numpy.array(cdict['v'])
classical_data[index] = cdict
# The new dg physics
#
set_dg_parameters(device, region_si)
set_dg_parameters(device, region_ox)
setup_atox(device, region_si)
setup_dg_si_potential_only(device, region_si)
#setup_dg_ox(device, region_ox)
for eq in eqfuncs:
eq()
quantum_data = [None]*len(dopings)
dg_solver_params = {
'type' : "dc",
'relative_error' : 1e-5,
'absolute_error' : 1,
'maximum_iterations' : 50
}
for index, d in enumerate(dopings):
qdict = {
'doping' : format_doping(float(d)),
'q' : [],
'v' : [],
}
ds.set_parameter(device=device, region=region_si, name="Nconstant", value=float(d))
ds.set_node_values(device=device, region=region_si, name="Potential", init_from="zero")
ds.set_node_values(device=device, region=region_si, name="Le", init_from="zero")
ds.set_node_values(device=device, region=region_si, name="Lh", init_from="zero")
ds.set_node_values(device=device, region=region_ox, name="Potential", init_from="zero")
ds.set_parameter(name=GetContactBiasName("top"), value=0.0)
print(d)
ds.set_parameter(device=device, region=region_si, name="Gamman", value=1.0)
ds.set_parameter(device=device, region=region_si, name="Gammanox", value=0.1)
ds.set_parameter(device=device, region=region_si, name="Gammap", value=0.1)
ds.set_parameter(device=device, region=region_si, name="Gammapox", value=0.1)
ds.solve(**dg_solver_params)
for gr in gamma_ramps:
gr()
# ramp top bias negative
ramp.rampparam(device="", region="", param=GetContactBiasName("top"), stop=vneg, step_size=0.1, min_step=0.01, solver_params=dg_solver_params, callback=None)
def mycb():
res = simulate_charge(device=device, contact="top", equation="PotentialEquation", solver_params=dg_solver_params)
qdict['v'].append(res[0])
qdict['q'].append(res[1])
ramp.rampparam(device="", region="", param=GetContactBiasName("top"), stop=vstop, step_size=qstep, min_step=0.01, solver_params=dg_solver_params, callback=mycb)
qdict['Electrons'] = numpy.array(ds.get_node_model_values(device=device, region=region_si, name="IntrinsicElectrons"))
qdict['Holes'] = numpy.array(ds.get_node_model_values(device=device, region=region_si, name="IntrinsicHoles"))
qdict['Potential'] = numpy.array(ds.get_node_model_values(device=device, region=region_si, name="Potential"))
qdict['Le'] = numpy.array(ds.get_node_model_values(device=device, region=region_si, name="Le"))
qdict['Lh'] = numpy.array(ds.get_node_model_values(device=device, region=region_si, name="Lh"))
qdict['q'] = numpy.array(qdict['q'])
qdict['v'] = numpy.array(qdict['v'])
quantum_data[index] = qdict
ds.write_devices(file='myresult.tec', type='tecplot')
#
##print max(get_node_model_values(device=device, region=region_si, name='n_classical'))
##print max(get_node_model_values(device=device, region=region_si, name='n_quantum'))
#
#if carrier_var == 'Electrons':
# edge_model(device=device, region=region_si, name="surface_term", equation="del_log_n1*EdgeCouple")
# edge_model(device=device, region=region_si, name="edge_volume_term", equation="del_log_n2*EdgeNodeVolume")
# node_model(device=device, region=region_si, name="volume_term", equation="Le_eqn*NodeVolume")
#elif carrier_var == 'Holes':
# edge_model(device=device, region=region_si, name="surface_term", equation="del_log_p1*EdgeCouple")
# edge_model(device=device, region=region_si, name="edge_volume_term", equation="del_log_p2*EdgeNodeVolume")
# node_model(device=device, region=region_si, name="volume_term", equation="Lh_eqn*NodeVolume")
#else:
# raise RuntimeError("Unknown Carrier Type")
#
figs = []
#fig = debug_plot(device=device, region=region_si)
#fig.suptitle(('debugging plot for %s,' + toxstring) % (qdict['doping'],) )
#figs.append(fig)
#
#
###
###plt.plot(
### get_edge_model_values(device=device, region=region_si, name='xmid'),
### get_edge_model_values(device=device, region=region_si, name='surface_term'), 'x',
### label="surf"
###)
####plt.show()
###plt.plot(
### get_edge_model_values(device=device, region=region_si, name='xmid'),
### get_edge_model_values(device=device, region=region_si, name='edge_volume_term'), '+',
### label="vol edge"
###)
###plt.plot(
### get_node_model_values(device=device, region=region_si, name='x'),
### get_node_model_values(device=device, region=region_si, name='volume_term'), 'o',
### label="vol node"
###)
###plt.legend(loc='best')
###plt.show()
###
###
####plt.ylim(0,4e20)
####plt.show()
###plt.plot(
### get_node_model_values(device=device, region=region_si, name='x'),
### get_node_model_values(device=device, region=region_si, name='Le'), '+',
### label="classical"
###)
####plt.plot(
#### get_node_model_values(device=device, region=region_si, name='x'),
#### get_node_model_values(device=device, region=region_si, name='potential_effective'),
#### label="effective potential"
####)
####plt.xlim(0, 7e-7)
####plt.ylim(1e18,1e22)
###plt.show()
###
##x=numpy.array(get_node_model_values(device=device, region=region_si, name='x'))
##for i in range(len(dopings)):
## plt.plot(
## x,
## classical_data[i]['Electrons'],
## label="Classical"
## )
## plt.plot(
## x,
## quantum_data[i]['Electrons'],python_packages.
## label="Density Gradient"
## )
## plt.title(classical_data[i]['doping'])
## plt.xlim(0, 7e-7)
## plt.ylim(0,1e21)
## plt.legend(loc='best')
## plt.show()
##
#x=numpy.array(get_node_model_values(device=device, region=region_si, name='x'))*1e7
#for i in range(len(dopings)):
# fig = plt.figure()
# plt.plot(
# x,
# classical_data[i][carrier_var],
# label="Classical"
# )
# plt.plot(
# x,
# quantum_data[i][carrier_var],
# label="Density Gradient"
# )
# plt.title(('%s' + toxstring) % (classical_data[i]['doping'],))
# plt.xlim(0, 7)
# plt.ylim(0,1e21)
# plt.legend(loc='best')
# plt.xlabel(xstring)
# plt.ylabel('%s (#/cm$^3$)' % (carrier_var,))
# #plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
# y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False, useMathText=True)
# ax = plt.gca()
# ax.yaxis.set_major_formatter(y_formatter)
# figs.append(fig)
##
##
##
colors=('k', 'b', 'g')
fig = plt.figure()
for i in range(len(dopings)):
plt.plot(classical_data[i]['v'], classical_data[i]['q'], '%s--' % colors[i], label='%s' % classical_data[i]['doping'])
plt.plot(quantum_data[i]['v'], quantum_data[i]['q'], '%s-' % colors[i], label='DG')
plt.title('CV Curves' + toxstring)
plt.xlabel(r'V$_g$ (V)')
plt.ylabel(r'C (fF/$\mu$m)')
plt.legend(loc='best')
plt.ylim(0,12)
figs.append(fig)
with PdfPages(pdfname) as pdf:
for f in figs:
pdf.savefig(f)
plt.close(f)