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FET_calculation_class.py
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import os
import matplotlib as mpl
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
class FETs_calculation(object):
def __init__(self, measurement_type, columns, rows, filepath_kernel,
chip_name, measurement, num_sd_bias):
self.measurement_type = measurement_type
self.columns = columns
self.rows = rows
self.filepath_kernel = filepath_kernel + r'\\' + chip_name + r'\\' + measurement
self.chip_name = chip_name
self.measurement = measurement
self.num_sd_bias = num_sd_bias
self.status_array = np.full((self.rows, self.columns), float('NaN'))
self.patches = [mpl.patches.Patch(color='gray', label='Non-conductive'),
mpl.patches.Patch(color='black', label='Gate leakage'),
mpl.patches.Patch(color='rosybrown', label='No data')]
self.status_cmap = mpl.colors.ListedColormap(['gray', 'black', 'rosybrown'])
def params_calc(self):
# инициализируем массивы параметров
self.on_off_ratio = np.full([self.rows, self.columns, self.num_sd_bias, 4], float('NaN'))
self.on_state_resistance = np.full([self.rows, self.columns, self.num_sd_bias, 4], float('NaN'))
self.off_state_resistance = np.full([self.rows, self.columns, self.num_sd_bias, 2], float('NaN'))
self.max_currents = np.zeros((self.rows, self.columns, self.num_sd_bias, 4))
self.min_currents = np.zeros((self.rows, self.columns, self.num_sd_bias, 2))
self.branches_names = ['p-type forward', 'n-type forward', 'p-type backward', 'n-type backward']
self.sd_biases = np.zeros((self.num_sd_bias))
# в этом цикле считаются параметры
for i in range(self.rows):
for j in range(self.columns):
try:
df = pd.read_csv(self.filepath_kernel + r'\\data\\fet_{}_{}.csv'.format(i + 1, j + 1),
engine='python',
sep=',')
if (df.values > 1e100).any(): # Данные говно
self.on_off_ratio[i, j, :, :] = float('NaN')
self.on_state_resistance[i, j, :, :] = float('NaN')
self.off_state_resistance[i, j, :, :] = float('NaN')
self.status_array[i, j] = 3
continue
if np.amax(np.abs(df.values[:, 2])) < 10 ** (-9): # условие для непроводящих
self.on_off_ratio[i, j, :, :] = float('NaN')
self.on_state_resistance[i, j, :, :] = float('NaN')
self.off_state_resistance[i, j, :, :] = float('NaN')
self.status_array[i, j] = 1
continue
if np.amax(np.abs(df.values[:, 3])) > 1e-9: # условие для пробитого гейта
self.on_off_ratio[i, j, :, :] = float('NaN')
self.on_state_resistance[i, j, :, :] = float('NaN')
self.off_state_resistance[i, j, :, :] = float('NaN')
