|
|
@@ -1,460 +1,458 @@
|
|
|
-# -*- coding: utf-8 -*-
|
|
|
-"""
|
|
|
-Created on Fri Oct 05 15:49:46 2018
|
|
|
-
|
|
|
-@author: aemdlabs
|
|
|
-"""
|
|
|
-
|
|
|
-#!/usr/bin/env python2
|
|
|
-# -*- coding: utf-8 -*-
|
|
|
-"""
|
|
|
-Created on Wed Sep 5 12:05:21 2018
|
|
|
-
|
|
|
-@author: aguimera
|
|
|
-"""
|
|
|
-
|
|
|
-from PhyREC.NeoInterface import NeoSegment#, ReadMCSFile
|
|
|
-import PhyREC.SignalAnalysis as Ran
|
|
|
-import PhyREC.PlotWaves as Rplt
|
|
|
-import quantities as pq
|
|
|
-import matplotlib.pyplot as plt
|
|
|
-import numpy as np
|
|
|
-import neo
|
|
|
-import csv
|
|
|
-from datetime import datetime
|
|
|
-from scipy import integrate
|
|
|
-
|
|
|
-
|
|
|
-def ReadMCSFile(McsFile, OutSeg=None, SigNamePrefix=''):
|
|
|
- import McsPy.McsData as McsData
|
|
|
-
|
|
|
- Dat = McsData.RawData(McsFile)
|
|
|
- Rec = Dat.recordings[0]
|
|
|
- NSamps = Rec.duration
|
|
|
-
|
|
|
- if OutSeg is None:
|
|
|
- OutSeg = NeoSegment()
|
|
|
-
|
|
|
- for AnaStrn, AnaStr in Rec.analog_streams.iteritems():
|
|
|
- if len(AnaStr.channel_infos) == 1:
|
|
|
- continue
|
|
|
-
|
|
|
- for Chn, Chinfo in AnaStr.channel_infos.iteritems():
|
|
|
- print 'Analog Stream ', Chinfo.label, Chinfo.sampling_frequency
|
|
|
- ChName = str(SigNamePrefix + Chinfo.label)
|
|
|
- print ChName
|
|
|
-
|
|
|
- Fs = Chinfo.sampling_frequency
|
|
|
- Var, Unit = AnaStr.get_channel_in_range(Chn, 0, NSamps)
|
|
|
- sig = neo.AnalogSignal(pq.Quantity(Var, Chinfo.info['Unit']),
|
|
|
- t_start=0*pq.s,
|
|
|
- sampling_rate=Fs.magnitude*pq.Hz,
|
|
|
- name=ChName)
|
|
|
-
|
|
|
- OutSeg.AddSignal(sig)
|
|
|
- return OutSeg
|
|
|
-
|
|
|
-def ReadLogFile(File):
|
|
|
- Fin = open(File)
|
|
|
-
|
|
|
- reader = csv.reader(Fin, delimiter='\t')
|
|
|
-
|
|
|
- LogVals = {}
|
|
|
- ValsPos = {}
|
|
|
- for il, e in enumerate(reader):
|
|
|
- if il == 0:
|
|
|
- for ih, v in enumerate(e):
|
|
|
- ValsPos[ih] = v
|
|
|
- LogVals[v] = []
|
|
|
- else:
|
|
|
- for ih, v in enumerate(e):
|
|
|
- par = ValsPos[ih]
|
|
|
- if (par=='Vgs') or (par=='Vds') or (par=='Vref'):
|
|
|
- LogVals[par].append(float(v.replace(',','.')))
|
|
|
- elif par == 'Date/Time':
|
|
|
- LogVals[par].append(datetime.strptime(v, '%d/%m/%Y %H:%M:%S'))
|
|
|
- else:
|
|
|
- LogVals[par].append(v)
|
|
|
-
|
|
|
- deltas = np.array(LogVals['Date/Time'])[:]-LogVals['Date/Time'][0]
|
|
|
- LogVals['Time'] = []
|
|
|
- for d in deltas:
|
|
|
- LogVals['Time'].append(d.total_seconds())
|
|
|
-
|
|
|
- Fin.close()
|
|
|
-
|
|
|
- return LogVals
|
|
|
-
|
|
|
-def GetSwitchTimes(Sig, Thres=-1e-4, Plot=True):
|
|
|
- s = Sig.GetSignal(None)
|
|
|
- ds = np.abs(np.diff(np.array(s), axis=0))
|
|
|
-
|
|
|
- if Plot:
|
|
|
- plt.figure()
|
|
|
- plt.plot(s.times, s)
|
|
|
- plt.plot(s.times[1:], ds)
|
|
|
-
|
|
|
- ds = Sig.duplicate_with_new_array(signal=ds)
|
|
|
- Times = Ran.threshold_detection(ds,
|
|
|
- threshold=Thres,
|
|
|
- RelaxTime=5*pq.s)
|
|
|
- return Times
|
|
|
-
|
|
|
-def MeanStd(Data, var):
|
|
|
- Arr = np.zeros([len(Data.keys()),len(Data[Data.keys()[0]][var])])
|
|
|
- for iT,TrtName in enumerate(Data.keys()):
|
|
|
- Arr[iT,:] = Data[TrtName][var][:,0]
|
|
|
-
|
|
|
