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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 globap[np]mean and
* global[np]meansigma
** existing global[np]sigma varys by instance and is defined by
* global[np]sigma
.param random0n=agauss(0,1,3)
.param random1n=agauss(0,1,3)
.param random2n=agauss(0,1,3)
.param random3n=agauss(0,1,3)
.param random4n=agauss(0,1,3)
.param random5n=agauss(0,1,3)
.param random6n=agauss(0,1,3)
.param random7n=agauss(0,1,3)
.param random8n=agauss(0,1,3)
.param random9n=agauss(0,1,3)
.param A0n=’random0n*(globalmcflag==1)’
.param A1n=’random1n*(globalmcflag==1)’
.param A2n=’random2n*(globalmcflag==1)’
.param A3n=’random3n*(globalmcflag==1)’
.param A4n=’random4n*(globalmcflag==1)’
.param A5n=’random5n*(globalmcflag==1)’
.param A6n=’random6n*(globalmcflag==1)’
.param A7n=’random7n*(globalmcflag==1)’
.param A8n=’random8n*(globalmcflag==1)’
.param A9n=’random9n*(globalmcflag==1)’
.param
+toxn_svt = ‘1.0800E-009 + ( 2.7831E-011*((A0n*gnsigma) + gnmean))’
+dxln_svt = ‘0.0000E+000 + ( 1.2000E-009*((A1n*gnsigma) + gnmean))’
+dxwn_svt = ‘0.0000E+000 + (-2.7000E-009*((A2n*gnsigma) + gnmean))’
+dvthn_svt = ‘0.0000E+000 + ( 1.5058E-002*((A3n*gnsigma) + gnmean))’
+cjn_svt = ‘3.2010E-003 + ( 2.2407E-004*((A4n*gnsigma) + gnmean))’
+cjswn_svt = ‘1.0000E-014 + ( 7.0000E-016*((A5n*gnsigma) + gnmean))’
+cjswgn_svt = ‘1.9383E-010 + ( 1.3568E-011*((A6n*gnsigma) + gnmean))’
+cgon_svt = ‘1.5000E-011 + (-1.0500E-012*((A7n*gnsigma) + gnmean))’
+cgln_svt = ‘1.0000E-011 + (-7.0000E-013*((A8n*gnsigma) + gnmean))’
+drdswn_svt = ‘0.0000E+00 + ( 4.9500E-000*((A9n*gnsigma) + gnmean))’
*** mismatch ***
.param AM0=agauss(0,1,1)
.param
+cvtn_svt=’avtn/1.414214’