self.status_array[i, j] = 2
continue
# эти переменные содержат информацию о числе измерений, количетсве циклов и т.п.
df.columns = np.arange(0, df.shape[1], 1)
m1 = int(np.rint(df.shape[0] / self.num_sd_bias))
n1 = df.shape[1]
number_of_cycles = int((n1 - 2) / 2)
max_forward_p = int(0)
max_backward_p = int(m1-1)
max_forward_n = int((m1-2)/2)
max_backward_n = int((m1-2)/2 + 1)
self.sd_biases = np.array(df[0].unique())
gate_current = np.zeros((m1 * self.num_sd_bias))
gate_current[:] = np.sum(np.array(df)[:, 3::2], axis = 1) / number_of_cycles
# в этом цикле усреднются токи по циклам для каждого байеса и достаются максимальные токи
for k in range(self.num_sd_bias):
single_transistor_currents = np.zeros((m1))
single_transistor_currents[:] = np.sum(np.array(df)[k*m1:(k+1)*m1, 2::2], axis = 1)
single_transistor_currents[:] /= number_of_cycles
single_transistor_currents[abs(single_transistor_currents) <= 1e-12] = 1e-12 * np.sign(self.sd_biases[k])
if df.iloc[0, 0] < 0 or df.iloc[int(m1/2), 0] > 0:
self.max_currents[i, j, k, :] = single_transistor_currents[[max_forward_p,
max_forward_n,
max_backward_p,
max_backward_n]]
else:
self.max_currents[i, j, k, :] = single_transistor_currents[[max_forward_n,
max_forward_p,
max_backward_n,
max_backward_p]]
# if abs(self.max_currents[i, j, k, 0]) < abs(self.max_currents[i, j, k, 1]):
#
# u = self.max_currents[i, j, k, 0]
# self.max_currents[i, j, k, 0] = self.max_currents[i, j, k, 1]
# self.max_currents[i, j, k, 1] = u
#
# u = self.max_currents[i, j, k, 2]
# self.max_currents[i, j, k, 2] = self.max_currents[i, j, k, 3]
# self.max_currents[i, j, k, 3] = u
# это минимальные токи
min_forward = np.amin(abs(single_transistor_currents[:max_forward_n]))
min_backward = np.amin(abs(single_transistor_currents[max_backward_n:]))
# min_forward = abs(single_transistor_currents[(df.iloc[:, 1][df.iloc[:, 1] == 0]).index[0]])
# min_backward = abs(single_transistor_currents[(df.iloc[:, 1][df.iloc[:, 1] == 0]).index[0]])
self.min_currents[i, j, k, :] = [min_forward * np.sign(self.sd_biases[k]),
min_backward * np.sign(self.sd_biases[k])]
# здесь задаются on/off, Ron и Roff
self.on_off_ratio[i, j, k, :] = self.max_currents[i, j, k, :] / [self.min_currents[i, j, k, 0],
self.min_currents[i, j, k, 0],
self.min_currents[i, j, k, 1],
self.min_currents[i, j, k, 1]]
self.on_state_resistance[i, j, k, :] = abs(self.sd_biases[k] / self.max_currents[i, j, k, :])
self.off_state_resistance[i, j, k, :] = abs(self.sd_biases[k] / self.min_currents[i, j, k, :])
except FileNotFoundError:
print('Data for fet_{}_{} not found!'.format(i+1, j+1))
self.on_off_ratio[i, j, :, :] = float('NaN')
self.on_state_resistance[i, j, :, :] = float('NaN')
self.off_state_resistance[i, j, :, :] = float('NaN')
self.status_array[i, j] = 3
continue
#
# for i in range(self.rows):
#
# if i%2 == 1:
#
# self.on_off_ratio[i, :, :, :] = self.on_off_ratio[i, ::-1, :, :]
# self.on_state_resistance[i, :, :, :] = self.on_state_resistance[i, ::-1, :, :]
# self.off_state_resistance[i, :, :, :] = self.off_state_resistance[i, ::-1, :, :]
# self.status_array[i, :] = self.status_array[i, ::-1]
def heatmaps(self):
os.makedirs(self.filepath_kernel + r'\Onoff heatmaps', exist_ok=True)
