Conversion of TXT to MAT for gm/ID result files for SG13G2

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

In [1]:

import pandas as pd
import numpy as np
from numpy.core.records import fromarrays
from scipy.io import savemat

In [2]:

choice = 0  #start from 0
devices = ['sg13_lv_nmos', 'sg13_lv_pmos']

# widths used for characterization and fringe cap parameters (fringe caps are not included in ngspice output)
w = np.array([5, 5])
nfing = np.array([1, 1])

In [3]:

df_raw = pd.read_csv('techsweep_'+devices[choice]+'.txt', sep=r'\s+')
par_names = df_raw.columns.to_list()
par_prefix = par_names[1].split('[')[0]

In [4]:

# remove extra headers in file body and unwanted columns
df = df_raw.drop(['frequency', 'frequency.1'], axis=1)
df = df.apply(pd.to_numeric)

# rename columns for readability
df.columns = df.columns.str.removeprefix(par_prefix+'[')
df.columns = df.columns.str.replace(par_prefix[1:], '')
df.columns = df.columns.str.removesuffix(']')

# round sweep vectors to easily addressable values
df['l'] = df['l'].apply(lambda x: round(x*1e6, 3))
df['vgs'] = df['vgs'].apply(lambda x: round(x, 3))
df['vds'] = df['vds'].apply(lambda x: round(x, 3))
df['vsb'] = df['vsb'].apply(lambda x: round(x, 3))
df

	cdd	cgb	cgd	cgdol	cgg	cgs	cgsol	cjd	cjs	css	...	ids	l	rg	sfl	sid	vds	vdss	vgs	vsb	vth
0	6.818000e-21	4.501000e-16	1.732000e-19	3.025000e-15	4.504000e-16	1.732000e-19	3.025000e-15	1.998000e-15	1.998000e-15	6.818000e-21	...	0.000000e+00	0.13	20.0400	0.000000e+00	2.834000e-29	0.000	0.1245	0.0	0.0	0.4360
1	1.367000e-21	3.647000e-16	6.294000e-20	3.025000e-15	3.648000e-16	6.294000e-20	3.025000e-15	1.727000e-15	1.727000e-15	1.367000e-21	...	0.000000e+00	0.13	20.0400	0.000000e+00	5.621000e-30	0.000	0.1245	-0.0	0.4	0.4746
2	3.731000e-22	3.156000e-16	3.074000e-20	3.025000e-15	3.157000e-16	3.074000e-20	3.025000e-15	1.565000e-15	1.565000e-15	3.731000e-22	...	0.000000e+00	0.13	20.0400	0.000000e+00	1.519000e-30	0.000	0.1245	-0.0	0.8	0.5078
3	1.222000e-22	2.827000e-16	1.767000e-20	3.025000e-15	2.827000e-16	1.767000e-20	3.025000e-15	1.453000e-15	1.453000e-15	1.222000e-22	...	0.000000e+00	0.13	20.0400	0.000000e+00	4.927000e-31	0.000	0.1245	-0.0	1.2	0.5375
4	2.974000e-21	4.501000e-16	-2.534000e-19	3.018000e-15	4.504000e-16	5.849000e-19	3.025000e-15	1.975000e-15	1.998000e-15	6.916000e-21	...	2.946000e-11	0.13	20.0400	1.046000e-28	2.090000e-29	0.025	0.1245	0.0	0.0	0.4360
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
119067	8.308000e-16	1.355000e-14	1.016000e-15	3.032000e-15	4.518000e-13	4.372000e-13	3.235000e-15	1.211000e-15	1.453000e-15	2.724000e-13	...	1.135000e-04	10.00	0.2605	2.918000e-19	2.417000e-24	1.475	0.9454	1.5	1.2	0.2935
119068	1.033000e-15	1.514000e-14	1.240000e-15	3.025000e-15	4.575000e-13	4.411000e-13	3.235000e-15	1.387000e-15	1.998000e-15	2.808000e-13	...	1.360000e-04	10.00	0.2605	3.562000e-19	2.708000e-24	1.500	1.0030	1.5	0.0	0.1808
119069	9.320000e-16	1.471000e-14	1.128000e-15	3.025000e-15	4.552000e-13	4.394000e-13	3.235000e-15	1.316000e-15	1.727000e-15	2.771000e-13	...	1.274000e-04	10.00	0.2605	3.321000e-19	2.595000e-24	1.500	0.9838	1.5	0.4	0.2238
119070	8.504000e-16	1.413000e-14	1.035000e-15	3.025000e-15	4.533000e-13	4.382000e-13	3.235000e-15	1.257000e-15	1.565000e-15	2.744000e-13	...	1.200000e-04	10.00	0.2605	3.109000e-19	2.501000e-24	1.500	0.9644	1.5	0.8	0.2607
119071	7.841000e-16	1.356000e-14	9.578000e-16	3.025000e-15	4.517000e-13	4.372000e-13	3.235000e-15	1.208000e-15	1.453000e-15	2.724000e-13	...	1.135000e-04	10.00	0.2605	2.920000e-19	2.418000e-24	1.500	0.9454	1.5	1.2	0.2935