- return np.mean(Arr,0), np.std(Arr,0)
|
|
|
-
|
|
|
-def MeanStdGM(Data):
|
|
|
- Arr = np.zeros([len(Data.keys()),len(Data[Data.keys()[0]])])
|
|
|
- for iT,TrtName in enumerate(Data.keys()):
|
|
|
- Arr[iT,:] = Data[TrtName]
|
|
|
-
|
|
|
- return np.mean(Arr,0), np.std(Arr,0)
|
|
|
-
|
|
|
-def Integrate(PSD, Freqs, Fmin, Fmax):
|
|
|
- indices = np.where((Freqs >= Fmin) & (Freqs<=Fmax))
|
|
|
- print( Freqs[indices])
|
|
|
- Irms = np.sqrt(integrate.trapz(PSD[indices], Freqs[indices]))
|
|
|
- return Irms
|
|
|
-
|
|
|
-
|
|
|
-MCSMapI={'SE1':'Ch03',
|
|
|
- 'SE2':'Ch05',
|
|
|
- 'SE3':'Ch01',
|
|
|
- 'SE4':'Ch02',
|
|
|
- 'SE5':'Ch22',
|
|
|
- 'SE6':'Ch06',
|
|
|
- 'SE7':'Ch16',
|
|
|
- 'SE8':'Ch37',
|
|
|
- 'SE9':'Ch20',
|
|
|
- 'SE10':'Ch10',
|
|
|
- 'SE11':'Ch24',
|
|
|
- 'SE12':'Ch08',
|
|
|
- 'SE13':'Ch14',
|
|
|
- 'SE14':'Ch04',
|
|
|
- 'SE15':'Ch18',
|
|
|
- 'SE16':'Ch33',
|
|
|
- 'SE17':'Ch34',
|
|
|
- 'SE18':'Ch60',
|
|
|
- 'SE19':'Ch38',
|
|
|
- 'SE20':'Ch64',
|
|
|
- 'SE21':'Ch40',
|
|
|
- 'SE22':'Ch56',
|
|
|
- 'SE23':'Ch42',
|
|
|
- 'SE24':'Ch70',
|
|
|
- 'SE25':'Ch66',
|
|
|
- 'SE26':'Ch65',
|
|
|
- 'SE27':'Ch68',
|
|
|
- 'SE28':'Ch67',
|
|
|
- 'SE29':'Ch55',
|
|
|
- 'SE30':'Ch62',
|
|
|
- 'SE31':'Ch58',
|
|
|
- 'SE32':'Ch69',
|
|
|
- 'ME1':'Ch57',
|
|
|
- 'ME2':'Ch61',
|
|
|
- 'ME3':'Ch53',
|
|
|
- 'ME4':'Ch63',
|
|
|
- 'ME5':'Ch52',
|
|
|
- 'ME6':'Ch41',
|
|
|
- 'ME7':'Ch49',
|
|
|
- 'ME8':'Ch51',
|
|
|
- 'ME9':'Ch46',
|
|
|
- 'ME10':'Ch45',
|
|
|
- 'ME11':'Ch44',
|
|
|
- 'ME12':'Ch39',
|
|
|
- 'ME13':'Ch54',
|
|
|
- 'ME14':'Ch43',
|
|
|
- 'ME15':'Ch50',
|
|
|
- 'ME16':'Ch47',
|
|
|
- 'ME17':'Ch32',
|
|
|
- 'ME18':'Ch27',
|
|
|
- 'ME19':'Ch30',
|
|
|
- 'ME20':'Ch29',
|
|
|
- 'ME21':'Ch28',
|
|
|
- 'ME22':'Ch25',
|
|
|
- 'ME23':'Ch26',
|
|
|
- 'ME24':'Ch07',
|
|
|
- 'ME25':'Ch21',
|
|
|
- 'ME26':'Ch11',
|
|
|
- 'ME27':'Ch17',
|
|
|
- 'ME28':'Ch15',
|
|
|
- 'ME29':'Ch13',
|
|
|
- 'ME30':'Ch31',
|
|
|
- 'ME31':'Ch19',
|
|
|
- 'ME32':'Ch09'}
|
|
|
-
|
|
|
- #Col, Row
|
|
|
-MCSMapFacingDown={'Ch58':(0,1),
|
|
|
- 'Ch57':(0,2),
|
|
|
- 'Ch56':(0,3),
|
|
|
- 'Ch55':(0,4),
|
|
|
- 'Ch54':(0,5),
|
|
|
- 'Ch53':(0,6),
|
|
|
- 'Ch52':(0,7),
|
|
|
- 'Ch51':(0,8),
|
|
|
- 'Ch50':(0,9),
|
|
|
- 'Ch49':(0,10),
|
|
|
- 'Ch60':(1,0),
|
|
|
- 'Ch61':(1,1),
|
|
|
- 'Ch62':(1,2),
|
|
|
- 'Ch63':(1,3),
|
|
|
- 'Ch64':(1,4),
|
|
|
- 'Ch65':(1,5),
|
|
|
- 'Ch43':(1,6),
|
|
|
- 'Ch44':(1,7),
|
|
|
- 'Ch45':(1,8),
|
|
|
- 'Ch46':(1,9),
|
|
|
- 'Ch47':(1,10),
|
|
|
- 'Ch70':(2,0),
|
|
|
- 'Ch69':(2,1),
|
|
|
- 'Ch68':(2,2),
|
|
|
- 'Ch67':(2,3),
|
|
|
- 'Ch66':(2,4),
|
|
|
- 'Ch42':(2,5),
|
|
|
- 'Ch41':(2,6),
|
|
|
- 'Ch40':(2,7),
|
|
|
- 'Ch39':(2,8),
|
|
|
- 'Ch38':(2,9),
|
|
|
- 'Ch37':(2,10),
|
|
|
- 'Ch01':(3,0),
|
|
|
- 'Ch02':(3,1),
|
|
|
- 'Ch03':(3,2),
|
|
|
- 'Ch04':(3,3),
|
|
|
- 'Ch05':(3,4),
|
|
|
- 'Ch06':(3,5),
|
|
|
- 'Ch30':(3,6),
|
|
|
- 'Ch31':(3,7),
|
|
|
- 'Ch32':(3,8),
|
|
|
- 'Ch33':(3,9),
|
|
|
- 'Ch34':(3,10),
|
|
|
- 'Ch11':(4,0),
|
|
|
- 'Ch10':(4,1),
|
|
|
- 'Ch09':(4,2),
|
|
|
- 'Ch08':(4,3),
|
|
|
- 'Ch07':(4,4),
|
|
|
- 'Ch29':(4,5),
|
|
|
- 'Ch28':(4,6),
|
|
|
- 'Ch27':(4,7),
|
|
|
- 'Ch26':(4,8),
|
|
|
