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Compact Models for Integrated Circuit Design
.param sigmavt=0
.param delvto=0
** systematic **
** instance based sampling of systematic variation
** g[pn]mean varying in clock step for each instance
** defined in calling library MC_svt using global[np]mean and
* global[np]meansigma
** existing global[np]sigma varies by instance and is defined by
* global[np]sigma
.param random0p=agauss(0,1,3)
.param random1p=agauss(0,1,3)
.param random2p=agauss(0,1,3)
.param random3p=agauss(0,1,3)
.param random4p=agauss(0,1,3)
.param random5p=agauss(0,1,3)
.param random6p=agauss(0,1,3)
.param random7p=agauss(0,1,3)
.param random8p=agauss(0,1,3)
.param random9p=agauss(0,1,3)
.param A0p=’random0p*(globalmcflag==1)’
.param A1p=’random1p*(globalmcflag==1)’
.param A2p=’random2p*(globalmcflag==1)’
.param A3p=’random3p* (globalmcflag==1)’
.param A4p=’random4p*(globalmcflag==1)’
.param A5p=’random5p*(globalmcflag==1)’
.param A6p=’random6p*(globalmcflag==1)’
.param A7p=’random7p*(globalmcflag==1)’
.param A8p=’random8p*(globalmcflag==1)’
.param A9p=’random9p*(globalmcflag==1)’
.param
+toxp_svt = ‘1.1398E-009 + ( 2.8414E-011*((A0p*gpsigma) + gpmean))’
+dxlp_svt = ‘0.0000E+000 + ( 1.2000E-009*((A1p*gpsigma) + gpmean))’
+dxwp_svt = ‘0.0000E+00 + (-2.7000E-009*((A2p*gpsigma) + gpmean))’
+dvthp_svt = ‘0.0000E+00 + (-2.3075E-002*((A3p*gpsigma) + gpmean))’
+cjp_svt = ‘3.9056E-003 + ( 2.7427E-004*((A4p*gpsigma) + gpmean))’
+cjswp_svt = ‘1.0000E-014 + ( 7.0000E-016*((A5p*gpsigma) + gpmean))’
+cjswgp_svt = ‘2.5218E-01 + ( 1.7653E-011*((A6p*gpsigma) + gpmean))’
+cgop_svt = ‘3.3205E-012 + (-2.3244E-013*((A7p*gpsigma) + gpmean))’
+cglp_svt = ‘6.4850E-012 + (-4.5395E-013*((A8p*gpsigma) + gpmean))’
+drdswp_svt = ‘0.0000E+00 + ( 5.3496E-000*((A9p*gpsigma) + gpmean))’
*** mismatch ***
.param AM0=agauss(0,1,1)
.param
+cvtp_svt=’avtp/1.414214’
+lef=’l-8.3256E-11’ wef=’w/n_fingers+0E-09’
+geo_fac=’1/sqrt(multi*n_fingers*lef*wef)’