119072 rows × 24 columns

In [5]:

# sweep variable vectors
l = np.unique(abs(df['l']))
vgs = np.unique(abs(df['vgs']))
vds = np.unique(abs(df['vds']))
vsb = np.unique(abs(df['vsb']))

In [6]:

# data
# ngspice sweep order is l, vgs, vds, vsb
dims = [len(l), len(vgs), len(vds), len(vsb)]
id = np.reshape(df['ids'].values, dims, order='C')
vt = np.reshape(df['vth'].values, dims, order='C')
gm = np.reshape(df['gm'].values, dims, order='C')
gmb = np.reshape(df['gmb'].values, dims, order='C')
gds = np.reshape(df['gds'].values, dims, order='C')
cgsol = np.reshape(df['cgsol'].values, dims, order='C')
cgg = np.reshape(df['cgg'].values, dims, order='C') \
      + np.reshape(df['cgdol'].values, dims, order='C') \
      + np.reshape(df['cgsol'].values, dims, order='C')
cgb = -np.reshape(df['cgb'].values, dims, order='C')
cgd = -np.reshape(df['cgd'].values, dims, order='C') \
      + np.reshape(df['cgdol'].values, dims, order='C')
cgs = -np.reshape(df['cgs'].values, dims, order='C') \
      + np.reshape(df['cgsol'].values, dims, order='C')
cdd = np.reshape(df['cdd'].values, dims, order='C') \
      + np.reshape(df['cjd'].values, dims, order='C') \
      + np.reshape(df['cgdol'].values, dims, order='C')
css = np.reshape(df['css'].values, dims, order='C') \
      + np.reshape(df['cjs'].values, dims, order='C') \
      + np.reshape(df['cgsol'].values, dims, order='C')
sth = np.reshape(df['sid'].values, dims, order='C')
sfl = np.reshape(df['sfl'].values, dims, order='C')

In [7]:

dic = {
  "INFO": "IHP SG13G2, 130nm CMOS, PSP",
  "CORNER": "NOM",
  "TEMP": 300.0,
  "VGS": vgs,
  "VDS": vds,
  "VSB": vsb,
  "L": l,
  "W": w[choice],
  "NFING": nfing[choice],
  "ID": id,
  "VT": vt,
  "GM": gm,
  "GMB": gmb,
  "GDS": gds,
  "CGG": cgg,
  "CGB": cgb,
  "CGD": cgd,
  "CGS": cgs,
  "CDD": cdd,
  "CSS": css,
  "STH": sth,
  "SFL": sfl
}

savemat(devices[choice]+'.mat', {devices[choice]: dic})