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Monte Carlo Analysis
Problems in Statistical Modeling
CC0 NET020 IN 4P
II6 VMINUS NET022 IBIAS
.ENDS LNA_DEMO
.MC 1000
Suppose now that the subcircuit BIPNPN is defined as follows:
.SUBCKT BIPNPN 1 2 3 4
Q0 1 2 3 4 NPNBIP
.PARAM RE_VAR=3.5 LOT=5% DEV=2.5%
.PARAM RB_VAR=100 LOT=5% DEV=2.5%
.PARAM RC_VAR=25 LOT=5% DEV=2.5%
.PARAM ISS_VAR=1E-16 LOT=5% DEV=2.5%
.MODEL NPNBIP NPN IS=24.5E-18 BF=121 NF=1 VAF=35 IKF=32E-3 ISE=847E-18
+ NE=1.9 BR=3.81 NR=1 VAR=3.2 IKR=2E-3 ISC=847E-18 NC=1.9
+ IRB=80E-6 RBM=5 XTB=0.5 EG=1.2 XTI=3 CJE=59.6E-15 VJE=0.895
+ MJE=0.41 TF=11E-12 XTF=15 VTF=1.5 ITF=40E-3 PTF=0 CJC=45.8E-15
+ VJC=0.642 MJC=0.33 XCJC=0.3 TR=5E-9 CJS=75.5E-15 VJS=0.65
+ MJS=0.33 FC=0.875
+ TRB1=1.05E-3 TRB2=4.4E-6 TRC1=1E-3 TRC2=3.1E-6 TRE1=0 TRE2=0
+ KF=1.32E-10 AF=1.8
* PARAMETERS WITH VARIATIONS
+ RE=RE_VAR RC=RC_VAR RB=RB_VAR
+ ISS=ISS_VAR
.ENDS
The statistical variations are defined outside the .MODEL statement. In this case, we still have
16 random variables with different names. The following table gives the equivalence between
these two different syntaxes.
First syntax
Second syntax
LOT variations M(BIPNPN.NPNBIP,RB) P(BIPNPN.RB_VAR)
M(BIPNPN.NPNBIP,RC) P(BIPNPN.RC_VAR)
... ...
DEV variations EM(XDUT.XDUT.XQ0.Q0,RB) ...
EM(XDUT.XDUT.XQ0.Q0,RC) PEM(BIPNPN.RC_VAR,XDUT.X
DUT.XQ0.Q0)
... ...
There is no clear difference between these approaches; it depends on the context.
Problems in Statistical Modeling
This section is devoted to some issues that you may encounter in statistical modeling.
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Eldo® User's Manual, 15.3