- 'Ch25':(4,9),
|
|
|
- 'Ch24':(4,10),
|
|
|
- 'Ch12':None,
|
|
|
- 'Ch59':None,
|
|
|
- 'Ch13':(5,1),
|
|
|
- 'Ch14':(5,2),
|
|
|
- 'Ch15':(5,3),
|
|
|
- 'Ch16':(5,4),
|
|
|
- 'Ch17':(5,5),
|
|
|
- 'Ch18':(5,6),
|
|
|
- 'Ch19':(5,7),
|
|
|
- 'Ch20':(5,8),
|
|
|
- 'Ch21':(5,9),
|
|
|
- 'Ch22':(5,10)}
|
|
|
-
|
|
|
-if __name__ == '__main__':
|
|
|
-
|
|
|
-
|
|
|
- Path = '23072019/B12784O18-T3/'
|
|
|
- InFileM = Path + '2019-07-23T18-55-56B12784O18-T3-ACDC-PostEth-PostLong.h5' ############
|
|
|
- InFileS = Path + '2019-07-23T18-55-56B12784O18-T3-ACDC-PostEth-PostLong_2.h5' ##########
|
|
|
- LogFile = Path + 'B12784O18-T3-ACDC-PostEth-PostLong.txt'
|
|
|
-
|
|
|
- StartCycle = -1
|
|
|
-
|
|
|
- LogVals = ReadLogFile(LogFile)
|
|
|
- delta = np.mean([t-LogVals['Time'][it] for it, t in enumerate(LogVals['Time'][1:])])*pq.s
|
|
|
- Delay = delta * StartCycle
|
|
|
-
|
|
|
- DCch = ('ME5', 'ME7', 'ME29', 'ME31', 'SE5', 'SE7', 'SE29', 'SE31')
|
|
|
-
|
|
|
- TrigChannel = 'SE31'
|
|
|
- TrigThres = 5e-5
|
|
|
- Vgs = np.array(LogVals['Vgs'])
|
|
|
- Vds = LogVals['Vds'][0]
|
|
|
-
|
|
|
- ivgain1 = 12e3*pq.V
|
|
|
- ivgain2 = 101
|
|
|
- ACgain = 10*1
|
|
|
- DCgain = 1*pq.V
|
|
|
- Fsig = 10
|
|
|
- StabTime = 10*pq.s
|
|
|
- GuardTime = 1*pq.s
|
|
|
- BW = 100
|
|
|
- ivgainDC = 118.8*pq.V
|
|
|
- ivgainAC = 1188*pq.V
|
|
|
-
|
|
|
-
|
|
|
- Rec = ReadMCSFile(InFileM,
|
|
|
- OutSeg=None,
|
|
|
- SigNamePrefix='M')
|
|
|
-
|
|
|
- Rec = ReadMCSFile(InFileS,
|
|
|
- OutSeg=Rec,
|
|
|
- SigNamePrefix='S')
|
|
|
-
|
|
|
-# %%
|
|
|
- plt.close('all')
|
|
|
- plt.ion()
|
|
|
-
|
|
|
- SwTimes = GetSwitchTimes(Sig=Rec.GetSignal(TrigChannel),
|
|
|
- Thres=TrigThres,
|
|
|
- Plot=True)
|
|
|
-
|
|
|
- SlotsDC = []
|
|
|
- for sig in Rec.Signals():
|
|
|
- if sig.name not in DCch:
|
|
|
- continue
|
|
|
-
|
|
|
- if sig.name.startswith('M'):
|
|
|
- col = 'r'
|
|
|
- else:
|
|
|
- col = 'g'
|
|
|
- SlotsDC.append(Rplt.WaveSlot(sig,
|
|
|
- Position=0,
|
|
|
- Color=col,
|
|
|
- Alpha=0.5))
|
|
|
-
|
|
|
- SwTimes = LogVals['Time']*pq.s+SwTimes[0]+Delay
|
|
|
-#%% calc IV DC
|
|
|
- DevDCVals = {}
|
|
|
-
|
|
|
- fig, Axt = plt.subplots()
|
|
|
- Ids = {}
|
|
|
- for sl in SlotsDC:
|
|
|
- Ids[sl.name] = []
|
|
|
- for isw, (t, vg) in enumerate(zip(SwTimes, Vgs)):
|
|
|
- ts = SwTimes[isw]+delta
|
|
|
- TWind = (t+StabTime, ts-GuardTime)
|
|
|
- s = sl.GetSignal(TWind, Units='V')
|
|
|
- Axt.plot(s)
|
|
|
- vio = np.mean(s).magnitude
|
|
|
- ids = (vio*101-(-vg+Vds))/12e3
|
|
|
- Ids[sl.name].append(ids)
|
|
|
-
|
|
|
-
|
|
|
-
|
|
|
-#%% Calc GM
|
|
|
- GM = {}
|
|
|
- Irms = {}
|
|
|
- Urms = {}
|
|
|
-
|
|
|
- fig, (AxPsd, Axt) = plt.subplots(2,1)
|
|
|
- fig2, (AxPs, Axt) = plt.subplots(2,1)
|
|
|
- for sig in Rec.Signals():
|
|
|
- if sig.name[0:3] == 'SEn':
|
|
|
- continue
|
|
|
-
|
|
|
-#
|
|
|
- GM[sig.name] = []
|
|
|
- Irms[sig.name] = []
|
|
|
- Urms[sig.name] = []
|
|
|
-
|
|
|
- for isw, (t, vg) in enumerate(zip(SwTimes, Vgs)):
|
|
|
- if isw == len(SwTimes)-1:
|
|
|
- ts = SwTimes[isw]-Delay
|
|
|
- else:
|
|
|
- ts = SwTimes[isw+1]
|
|
|
-
|
|
|
- TWind = (t+StabTime, ts-GuardTime)
|
|
|
- s = sig.GetSignal(TWind, Units ='V')
