def intrho(self, EFn, EFp):
assert (len(self.dopants) == 1), "Should have one dopant"
#d=self.dopants.values()[0]
# if d["type"]=="donor":
# intrho_maj=\
# -q*kT*(self.Nc*int_fermi_dirac_one_half((mu-self.Ec)/kT)
# +d["conc"]*fermi_dirac_zero(-(mu-self.Ec+d["E"]+np.log(2)*kT)/kT))
# elif d["type"]=="acceptor":
# intrho_maj=\
# -q*kT*(self.Nv*int_fermi_dirac_one_half((self.Ev-mu)/kT)
# +d["conc"]*fermi_dirac_zero(-(self.Ev+d["E"]-mu+np.log(4)*kT)/kT))
#print 'sup: ',intrho_maj[-1], self._intrho_ref, self.Nc*int_fermi_dirac_one_half((mu-self.Ec)/kT)[-1]
# intrho=intrho_maj-self._intrho_ref+self._minorrho*(mu-self.mu_bulk)
eterm = -q * kT * self.Nc * int_fermi_dirac_one_half(
(EFn - self.Ec) / kT)
hterm = -q * kT * self.Nv * int_fermi_dirac_one_half(
(self.Ev - EFp) / kT)
if (len(self.dopants) != 1): return
d = self.dopants.values()[0]
if d["type"] == "donor":
dterm = -q * kT * d["conc"] * fermi_dirac_zero(
-(EFn - self.Ec + d["E"] + np.log(2) * kT) / kT)
elif d["type"] == "acceptor":
dterm = -q * kT * d["conc"] * fermi_dirac_zero(
-(self.Ev + d["E"] - EFp + np.log(4) * kT) / kT)
intrho = eterm + hterm + dterm - self.intrho_ref
return intrho
def _recomputeMuBulk(self):
# If undoped, EBulk=0 (ie intrinsic)
if not len(self.dopants.values()):
self.mu_bulk=0
else:
# Find the net doping
netDope=sum([
v["conc"]*(-1 if v["type"]=="donor" else 1)
for v in self.dopants.values()])
# Use the net doping with complete ionization to get a starting guess (E0) for EBulk
if(not netDope): mu_0=0
else: mu_0=np.sign(-netDope)*kT*np.log(abs(netDope)/self.ni)
# Find EBulk to minimize the net charge in the material
res=sciopt.minimize_scalar(lambda mu:abs(self.rho(mu,mu)),sorted([0,mu_0]),method='Golden')
self.mu_bulk=res.x
eterm=-q*kT*self.Nc*int_fermi_dirac_one_half((self.mu_bulk-self.Ec)/kT)
hterm=-q*kT*self.Nv*int_fermi_dirac_one_half((self.Ev-self.mu_bulk)/kT)
if(len(self.dopants)!=1): return
d=self.dopants.values()[0]
if d["type"]=="donor":
dterm=-q*kT*d["conc"]*fermi_dirac_zero(-(self.mu_bulk-self.Ec+d["E"]+np.log(2)*kT)/kT)
elif d["type"]=="acceptor":
dterm=-q*kT*d["conc"]*fermi_dirac_zero(-(self.Ev+d["E"]-self.mu_bulk+np.log(4)*kT)/kT)
self.intrho_ref=eterm+hterm+dterm
def _recomputeMuBulk(self):
# If undoped, EBulk=0 (ie intrinsic)
if not len(self.dopants.values()):
self.mu_bulk = 0
else:
# Find the net doping
netDope = sum([
v["conc"] * (-1 if v["type"] == "donor" else 1)
for v in self.dopants.values()
])
# Use the net doping with complete ionization to get a starting guess (E0) for EBulk
if (not netDope): mu_0 = 0
else:
mu_0 = np.sign(-netDope) * kT * np.log(abs(netDope) / self.ni)
# Find EBulk to minimize the net charge in the material
res = sciopt.minimize_scalar(lambda mu: abs(self.rho(mu, mu)),
sorted([0, mu_0]),
method='Golden')
self.mu_bulk = res.x
eterm = -q * kT * self.Nc * int_fermi_dirac_one_half(
(self.mu_bulk - self.Ec) / kT)
hterm = -q * kT * self.Nv * int_fermi_dirac_one_half(
(self.Ev - self.mu_bulk) / kT)
