kT = 1
I_0 = np.logspace(-1, 2, 10) * kT
bias = np.linspace(-10 * kT, 10 * kT, 100)
ab_R = np.array([0.25])
current = np.zeros((len(I_0), len(ab_R), len(bias)))
#%% Initialize system
kmc = kmc_dn.kmc_dn(1, 0, xdim, ydim, 0, electrodes=electrodes)
kmc.load_acceptors(acceptors)
tic = time.time()
#%% Simulation loop
for i in range(len(I_0)):
for j in range(len(ab_R)):
# Set constants
kmc.kT = kT
kmc.I_0 = I_0[i]
kmc.ab = ab_R[j] * kmc.R
kmc.initialize(placement=False)
# Simulate
current_sim = kmc_dn_utils.IV(kmc, 0, bias, hops=1000)
current[i, j] = current_sim[:, 0]
print(f'{time.time() - tic}')
np.savez('current_map', current=current, I_0=I_0, ab_R=ab_R, bias=bias)
#%% Visualize
domain = kmc_dn_utils.visualize_basic(kmc)
#plt.figure()
#plt.plot(bias, current[:, 0])
plt.show()
electrodes = np.zeros((2, 4))
electrodes[0] = [0, 0, 0, 10]
electrodes[1] = [xdim, 0, 0, 0]
#%% Initialize simulation object
kmc = kmc_dn.kmc_dn(N, M, xdim, ydim, zdim, mu=mu, electrodes=electrodes)
# Set U_0 = kmc.kT
U_0 = 0.1 * kmc.kT
# Place single atom in middle of domain
acceptors = np.array([[xdim / 2, 0, 0]])
kmc.load_acceptors(acceptors)
# Set ab >> R, such that distance does not matter
kmc.ab = 100000 * kmc.R
kmc.initialize(V=False, dopant_placement=False)
#%% Current simulation with backgate
# Voltages
bias = np.linspace(-100 * U_0, 100 * U_0, num=101)
current = np.zeros((avg, 2, len(bias)))
# IV measurement
for i in range(avg):
current[i] = kmc_dn_ut.IV(kmc, 0, bias, hops=hops)
# Save
np.savez('data', bias=bias, current=current)
kmc = kmc_dn.kmc_dn(1, 0, xdim, ydim, 0, electrodes=electrodes, callback = 'none')
kmc.load_acceptors(acceptors)
kmc.R = R
# Set constants
kmc.kT = kT
kmc.I_0 = I_0
kmc.ab = ab_R*kmc.R
kmc.initialize(placement = False)
#%% Simulate IV curve
V_high = -250
points = 100
prehops = 5000
tol = 1E-3
interval = 5000
hops = 1000
avg = 10
bias = np.linspace(V_high, 0, points)
current = np.zeros((avg, points, 2))
for i in range(avg):
current[i] = kmc_dn_utils.IV(kmc, 0, bias, prehops = prehops, tol = tol, interval = interval)
current_avg = np.mean(current, axis = 0)
#%% Save data
np.savez('data', current = current, bias = bias, kT = kT, I_0 = I_0, ab_R = ab_R)
#%% Visualize
plt.figure()
plt.plot(-bias, current_avg[:, 0])
plt.show()
0,
electrodes=electrodes,
callback='none')
kmc.load_acceptors(acceptors)
kmc.load_donors(donors)
#%% Simulation loop
for i in range(len(I_0)):
for j in range(len(ab_R)):
# Set constants
kmc.kT = kT
kmc.I_0 = I_0[i]
kmc.ab = ab_R[j] * kmc.R
kmc.initialize(dopant_placement=False)
# Simulate
current_sim = kmc_dn_utils.IV(kmc,
0,
bias,
prehops=prehops,
hops=hops)
current[k, i, j] = current_sim[:, 1]
#np.savez('data', current = current, I_0 = I_0, ab_R = ab_R, bias = bias)
#%% Visualize
domain = kmc_dn_utils.visualize_basic(kmc)
plt.figure()
for i in range(current.shape[0]):
plt.plot(bias, current[i, 0, 0])
plt.show()
0,
electrodes=electrodes,
callback='none')
kmc.load_acceptors(acceptors)
kmc.load_donors(donors)
#%% Simulation loop
for i in range(len(I_0)):
for j in range(len(ab_R)):
# Set constants
kmc.kT = kT
kmc.I_0 = I_0[i]
kmc.ab = ab_R[j] * kmc.R
kmc.initialize(placement=False)
# Simulate
current_sim = kmc_dn_utils.IV(kmc,