|
|
|
-
|
|
|
-
|
|
|
- if s.name in DCch:
|
|
|
- s = (s*ivgain2-(-vg+Vds)*pq.V)/ivgain1
|
|
|
- else:
|
|
|
- s = s/(ivgain1*ACgain/ivgain2)
|
|
|
-
|
|
|
-
|
|
|
- Axt.plot(s.times, s, label=vg, alpha=0.5)
|
|
|
-
|
|
|
- PS = Ran.PlotPSD((s,),
|
|
|
- Time = TWind,
|
|
|
- Ax=AxPs,
|
|
|
- FMin=1,
|
|
|
- Label=str(vg),
|
|
|
- scaling='spectrum')
|
|
|
-
|
|
|
- ps = PS[sig.name]['psd']
|
|
|
- Fps = PS[sig.name]['ff']
|
|
|
-
|
|
|
- indicesPeak = np.where( ((Fps >= Fsig-4) & (Fps<=Fsig+4)))
|
|
|
-
|
|
|
- IDSpeak = np.sqrt(ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]]+
|
|
|
- ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]+1]+
|
|
|
- ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]-1])
|
|
|
-
|
|
|
- gm = IDSpeak*1000/0.707
|
|
|
- GM[sig.name] = np.append(GM[sig.name],gm)
|
|
|
-
|
|
|
- PSD = Ran.PlotPSD((s,),
|
|
|
- Time = TWind,
|
|
|
- Ax=AxPsd,
|
|
|
- FMin=1,
|
|
|
- Label=str(vg),
|
|
|
- scaling='density')
|
|
|
-
|
|
|
- psd = PSD[sig.name]['psd'][:,0]
|
|
|
- Fpsd = PSD[sig.name]['ff']
|
|
|
-
|
|
|
-# irms = Integrate(psd, Fpsd, 1.9, 1.9*3.2)
|
|
|
-# Irms[sig.name] = np.append(Irms[sig.name],irms*np.sqrt(2))
|
|
|
- irms = Integrate(psd, Fpsd, 63, 200)
|
|
|
- Irms[sig.name] = np.append(Irms[sig.name],irms*np.sqrt(2))
|
|
|
-
|
|
|
- Irms[sig.name] = Irms[sig.name]
|
|
|
-
|
|
|
- Urms[sig.name] = Irms[sig.name]/GM[sig.name]
|
|
|
-
|
|
|
-
|
|
|
-
|
|
|
- plt.figure(12)
|
|
|
- GMmean, GMstd = MeanStdGM(GM)
|
|
|
- plt.plot(Vgs, GMmean*1000/0.1,'k',label = '1 metal layer')
|
|
|
- plt.fill_between(Vgs, GMmean*1000/0.1-GMstd*1000/0.1, GMmean*1000/0.1+GMstd*1000/0.1,color = 'k',alpha =0.3)
|
|
|
- plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
|
|
|
- plt.ylabel('G$_m$ (mS/V)')
|
|
|
- plt.legend()
|
|
|
-
|
|
|
- plt.figure(7)
|
|
|
- IrmsMean, IrmsStd = MeanStdGM(Irms)
|
|
|
- plt.semilogy(Vgs, IrmsMean,'k',label = 'rms')
|
|
|
- plt.fill_between(Vgs, IrmsMean-IrmsStd, IrmsMean+IrmsStd ,color = 'k',alpha =0.3)
|
|
|
-
|
|
|
- plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
|
|
|
- plt.ylabel('I$_{rms}$ (A)')
|
|
|
- plt.legend()
|
|
|
-
|
|
|
- plt.figure(8)
|
|
|
- UrmsMean, UrmsStd = MeanStdGM(Urms)
|
|
|
- plt.semilogy(Vgs, UrmsMean,'k',label = '1 metal layer')
|
|
|
- plt.fill_between(Vgs, UrmsMean-UrmsStd, UrmsMean+UrmsStd ,color = 'k',alpha =0.3)
|
|
|
- plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
|
|
|
- plt.ylabel('U$_{rms}$ (A)')
|
|
|
- plt.legend()
|
|
|
-
|
|
|
-
|
|
|
-
|
|
|
-#%% plot map Urms
|
|
|
-import scipy.io
|
|
|
-scipy.io.savemat('UrmsLow.mat',Urms)
|
|
|
-
|
|
|
-plt.figure()
|
|
|
-A=np.log10(np.ones((11,6))*5e-17)
|
|
|
-
|
|
|
-import matplotlib.colors as colors
|
|
|
-for Trt in Urms.keys():
|
|
|
- ch = MCSMapI[Trt]
|
|
|
-# if Trt in DCch:
|
|
|
-# continue
|
|
|
-
|
|
|
- A[MCSMapFacingDown[ch][1],MCSMapFacingDown[ch][0]] = (Urms[Trt][9])*1e6
|
|
|
-
|
|
|
-plt.imshow(A, interpolation='nearest', vmin=1, vmax=15, norm=colors.LogNorm(vmin=1, vmax=15))
|
|
|
-plt.grid(True)
|
|
|
-cbar=plt.colorbar()
|
|
|
-plt.xlabel('column',fontsize=12)
|
|
|
-plt.ylabel('row',fontsize=12)
|
|
|
-cbar.set_label('U$_{gs-rms}$ ($\mu$V)', rotation=270, labelpad=15,fontsize=13)