os.makedirs(self.filepath_kernel + r'\Ropen and Rclosed heatmaps', exist_ok=True)
direction = ['forward', 'backward']
for i in range(4):
if np.mean(self.on_off_ratio[:, :, :, i][~np.isnan(self.on_off_ratio[:, :, :, i])]) > 2:
for k in range(0, self.num_sd_bias):
fig, ax = plt.subplots(figsize = (10, 10))
sns.heatmap(self.status_array, linewidth = .3, ax = ax,
cmap = self.status_cmap, vmin = 1, vmax = 3, cbar = False,
xticklabels = np.arange(1, self.status_array.shape[1] + 1, 1),
yticklabels = np.arange(1, self.status_array.shape[0] + 1, 1))
sns.heatmap(self.on_off_ratio[:, :, k, i], linewidth = .5, ax = ax,
vmin = 1, vmax = 10 ** 6, cbar=True, annot = True, fmt = '.1g',
norm = mpl.colors.LogNorm(), cmap = 'rainbow',
cbar_kws = {'ticks': [1e0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6]},
xticklabels=np.arange(1, self.status_array.shape[1] + 1, 1),
yticklabels=np.arange(1, self.status_array.shape[0] + 1, 1))
plt.title(self.chip_name + '\n' + self.measurement + '\n'
+ '$I_{ON}/I_{OFF}$' + ', bias = {} V'.format(self.sd_biases[k])
+ '\n' + '{}'.format(self.branches_names[i]))
fig.legend(handles = self.patches, loc = 8, ncol = 3, fontsize = 'small')
plt.savefig((self.filepath_kernel + r'\Onoff heatmaps' + r'\On off ratio {} V {}.png'.format(
self.sd_biases[k], self.branches_names[i])),
dpi = 300, bbox_inches = 'tight', format = 'png')
plt.clf()
plt.close(fig)
fig, ax = plt.subplots(figsize=(10, 10))
sns.heatmap(self.status_array, linewidth=.3, ax=ax,
cmap=self.status_cmap, vmin=1, vmax=3, cbar=False,
xticklabels=np.arange(1, self.status_array.shape[1] + 1, 1),
yticklabels=np.arange(1, self.status_array.shape[0] + 1, 1))
sns.heatmap(self.on_state_resistance[:, :, k, i], linewidth=.5, ax=ax,
vmin=1e3, vmax=1e8, cbar=True, annot = True, fmt = '.1g',
norm=mpl.colors.LogNorm(), cmap='rainbow',
cbar_kws={'ticks': [1e3, 1e4, 1e5, 1e6, 1e7, 1e8]},
xticklabels=np.arange(1, self.status_array.shape[1] + 1, 1),
yticklabels=np.arange(1, self.status_array.shape[0] + 1, 1))
plt.title(
self.chip_name + '\n' + self.measurement + '\n' + '$R_{ON}$,' + ' bias = {} V'.format(self.sd_biases[k])
+ '\n' + '{}'.format(self.branches_names[i]))
fig.legend(handles=self.patches, loc=8, ncol=3, fontsize='small')
plt.savefig((self.filepath_kernel + r'\Ropen and Rclosed heatmaps' + r'\Ropen {} V {}.png'.format(
self.sd_biases[k], self.branches_names[i])),
dpi=300, bbox_inches='tight', format='png')
plt.clf()
plt.close(fig)
def plot_graphs(self):
if self.measurement_type == 'Isd-Vg':
os.makedirs(self.filepath_kernel + r'\Transfer curves log scale', exist_ok=True)
os.makedirs(self.filepath_kernel + r'\Transfer curves', exist_ok=True)
else:
os.makedirs(self.filepath_kernel + r'\IV log scale', exist_ok=True)
os.makedirs(self.filepath_kernel + r'\IV', exist_ok=True)
locmax = mpl.ticker.SymmetricalLogLocator(base=10.0, linthresh=1e-13, subs=(0.1, 1, 10, 100,))
locmin = mpl.ticker.SymmetricalLogLocator(base=10.0, linthresh=1e-13, subs=(0.2, 0.4, 0.6, 0.8, 1,))
for i in range(self.rows):
for j in range(self.columns):
if np.isnan(self.status_array[i,j]) == True:
maxt = np.zeros((self.num_sd_bias))
mint = np.zeros((self.num_sd_bias))
df = pd.read_csv(self.filepath_kernel + r'\\data\\fet_{}_{}.csv'.format(i + 1, j + 1),