0,
bias,
tol=1E-3,
prehops=1000,
interval=1000)
current[i, j] = current_sim[:, 1]
np.savez('data', current=current, I_0=I_0, ab_R=ab_R, bias=bias)
#%% Visualize
domain = kmc_dn_utils.visualize_basic(kmc)
plt.figure()
plt.plot(bias, current[0, 0])
plt.show()
P = [0, 1, 0, 1]
Q = [0, 0, 1, 1]
w = [1, 1, 1, 1]
kmc.electrodes[cf.output] = 0
# Set control voltages and electrode 0 to high
for index, control in enumerate(cf.controls):
kmc.electrodes[control, 3] = (1-cf.gene[index])*cf.controlrange[0] \
+ cf.gene[index]*cf.controlrange[1]
kmc.electrodes[cf.P, 3] = cf.inputrange
if cf.allzero:
kmc.electrodes[:, 3] = 0
kmc.update_V()
# Obtain IV curve by sweeping electrode 1
currentlist = np.zeros((cf.avg, 8, cf.n_voltage))
for i in range(cf.avg):
currentlist[i] = kmc_dn_utils.IV(kmc,
cf.sweep_electrode,
cf.voltagelist,
hops=cf.hops,
prehops=cf.prehops)
SaveLib.saveExperiment(saveDirectory, V=cf.voltagelist, I=currentlist)
plt.figure()
plt.plot(cf.voltagelist, currentlist[0, cf.output])
plt.show()
for i in range(4):
for j in range(4):
acceptors[4 * i + j] = [(i + 1) * xdim / 5, (j + 1) * ydim / 5, 0]
electrodes = np.zeros((2, 4))
electrodes[0] = [0, ydim / 2, 0, 10]
electrodes[1] = [xdim, ydim / 2, 0, 0]
kT = 1
bias = np.linspace(-10 * kT, 10 * kT, 100)
I_0 = 1000
ab_R = 0.1
#%% Initialize system
kmc = kmc_dn.kmc_dn(1, 0, xdim, ydim, 0, electrodes=electrodes)
kmc.load_acceptors(acceptors)
tic = time.time()
#%% Simulation loop
# Set constants
kmc.kT = kT
kmc.I_0 = I_0
kmc.ab = ab_R * kmc.R
kmc.initialize(placement=False)
# Simulate
current_sim = kmc_dn_utils.IV(kmc, 0, bias, interval=1000)
current = current_sim[:, 0]
#%% Visualize
plt.figure()
plt.plot(bias, current)
plt.show()
kmc.kT = kT
kmc.I_0 = cf.I_0
kmc.ab = cf.ab_R * kmc.R
# Update constant quantities that depend on I_0 and ab
kmc.calc_E_constant()
kmc.calc_transitions_constant()
# Initialize starting occupation
kmc.place_charges_random()
start_occupation = kmc.occupation.copy()
# Obtain device response
currents = kmc_dn_utils.IV(kmc,
cf.bias_electrode,
voltagelist,
prehops=prehops,
hops=hops,
printETA=True)
output = currents[1].copy()
# Save output and kmc object
outputs[ii] = output
kmc.saveSelf(filepath + '/kmc_objects/' + '{0:03}'.format(ii) + '.kmc')
# Save experiment
SaveLib.saveExperiment(
filepath,
outputs=outputs,
voltagelist=voltagelist,
)
kmc.I_0 = cf.generange[0][0] + genePool.pool[j, 0] \
* (cf.generange[0][1] - cf.generange[0][0])
kmc.ab = cf.generange[1][0] + genePool.pool[j, 1] \
* (cf.generange[1][1] - cf.generange[1][0])
kmc.ab = kmc.ab * kmc.R
# Update constant quantities that depend on I_0 and ab
kmc.calc_E_constant()
kmc.calc_transitions_constant()
fitness_list = []
# Obtain device response
for l in range(cf.avg):
current_array = kmc_dn_utils.IV(kmc,
0,
voltagelist,
prehops=prehops,
hops=hops)
output = current_array[1]
outputArray[i, j] = output
fitness_list.append(cf.Fitness(output, target))
genePool.fitness[j] = min(fitness_list)
fitnessArray[i, j] = genePool.fitness[j]
# Status print
print("Generation nr. " + str(i + 1) + " completed")
print("Highest fitness: " + str(max(genePool.fitness)))
# Evolve to the next generation
genePool.NextGen()