|
|
|
+# -*- coding: utf-8 -*-
|
|
|
+"""
|
|
|
+Created on Fri Oct 05 15:49:46 2018
|
|
|
+
|
|
|
+@author: aemdlabs
|
|
|
+"""
|
|
|
+
|
|
|
+#!/usr/bin/env python2
|
|
|
+# -*- coding: utf-8 -*-
|
|
|
+"""
|
|
|
+Created on Wed Sep 5 12:05:21 2018
|
|
|
+
|
|
|
+@author: aguimera
|
|
|
+"""
|
|
|
+
|
|
|
+from PhyREC.NeoInterface import NeoSegment#, ReadMCSFile
|
|
|
+import PhyREC.SignalAnalysis as Ran
|
|
|
+import PhyREC.PlotWaves as Rplt
|
|
|
+import quantities as pq
|
|
|
+import matplotlib.pyplot as plt
|
|
|
+import numpy as np
|
|
|
+import neo
|
|
|
+import csv
|
|
|
+from datetime import datetime
|
|
|
+from scipy import integrate
|
|
|
+
|
|
|
+
|
|
|
+def ReadMCSFile(McsFile, OutSeg=None, SigNamePrefix=''):
|
|
|
+ import McsPy.McsData as McsData
|
|
|
+
|
|
|
+ Dat = McsData.RawData(McsFile)
|
|
|
+ Rec = Dat.recordings[0]
|
|
|
+ NSamps = Rec.duration
|
|
|
+
|
|
|
+ if OutSeg is None:
|
|
|
+ OutSeg = NeoSegment()
|
|
|
+
|
|
|
+ for AnaStrn, AnaStr in Rec.analog_streams.iteritems():
|
|
|
+ if len(AnaStr.channel_infos) == 1:
|
|
|
+ continue
|
|
|
+
|
|
|
+ for Chn, Chinfo in AnaStr.channel_infos.iteritems():
|
|
|
+ print 'Analog Stream ', Chinfo.label, Chinfo.sampling_frequency
|
|
|
+ ChName = str(SigNamePrefix + Chinfo.label)
|
|
|
+ print ChName
|
|
|
+
|
|
|
+ Fs = Chinfo.sampling_frequency
|
|
|
+ Var, Unit = AnaStr.get_channel_in_range(Chn, 0, NSamps)
|
|
|
+ sig = neo.AnalogSignal(pq.Quantity(Var, Chinfo.info['Unit']),
|
|
|
+ t_start=0*pq.s,
|
|
|
+ sampling_rate=Fs.magnitude*pq.Hz,
|
|
|
+ name=ChName)
|
|
|
+
|
|
|
+ OutSeg.AddSignal(sig)
|
|
|
+ return OutSeg
|
|
|
+
|
|
|
+def ReadLogFile(File):
|
|
|
+ Fin = open(File)
|
|
|
+
|
|
|
+ reader = csv.reader(Fin, delimiter='\t')
|
|
|
+
|
|
|
+ LogVals = {}
|
|
|
+ ValsPos = {}
|
|
|
+ for il, e in enumerate(reader):
|
|
|
+ if il == 0:
|
|
|
+ for ih, v in enumerate(e):
|
|
|
+ ValsPos[ih] = v
|
|
|
+ LogVals[v] = []
|
|
|
+ else:
|
|
|
+ for ih, v in enumerate(e):
|
|
|
+ par = ValsPos[ih]
|
|
|
+ if (par=='Vgs') or (par=='Vds') or (par=='Vref'):
|
|
|
+ LogVals[par].append(float(v.replace(',','.')))
|
|
|
+ elif par == 'Date/Time':
|
|
|
+ LogVals[par].append(datetime.strptime(v, '%d/%m/%Y %H:%M:%S'))
|
|
|
+ else:
|
|
|
+ LogVals[par].append(v)
|
|
|
+
|
|
|
+ deltas = np.array(LogVals['Date/Time'])[:]-LogVals['Date/Time'][0]
|
|
|
+ LogVals['Time'] = []
|
|
|
+ for d in deltas:
|
|
|
+ LogVals['Time'].append(d.total_seconds())
|
|
|
+
|
|
|
+ Fin.close()
|
|
|
+
|
|
|
+ return LogVals
|
|
|
+
|
|
|
+def GetSwitchTimes(Sig, Thres=-1e-4, Plot=True):
|
|
|
+ s = Sig.GetSignal(None)
|
|
|
+ ds = np.abs(np.diff(np.array(s), axis=0))
|
|
|
+
|
|
|
+ if Plot:
|
|
|
+ plt.figure()
|
|
|
+ plt.plot(s.times, s)
|
|
|
+ plt.plot(s.times[1:], ds)
|
|
|
+
|
|
|
+ ds = Sig.duplicate_with_new_array(signal=ds)
|
|
|
+ Times = Ran.threshold_detection(ds,
|
|
|
+ threshold=Thres,
|
|
|
+ RelaxTime=5*pq.s)
|
|
|
+ return Times
|
|
|
+
|
|
|
+def MeanStd(Data, var):
|
|
|
+ Arr = np.zeros([len(Data.keys()),len(Data[Data.keys()[0]][var])])
|
|
|
+ for iT,TrtName in enumerate(Data.keys()):
|
|
|
+ Arr[iT,:] = Data[TrtName][var][:,0]
|
|