if (len(self.dopants) != 1): return
d = self.dopants.values()[0]
if d["type"] == "donor":
dterm = -q * kT * d["conc"] * fermi_dirac_zero(
-(self.mu_bulk - self.Ec + d["E"] + np.log(2) * kT) / kT)
elif d["type"] == "acceptor":
dterm = -q * kT * d["conc"] * fermi_dirac_zero(
-(self.Ev + d["E"] - self.mu_bulk + np.log(4) * kT) / kT)
self.intrho_ref = eterm + hterm + dterm
def intrho(self,EFn,EFp):
assert(len(self.dopants)==1),"Should have one dopant"
#d=self.dopants.values()[0]
# if d["type"]=="donor":
# intrho_maj=\
# -q*kT*(self.Nc*int_fermi_dirac_one_half((mu-self.Ec)/kT)
# +d["conc"]*fermi_dirac_zero(-(mu-self.Ec+d["E"]+np.log(2)*kT)/kT))
# elif d["type"]=="acceptor":
# intrho_maj=\
# -q*kT*(self.Nv*int_fermi_dirac_one_half((self.Ev-mu)/kT)
# +d["conc"]*fermi_dirac_zero(-(self.Ev+d["E"]-mu+np.log(4)*kT)/kT))
#print 'sup: ',intrho_maj[-1], self._intrho_ref, self.Nc*int_fermi_dirac_one_half((mu-self.Ec)/kT)[-1]
# intrho=intrho_maj-self._intrho_ref+self._minorrho*(mu-self.mu_bulk)
eterm=-q*kT*self.Nc*int_fermi_dirac_one_half((EFn-self.Ec)/kT)
hterm=-q*kT*self.Nv*int_fermi_dirac_one_half((self.Ev-EFp)/kT)
if(len(self.dopants)!=1): return
d=self.dopants.values()[0]
if d["type"]=="donor":
dterm=-q*kT*d["conc"]*fermi_dirac_zero(-(EFn-self.Ec+d["E"]+np.log(2)*kT)/kT)
elif d["type"]=="acceptor":
dterm=-q*kT*d["conc"]*fermi_dirac_zero(-(self.Ev+d["E"]-EFp+np.log(4)*kT)/kT)
intrho=eterm+hterm+dterm - self.intrho_ref
return intrho
# if d["type"]=="acceptor":
# self._intrho=\
# q*(self.Nv*int_fermi_dirac_one_half((self.Ev-self.mu_bulk)/kT)
# -d["conc"]*fermi_dirac_zero(-(self.Ec-d["Ed"]-self.mu_bulk+log(2))/kT))
#if __name__ == "__main__":
#
# uno=Material("GaN",{"Mg":8e19})
# print uno.mu_bulk
# dos=Material("GaN",{"Si":2e19})
# print dos.mu_bulk
# print "phib: ",uno.mu_bulk-dos.mu_bulk
#
# print "N"
# tmat=Material("GaN",{"Si":2e19})
# #print tmat.Ec-tmat.mu_bulk
# defi=np.linspace(0,1.5,1000000)
# rhos=tmat.rho(tmat.mu_bulk-defi,tmat.mu_bulk-defi)
# #print rhos[-1]/q
# print (np.cumsum(rhos)*(defi[1]-defi[0]))[-1]/q
# print -tmat.intrho(tmat.mu_bulk-defi,tmat.mu_bulk-defi)[-1]/q
#
# print "P"
# tmat=Material("GaN",{"Mg":8e19})
# print tmat.Ev-tmat.mu_bulk
# defi=-np.linspace(0,1.5,1000000)
# rhos=tmat.rho(tmat.mu_bulk-defi,tmat.mu_bulk-defi)
# print rhos[-1]/q
# print (np.cumsum(rhos)*(defi[1]-defi[0]))[-1]/q
# print -tmat.intrho(tmat.mu_bulk-defi,tmat.mu_bulk-defi)[-1]/q
#
# print "SPACING"
# nds=tmat.Nd_ionized(tmat.mu_bulk-defi)
# print (np.cumsum(nds)*(defi[1]-defi[0]))[-1]
# print 2e19*kT*fermi_dirac_zero(-(tmat.mu_bulk-defi[-1]-tmat.Ec+.015+np.log(2)*kT)/kT)-2e19*kT*fermi_dirac_zero(-(tmat.mu_bulk-tmat.Ec+.015+np.log(2)*kT)/kT)
#
# print "MORE SPACING"
# ns=tmat.n(tmat.mu_bulk-defi)
# ns2=tmat.Nc*fermi_dirac_one_half((tmat.mu_bulk-defi-tmat.Ec)/kT)
# print (np.cumsum(ns)*(defi[1]-defi[0]))[-1]
# print tmat.Nc*kT*(int_fermi_dirac_one_half((tmat.mu_bulk-defi[-1]-tmat.Ec)/kT)-int_fermi_dirac_one_half((tmat.mu_bulk-tmat.Ec)/kT))
#