def main():
"""Main function"""
print data_path, '\n'
for f in files:
print f
# Loops through all files
if "I_V Sweep" not in f:
continue
datad, headd = mf.csvImport(f, data_path, 104)
deviceID = headd[4][1]
print " Device:", deviceID
voltage = np.array(datad["V1"])
current = np.array(datad["I1"])
slope, intercept, r_value, p_value, std_err = stats.linregress(voltage, current)
resistance = 1.0/slope
data_summary.append([deviceID, resistance])
mf.dataOutput("SUMMARY.txt", data_path, map(list, zip(*data_summary)), format="%s\t %.5e\n")
return
def main():
"""Main function"""
print "\nBatch importing .csv files..."
print data_path, '\n'
for f in files:
print f
# Loops through all transfer files
if "Bias Stress" not in f:
continue
datatransfer, datastress = mf.csvBiasStressImport(f, data_path)
no_scans = len(datatransfer)
no_stress = len(datastress)
stress_time = datastress[0]['Time'][-1]
for s in xrange(no_scans):
print " Scan number:", s+1
outname = "{:s}_{:03d}".format(re.sub("[^\[]*\[|_;|]", "", f[:-4].replace(' ', '_')), s+1)
vd = np.array(datatransfer[s]['Vdrain'][0])
vg = np.array(datatransfer[s]['Vgate'])
ids = np.array(datatransfer[s]['absIdrain'])
igs = np.array(datatransfer[s]['absIgate'])
if doublesweep:
ids_forward = ids[:len(ids)/2]
vg_forward = vg[:len(vg)/2]
else:
ids_forward = ids
vg_forward = vg
ids_smoothed = mf.adjAvSmooth(abs(ids_forward), N=1)
diff_ids_smoothed = np.array(mf.numDiff(ids_smoothed, vg_forward))
tlmaxarg = np.argmax(diff_ids_smoothed[skipinit:-1]) + skipinit
# Linear mobility (max transconductance)
linmob_tmax = (chl/(chw*ci*vd))*(diff_ids_smoothed[tlmaxarg])
# Linear threshold voltage (max transconductance)
vthlin_tmax = vg[tlmaxarg] - ids_smoothed[tlmaxarg]/diff_ids_smoothed[tlmaxarg]
# Linear mobility at vth + 1 V
linmob_constn = (chl/(chw*ci*vd))*np.interp(vthlin_tmax + 1.0, vg_forward, diff_ids_smoothed)
# Subthreshold slope
lin_sts = min(abs(1/np.array(mf.numDiff([np.log10(abs(x)) for x in ids_smoothed[skipinit:-1]], vg_forward[skipinit:-1]))))
data_summary.append([outname, linmob_tmax, vthlin_tmax, linmob_constn, lin_sts])
mf.dataOutput(outname + "_transfer.txt", data_path, [vg, ids, igs], format="%.2f\t %.5e\t %.5e\n")
# Plot transfer
if plot:
mf.quickPlot(outname + "_TRANSFERplot", data_path, [vg, ids, igs], xlabel="Vg [V]", ylabel="Id,g [A]", yscale="log", yrange=[1e-12, 1e-3])
mf.quickPlot(outname + "_TCplot", data_path, [vg_forward, ids_smoothed], xlabel="Vg [V]", ylabel="Id [A]")
if s < no_stress:
time = np.array(datastress[s]['Time'])
idsst = np.array(datastress[s]['absIdStress'])
igsst = np.array(datastress[s]['absIgStress'])
mf.dataOutput(outname + "_stress.txt", data_path, [time, idsst, igsst], format="%.2f\t %.5e\t %.5e\n")
mf.dataOutput("BIAS_STRESS_SUMMARY.txt", data_path, map(list, zip(*data_summary)), format="%s\t %.5e\t %.5f\t %.5e\t %.5e\n")
return