engine='python',
sep=',')
df.columns = np.arange(0, df.shape[1], 1)
m1 = int(np.rint(df.shape[0] / self.num_sd_bias))
n1 = df.shape[1]
number_of_cycles = int((n1 - 2) / 2)
fig, ax = plt.subplots(figsize=(6, 6))
for k in range(self.num_sd_bias):
curr = np.zeros((m1))
curr[:] = np.sum(np.array(df)[k * m1:(k + 1) * m1, 2::2], axis=1)
curr[:] /= number_of_cycles
plt.plot(df[1].iloc[0:m1], curr*np.sign(self.sd_biases[k]),
label='{} V'.format(df[0].iloc[k*m1]))
maxt[k] = max(curr*np.sign(self.sd_biases[k]))
mint[k] = min(curr*np.sign(self.sd_biases[k]))
plt.title(self.chip_name + '\n' + self.measurement + '\n' + 'FET {}_{}'.format(i + 1, j + 1))
plt.legend(loc=0)
plt.yscale('symlog', linthreshy=10 ** (-13))
if self.measurement_type == 'Isd-Vg':
plt.xlabel('$V_{G}$, V', fontsize=14)
else:
plt.xlabel('$V_{SD}$, V', fontsize=14)
plt.ylabel('$I_{SD}$, A', fontsize=14)
ax.yaxis.set_major_locator(locmax)
ax.yaxis.set_minor_locator(locmin)
ax.yaxis.set_minor_formatter(mpl.ticker.NullFormatter())
ticks = np.array(list(ax.axes.get_yticks()))
ticks = ticks[(abs(ticks) > 5 * 1e-14) | (ticks == 0)]
ticks = ticks[ticks < max(maxt)]
ticks = ticks[ticks > min(mint)]
ax.axes.set_yticks(ticks)
if self.measurement_type == 'Isd-Vg':
plt.savefig((self.filepath_kernel
+ r'\Transfer curves log scale\fet_{}_{}.png').format(i + 1, j + 1),
format='png',
dpi=150,
bbox_inches='tight')
else:
plt.savefig(
(self.filepath_kernel + r'\IV log scale\fet_{}_{}.png').format(i + 1, j + 1),
format='png',
dpi=150,
bbox_inches='tight')
plt.clf()
plt.close(fig)
fig, ax = plt.subplots(figsize=(6, 6))
for k in range(self.num_sd_bias):
curr = np.zeros((m1))
curr[:] = np.sum(np.array(df)[k * m1:(k + 1) * m1, 2::2], axis=1)
curr[:] /= number_of_cycles
plt.plot(df[1].iloc[0:m1], curr * np.sign(self.sd_biases[k]),
label='{} V'.format(df[0].iloc[k * m1]))
plt.title(self.chip_name + '\n' + self.measurement + '\n' + 'FET {}_{}'.format(i + 1, j + 1))
plt.legend(loc=0)
if self.measurement_type == 'Isd-Vg':
plt.xlabel('$V_{G}$, V', fontsize=14)
else:
plt.xlabel('$V_{SD}$, V', fontsize=14)
plt.ylabel('$I_{SD}$, A', fontsize=14)
plt.ticklabel_format(axis='y', style='sci', scilimits=(0, 0))
# plt.grid(b=True, which='major', axis='both')
if self.measurement_type == 'Isd-Vg':
plt.savefig((self.filepath_kernel + r'\Transfer curves\fet_{}_{}.png').format(i + 1, j + 1),
format='png',
dpi=150,
bbox_inches='tight')
else:
plt.savefig((self.filepath_kernel + r'\IV\fet_{}_{}.png').format(i + 1, j + 1),
format='png',
dpi=150,
bbox_inches='tight')
plt.clf()
plt.close(fig)
def mobility_calc(self, length_array, width, C, plot_graphs = True, plot_heatmaps = True):
os.makedirs(self.filepath_kernel + r'\Mobility graphs', exist_ok=True)
os.makedirs(self.filepath_kernel + r'\Mobility heatmaps', exist_ok=True)
self.max_mobility = np.full((self.rows, self.columns, self.num_sd_bias), float('NaN'))
self.length_array = length_array
self.width = width
for i in range(self.rows):
for j in range(self.columns):
if np.isnan(self.status_array[i,j]) == True:
df = pd.read_csv(self.filepath_kernel + r'\\data\\fet_{}_{}.csv'.format(i + 1, j + 1),
engine='python',
sep=',')
df.columns = np.arange(0, df.shape[1], 1)
m1 = int(np.rint(df.shape[0] / self.num_sd_bias))