|
+
|
|
|
+ return np.mean(Arr,0), np.std(Arr,0)
|
|
|
+
|
|
|
+def MeanStdGM(Data):
|
|
|
+ Arr = np.zeros([len(Data.keys()),len(Data[Data.keys()[0]])])
|
|
|
+ for iT,TrtName in enumerate(Data.keys()):
|
|
|
+ Arr[iT,:] = Data[TrtName]
|
|
|
+
|
|
|
+ return np.mean(Arr,0), np.std(Arr,0)
|
|
|
+
|
|
|
+def Integrate(PSD, Freqs, Fmin, Fmax):
|
|
|
+ indices = np.where((Freqs >= Fmin) & (Freqs<=Fmax))
|
|
|
+ print( Freqs[indices])
|
|
|
+ Irms = np.sqrt(integrate.trapz(PSD[indices], Freqs[indices]))
|
|
|
+ return Irms
|
|
|
+
|
|
|
+
|
|
|
+MCSMapI={'SE1':'Ch03',
|
|
|
+ 'SE2':'Ch05',
|
|
|
+ 'SE3':'Ch01',
|
|
|
+ 'SE4':'Ch02',
|
|
|
+ 'SE5':'Ch22',
|
|
|
+ 'SE6':'Ch06',
|
|
|
+ 'SE7':'Ch16',
|
|
|
+ 'SE8':'Ch37',
|
|
|
+ 'SE9':'Ch20',
|
|
|
+ 'SE10':'Ch10',
|
|
|
+ 'SE11':'Ch24',
|
|
|
+ 'SE12':'Ch08',
|
|
|
+ 'SE13':'Ch14',
|
|
|
+ 'SE14':'Ch04',
|
|
|
+ 'SE15':'Ch18',
|
|
|
+ 'SE16':'Ch33',
|
|
|
+ 'SE17':'Ch34',
|
|
|
+ 'SE18':'Ch60',
|
|
|
+ 'SE19':'Ch38',
|
|
|
+ 'SE20':'Ch64',
|
|
|
+ 'SE21':'Ch40',
|
|
|
+ 'SE22':'Ch56',
|
|
|
+ 'SE23':'Ch42',
|
|
|
+ 'SE24':'Ch70',
|
|
|
+ 'SE25':'Ch66',
|
|
|
+ 'SE26':'Ch65',
|
|
|
+ 'SE27':'Ch68',
|
|
|
+ 'SE28':'Ch67',
|
|
|
+ 'SE29':'Ch55',
|
|
|
+ 'SE30':'Ch62',
|
|
|
+ 'SE31':'Ch58',
|
|
|
+ 'SE32':'Ch69',
|
|
|
+ 'ME1':'Ch57',
|
|
|
+ 'ME2':'Ch61',
|
|
|
+ 'ME3':'Ch53',
|
|
|
+ 'ME4':'Ch63',
|
|
|
+ 'ME5':'Ch52',
|
|
|
+ 'ME6':'Ch41',
|
|
|
+ 'ME7':'Ch49',
|
|
|
+ 'ME8':'Ch51',
|
|
|
+ 'ME9':'Ch46',
|
|
|
+ 'ME10':'Ch45',
|
|
|
+ 'ME11':'Ch44',
|
|
|
+ 'ME12':'Ch39',
|
|
|
+ 'ME13':'Ch54',
|
|
|
+ 'ME14':'Ch43',
|
|
|
+ 'ME15':'Ch50',
|
|
|
+ 'ME16':'Ch47',
|
|
|
+ 'ME17':'Ch32',
|
|
|
+ 'ME18':'Ch27',
|
|
|
+ 'ME19':'Ch30',
|
|
|
+ 'ME20':'Ch29',
|
|
|
+ 'ME21':'Ch28',
|
|
|
+ 'ME22':'Ch25',
|
|
|
+ 'ME23':'Ch26',
|
|
|
+ 'ME24':'Ch07',
|
|
|
+ 'ME25':'Ch21',
|
|
|
+ 'ME26':'Ch11',
|
|
|
+ 'ME27':'Ch17',
|
|
|
+ 'ME28':'Ch15',
|
|
|
+ 'ME29':'Ch13',
|
|
|
+ 'ME30':'Ch31',
|
|
|
+ 'ME31':'Ch19',
|
|
|
+ 'ME32':'Ch09'}
|
|
|
+
|
|
|
+ #Col, Row
|
|
|
+MCSMapFacingDown={'Ch58':(0,1),
|
|
|
+ 'Ch57':(0,2),
|
|
|
+ 'Ch56':(0,3),
|
|
|
+ 'Ch55':(0,4),
|
|
|
+ 'Ch54':(0,5),
|
|
|
+ 'Ch53':(0,6),
|
|
|
+ 'Ch52':(0,7),
|
|
|
+ 'Ch51':(0,8),
|
|
|
+ 'Ch50':(0,9),
|
|
|
+ 'Ch49':(0,10),
|
|
|
+ 'Ch60':(1,0),
|
|
|
+ 'Ch61':(1,1),
|
|
|
+ 'Ch62':(1,2),
|
|
|
+ 'Ch63':(1,3),
|
|
|
+ 'Ch64':(1,4),
|
|
|
+ 'Ch65':(1,5),
|
|
|
+ 'Ch43':(1,6),
|
|
|
+ 'Ch44':(1,7),
|
|
|
+ 'Ch45':(1,8),
|
|
|
+ 'Ch46':(1,9),
|
|
|
+ 'Ch47':(1,10),
|
|
|
+ 'Ch70':(2,0),
|
|
|
+ 'Ch69':(2,1),
|
|
|
+ 'Ch68':(2,2),
|
|
|
+ 'Ch67':(2,3),
|
|
|
+ 'Ch66':(2,4),
|
|
|
+ 'Ch42':(2,5),
|
|
|
+ 'Ch41':(2,6),
|
|
|
+ 'Ch40':(2,7),
|
|
|
+ 'Ch39':(2,8),
|
|
|
+ 'Ch38':(2,9),
|
|
|
+ 'Ch37':(2,10),
|
|
|
+ 'Ch01':(3,0),
|
|
|
+ 'Ch02':(3,1),
|
|
|
+ 'Ch03':(3,2),
|
|
|
+ 'Ch04':(3,3),
|
|
|
+ 'Ch05':(3,4),
|
|
|
+ 'Ch06':(3,5),
|
|
|
+ 'Ch30':(3,6),
|
|
|
+ 'Ch31':(3,7),
|
|
|
+ 'Ch32':(3,8),
|
|
|
+ 'Ch33':(3,9),
|
|
|
+ 'Ch34':(3,10),
|
|
|
+ 'Ch11':(4,0),
|
|
|
+ 'Ch10':(4,1),
|
|
|
+ 'Ch09':(4,2),
|
|
|
+ 'Ch08':(4,3),
|
|
|
+ 'Ch07':(4,4),
|
|
|
+ 'Ch29':(4,5),
|
|
|
+ 'Ch28':(4,6),
|
|
|
+ 'Ch27':(4,7),
|
|
|
+ 'Ch26':(4,8),