n1 = df.shape[1]
number_of_cycles = int((n1 - 2) / 2)
max_forward_p = int(0)
max_backward_p = int(m1 - 1)
max_forward_n = int((m1 - 2) / 2)
max_backward_n = int((m1 - 2) / 2 + 1)
self.sd_biases = np.array(df[0].unique())
curr = np.zeros((m1, self.num_sd_bias))
curr_diff = np.zeros((m1 - 2, self.num_sd_bias))
for k in range(self.num_sd_bias):
curr[:, k] = np.sum(np.array(df)[k*m1:(k+1)*m1, 2::2], axis = 1)
curr[:, k] /= number_of_cycles
curr_diff[:, k] = -np.diff(np.delete(curr[:, k], max_backward_n), n=1) /\
np.diff(np.delete(np.array(df[1].iloc[k*m1:(k+1)*m1]), max_backward_n), n=1)
curr_diff[:, k] = self.length_array[i, j] / self.width / C * curr_diff[:, k] / self.sd_biases[k]
self.max_mobility[i, j, k] = np.max(curr_diff)
if self.status_array[i, j] >= 1:
self.max_mobility[i, j, :] = float('NaN')
if plot_graphs:
fig, ax = plt.subplots(figsize=(6, 6))
for k in range(self.num_sd_bias):
plt.plot(np.delete(np.array(df[1].iloc[k*m1:(k+1)*m1]), [max_forward_p, max_backward_n]),
curr_diff[:, k],
label='{} V'.format(self.sd_biases[k]))
plt.title(self.chip_name+ '\n' + self.measurement + '\n' + '$\u03bc_{FE}$,' + '$cm^{2}/(V*s)$'
+ '\n' + 'FET {}_{}'.format(i + 1, j + 1))
# '$\u03bc_{FE}$,' + '$cm^{2}/(V*s)$'
plt.legend(loc=0)
plt.xlabel('$V_{G}$, V', fontsize=14)
plt.ylabel('$\u03bc_{FE}$,' + '$cm^{2}/(V*s)$', fontsize=14)
# plt.grid(b=True, axis='both', which='major')
plt.savefig((self.filepath_kernel + r'\Mobility graphs\fet_{}_{}.png').format(i + 1, j + 1),
format='png',
dpi=150,
bbox_inches='tight')
plt.clf()
plt.close(fig)
if plot_heatmaps:
for k in range(0, self.num_sd_bias):
fig, ax = plt.subplots(figsize=(8, 8))
df = pd.DataFrame(self.status_array)
df.columns = range(1, self.columns + 1)
df.index = range(1, self.rows + 1)
sns.heatmap(df, linewidth=.3, ax=ax, cmap = self.status_cmap, vmin=1, vmax=3, cbar=False)
df = pd.DataFrame(self.max_mobility[:, :, k])
df.columns = range(1, self.columns + 1)
df.index = range(1, self.rows + 1)
sns.heatmap(df, linewidth=.3, vmin=1e0, annot = True, fmt = '.1g',
vmax=1e3, ax=ax, cbar=True, norm=mpl.colors.LogNorm(), cmap='rainbow',
cbar_kws={'ticks': [1e1, 1e2, 1e3]})
plt.title(self.chip_name + '\n' + self.measurement + '\n' + 'Max mobility'
+ '\n' + 'Bias = {} V'.format(self.sd_biases[k]))
fig.legend(handles=self.patches, loc=8, ncol=3, fontsize='small')
plt.savefig((self.filepath_kernel + r'\Mobility heatmaps'
+ r'\Max mobility {} V.png'.format(self.sd_biases[k])),
format='png', dpi=300, bbox_inches='tight')
plt.clf()
plt.close(fig)
def lch_dependence(self):
sns.set(style="ticks", rc={"lines.linewidth": 1})
if np.isnan(self.status_array).any() == True:
for z in range(self.num_sd_bias):
fig, ax = plt.subplots(figsize=(6, 6))
df = pd.DataFrame(np.transpose([np.ndarray.flatten((self.on_state_resistance[:, :, z, 0])),
np.ndarray.flatten((self.off_state_resistance[:, :, z, 0])),
np.ndarray.flatten((self.on_off_ratio[:, :, z, 0])),
np.ndarray.flatten(self.max_mobility[:, :, z]),
np.rint(np.ndarray.flatten(self.length_array[:, :] * 1e4))]))
df.dropna(inplace = True, how = 'any')
df.columns = ('R_on', 'R_off', 'On/off', 'Max_mob', 'Lch')
if (self.on_off_ratio == float('nan')).all() == False:
# sns.scatterplot(x='R_open', y='R_closed', hue = 'Lch', data=df)
# data['R open'] = data['R open']/data['Lch']
# data['R closed'] = data['R closed']/data['Lch']
g = sns.pairplot(df, hue='Lch', diag_kind='hist', palette='rainbow',
vars=['R_on', 'R_off', 'On/off', 'Max_mob'],
plot_kws={'size': 15, 'alpha': 0.85}, diag_kws={'alpha': 0.4, 'log': True,
'bins': np.logspace(np.log10(1e-3),
np.log10(1e12),
80)})
k = ['R_on', 'R_off', 'On/off', 'Max_mob']
for i in range(0, g.axes.shape[0]):
for j in range(0, g.axes.shape[1]):
if i != j:
g.axes[i, j].set_xscale('log')
g.axes[i, j].set_yscale('log')
g.axes[i, j].set_xlim([10 ** np.floor(np.log10(np.min(df[k[j]]))),
10 ** np.ceil(np.log10(np.max(df[k[j]])))])
g.axes[i, j].set_ylim([10 ** np.floor(np.log10(np.min(df[k[i]]))),
10 ** np.ceil(np.log10(np.max(df[k[i]])))])
else:
g.axes[i, j].set_xscale('log')
g.axes[i, j].set_xlim([10 ** np.floor(np.log10(np.min(df[k[j]]))),
10 ** np.ceil(np.log10(np.max(df[k[j]])))])
# plt.savefig('SuperPuper.png', dpi=600, format='png')
# sns.stripplot(x='Lch', y='R_open', data=df, jitter=False, color='orange')
# plt.title(self.chip_name + '\n' + self.measurement + '\n' + 'On/off ratio(Lch)' + '\n' + 'Bias = {} V'.format(
# self.sd_biases[z]))
# plt.xlabel('Lch, um', fontsize=14)
# plt.ylabel('Log10 on/off ratio', fontsize=14)
fig.tight_layout(pad=1)
plt.savefig(
(self.filepath_kernel + r'\Pairplot {}V.png'.format(self.sd_biases[z])),
format='png', dpi=300)
plt.clf()
plt.close(fig)
#
# sns.stripplot(x='Lch', y='R_closed', data=df, jitter=False, color='orange')
#
# plt.title(self.chip_name + '\n' + self.measurement + '\n' + 'On/off ratio(Lch)' + '\n' + 'Bias = {} V'.format(
# self.sd_biases[z]))
#
# # plt.xlabel('Lch, um', fontsize=14)
# # plt.ylabel('Log10 on/off ratio', fontsize=14)
#
# fig.tight_layout(pad=1)
#
# plt.savefig(
# (self.filepath_kernel + r'\R_closed(Lch) {} V.png'.format(self.sd_biases[z])),
# format='png', dpi=150)
#
# plt.clf()
# plt.close(fig)
def export_data(self):
status = np.array(self.status_array, dtype='str')
for i in range(self.rows):
for j in range(self.columns):
if self.status_array[i, j] == 1:
status[i, j] = 'Non-conductive'
if self.status_array[i, j] == 2:
status[i, j] = 'Gate leakage'
if self.status_array[i, j] == 3:
status[i, j] = 'Bad/no data'
for i in range(self.rows):
for j in range(self.columns):
if status[i, j] == 'nan':
status[i, j] = 'Operable'
for z in range(0, self.num_sd_bias):
df = pd.DataFrame(np.array([
np.array(np.repeat(self.chip_name, self.rows * self.columns)),
np.array(np.repeat(self.measurement, self.rows * self.columns)),
np.ndarray.flatten(status),
np.repeat(self.sd_biases[z], self.rows * self.columns),
np.rint(np.ndarray.flatten(self.length_array * 10 ** 4)),
np.rint(np.ndarray.flatten(self.on_state_resistance[:, :, z, 0])),
np.rint(np.ndarray.flatten(self.off_state_resistance[:, :, z, 0])),
np.ndarray.flatten(self.on_off_ratio[:, :, z, 0]),
np.round(np.ndarray.flatten(self.max_mobility[:, :, z]), 3)]).T)
df.columns = ['Chip name', 'Measurement', 'Status', 'Bias',
'Lch', 'R_on', 'R_off', 'On/off', 'Max mobility']
pd.DataFrame.to_csv(df, path_or_buf = self.filepath_kernel + r'\Results {} V.csv'.format(self.sd_biases[z]),
sep = ',', header = True, index = False)