|
|
|
+ 'Ch25':(4,9),
|
|
|
+ 'Ch24':(4,10),
|
|
|
+ 'Ch12':None,
|
|
|
+ 'Ch59':None,
|
|
|
+ 'Ch13':(5,1),
|
|
|
+ 'Ch14':(5,2),
|
|
|
+ 'Ch15':(5,3),
|
|
|
+ 'Ch16':(5,4),
|
|
|
+ 'Ch17':(5,5),
|
|
|
+ 'Ch18':(5,6),
|
|
|
+ 'Ch19':(5,7),
|
|
|
+ 'Ch20':(5,8),
|
|
|
+ 'Ch21':(5,9),
|
|
|
+ 'Ch22':(5,10)}
|
|
|
+
|
|
|
+if __name__ == '__main__':
|
|
|
+
|
|
|
+
|
|
|
+ Path = '23072019/B12784O18-T3/'
|
|
|
+ InFileM = Path + '2019-07-23T18-55-56B12784O18-T3-ACDC-PostEth-PostLong.h5' ############
|
|
|
+ InFileS = Path + '2019-07-23T18-55-56B12784O18-T3-ACDC-PostEth-PostLong_2.h5' ##########
|
|
|
+ LogFile = Path + 'B12784O18-T3-ACDC-PostEth-PostLong.txt'
|
|
|
+
|
|
|
+ StartCycle = -1
|
|
|
+
|
|
|
+ LogVals = ReadLogFile(LogFile)
|
|
|
+ delta = np.mean([t-LogVals['Time'][it] for it, t in enumerate(LogVals['Time'][1:])])*pq.s
|
|
|
+ Delay = delta * StartCycle
|
|
|
+
|
|
|
+ DCch = ('ME5', 'ME7', 'ME29', 'ME31', 'SE5', 'SE7', 'SE29', 'SE31')
|
|
|
+
|
|
|
+ TrigChannel = 'SE31'
|
|
|
+ TrigThres = 5e-5
|
|
|
+ Vgs = np.array(LogVals['Vgs'])
|
|
|
+ Vds = LogVals['Vds'][0]
|
|
|
+
|
|
|
+ ivgain1 = 12e3*pq.V
|
|
|
+ ivgain2 = 101
|
|
|
+ ACgain = 10*1
|
|
|
+ DCgain = 1*pq.V
|
|
|
+ Fsig = 10
|
|
|
+ StabTime = 10*pq.s
|
|
|
+ GuardTime = 1*pq.s
|
|
|
+ BW = 100
|
|
|
+ ivgainDC = 118.8*pq.V
|
|
|
+ ivgainAC = 1188*pq.V
|
|
|
+
|
|
|
+
|
|
|
+ Rec = ReadMCSFile(InFileM,
|
|
|
+ OutSeg=None,
|
|
|
+ SigNamePrefix='M')
|
|
|
+
|
|
|
+ Rec = ReadMCSFile(InFileS,
|
|
|
+ OutSeg=Rec,
|
|
|
+ SigNamePrefix='S')
|
|
|
+
|
|
|
+# %%
|
|
|
+ plt.close('all')
|
|
|
+ plt.ion()
|
|
|
+
|
|
|
+ SwTimes = GetSwitchTimes(Sig=Rec.GetSignal(TrigChannel),
|
|
|
+ Thres=TrigThres,
|
|
|
+ Plot=True)
|
|
|
+
|
|
|
+ SlotsDC = []
|
|
|
+ for sig in Rec.Signals():
|
|
|
+ if sig.name not in DCch:
|
|
|
+ continue
|
|
|
+
|
|
|
+ if sig.name.startswith('M'):
|
|
|
+ col = 'r'
|
|
|
+ else:
|
|
|
+ col = 'g'
|
|
|
+ SlotsDC.append(Rplt.WaveSlot(sig,
|
|
|
+ Position=0,
|
|
|
+ Color=col,
|
|
|
+ Alpha=0.5))
|
|
|
+
|
|
|
+ SwTimes = LogVals['Time']*pq.s+SwTimes[0]+Delay
|
|
|
+#%% calc IV DC
|
|
|
+ DevDCVals = {}
|
|
|
+
|
|
|
+ fig, Axt = plt.subplots()
|
|
|
+ Ids = {}
|
|
|
+ for sl in SlotsDC:
|
|
|
+ Ids[sl.name] = []
|
|
|
+ for isw, (t, vg) in enumerate(zip(SwTimes, Vgs)):
|
|
|
+ ts = SwTimes[isw]+delta
|
|
|
+ TWind = (t+StabTime, ts-GuardTime)
|
|
|
+ s = sl.GetSignal(TWind, Units='V')
|
|
|
+ Axt.plot(s)
|
|
|
+ vio = np.mean(s).magnitude
|
|
|
+ ids = (vio*101-(-vg+Vds))/12e3
|
|
|
+ Ids[sl.name].append(ids)
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+#%% Calc GM
|
|
|
+ GM = {}
|
|
|
+ Irms = {}
|
|
|
+ Urms = {}
|
|
|
+
|
|
|
+ fig, (AxPsd, Axt) = plt.subplots(2,1)
|
|
|
+ fig2, (AxPs, Axt) = plt.subplots(2,1)
|
|
|
+ for sig in Rec.Signals():
|
|
|
+ if sig.name[0:3] == 'SEn':
|
|
|
+ continue
|
|
|
+
|
|
|
+#
|
|
|
+ GM[sig.name] = []
|
|
|
+ Irms[sig.name] = []
|
|
|
+ Urms[sig.name] = []
|
|
|
+
|
|
|
+ for isw, (t, vg) in enumerate(zip(SwTimes, Vgs)):
|
|
|
+ if isw == len(SwTimes)-1:
|
|
|
+ ts = SwTimes[isw]-Delay
|
|
|
+ else:
|
|
|
+ ts = SwTimes[isw+1]
|
|
|
+
|
|
|
+ TWind = (t+StabTime, ts-GuardTime)
|
|
|
+ s = sig.GetSignal(TWind, Units ='V')
|
|
|
+
|
|
|
+
|
|
|
+ if s.name in DCch:
|
|
|
+ s = (s*ivgain2-(-vg+Vds)*pq.V)/ivgain1
|
|
|
+ else:
|
|
|
+ s = s/(ivgain1*ACgain/ivgain2)
|
|
|
+
|
|
|
+
|
|
|
+ Axt.plot(s.times, s, label=vg, alpha=0.5)
|
|
|
+
|
|
|
+ PS = Ran.PlotPSD((s,),
|
|
|
+ Time = TWind,
|
|
|
+ Ax=AxPs,
|
|
|
+ FMin=1,
|
|
|
+ Label=str(vg),
|
|
|
+ scaling='spectrum')
|
|
|
+
|
|
|
+ ps = PS[sig.name]['psd']
|
|
|
+ Fps = PS[sig.name]['ff']
|
|
|
+
|
|
|
+ indicesPeak = np.where( ((Fps >= Fsig-4) & (Fps<=Fsig+4)))
|
|
|
+
|
|
|
+ IDSpeak = np.sqrt(ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]]+
|
|
|
+ ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]+1]+
|
|
|
+ ps[np.argmax(ps[indicesPeak])+indicesPeak[0][0]-1])
|
|
|
+
|
|
|
+ gm = IDSpeak*1000/0.707
|
|
|
+ GM[sig.name] = np.append(GM[sig.name],gm)
|
|
|
+
|
|
|
+ PSD = Ran.PlotPSD((s,),
|
|
|
+ Time = TWind,
|
|
|
+ Ax=AxPsd,
|
|
|
+ FMin=1,
|
|
|
+ Label=str(vg),
|
|
|
+ scaling='density')
|
|
|
+
|
|
|
+ psd = PSD[sig.name]['psd'][:,0]
|
|
|
+ Fpsd = PSD[sig.name]['ff']
|
|
|
+
|
|
|
+# irms = Integrate(psd, Fpsd, 1.9, 1.9*3.2)
|
|
|
+# Irms[sig.name] = np.append(Irms[sig.name],irms*np.sqrt(2))
|
|
|
+ irms = Integrate(psd, Fpsd, 63, 200)
|
|
|
+ Irms[sig.name] = np.append(Irms[sig.name],irms*np.sqrt(2))
|
|
|
+
|
|
|
+ Irms[sig.name] = Irms[sig.name]
|
|
|
+
|
|
|
+ Urms[sig.name] = Irms[sig.name]/GM[sig.name]
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+ plt.figure(12)
|
|
|
+ GMmean, GMstd = MeanStdGM(GM)
|
|
|
+ plt.plot(Vgs, GMmean*1000/0.1,'k',label = '1 metal layer')
|
|
|
+ plt.fill_between(Vgs, GMmean*1000/0.1-GMstd*1000/0.1, GMmean*1000/0.1+GMstd*1000/0.1,color = 'k',alpha =0.3)
|
|
|
+ plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
|
|
|
+ plt.ylabel('G$_m$ (mS/V)')
|
|
|
+ plt.legend()
|
|
|
+
|
|
|
+ plt.figure(7)
|
|
|
+ IrmsMean, IrmsStd = MeanStdGM(Irms)
|
|
|
+ plt.semilogy(Vgs, IrmsMean,'k',label = 'rms')
|
|
|
+ plt.fill_between(Vgs, IrmsMean-IrmsStd, IrmsMean+IrmsStd ,color = 'k',alpha =0.3)
|
|
|
+
|
|
|
+ plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
|
|
|
+ plt.ylabel('I$_{rms}$ (A)')
|
|
|
+ plt.legend()
|
|
|
+
|
|
|
+ plt.figure(8)
|
|
|
+ UrmsMean, UrmsStd = MeanStdGM(Urms)
|
|
|
+ plt.semilogy(Vgs, UrmsMean,'k',label = '1 metal layer')
|
|
|
+ plt.fill_between(Vgs, UrmsMean-UrmsStd, UrmsMean+UrmsStd ,color = 'k',alpha =0.3)
|
|
|
+ plt.xlabel('V$_{gs}$ - V$_{CNP}$ (V)')
|
|
|
+ plt.ylabel('U$_{rms}$ (A)')
|
|
|
+ plt.legend()
|
|
|
+
|
|
|
+
|
|
|
+
|
|
|
+#%% plot map Urms
|
|
|
+
|
|
|
+plt.figure()
|
|
|
+A=np.log10(np.ones((11,6))*5e-17)
|
|
|
+
|
|
|
+import matplotlib.colors as colors
|
|
|
+for Trt in Urms.keys():
|
|
|
+ ch = MCSMapI[Trt]
|
|
|
+# if Trt in DCch:
|
|
|
+# continue
|
|
|
+
|
|
|
+ A[MCSMapFacingDown[ch][1],MCSMapFacingDown[ch][0]] = (Urms[Trt][9])*1e6
|
|
|
+
|
|
|
+plt.imshow(A, interpolation='nearest', vmin=1, vmax=15, norm=colors.LogNorm(vmin=1, vmax=15))
|
|
|
+plt.grid(True)
|
|
|
+cbar=plt.colorbar()
|
|
|
+plt.xlabel('column',fontsize=12)
|
|
|
+plt.ylabel('row',fontsize=12)
|
|
|
+cbar.set_label('U$_{gs-rms}$ ($\mu$V)', rotation=270, labelpad=15,fontsize=13)
|
