User`s guide

13
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34 Monte Carlo and Sensitivity/Worst-Case Analyses
Manual optimization
Worst-case analysis can be used to perform manual optimization
with PSpice A/D. The monotonicity condition is usually met if
the parameters have a very limited range. Performing worst-case
analysis with tight tolerances on the parameters yields
sensitivity and worst-case results (in the output file) which can
be used to decide how the parameters should be varied to
achieve the desired response. You can then make adjustments to
the nominal values in the circuit file, and perform the worst-case
analysis again for a new set of gradients. Parametric sweeps
(.STEP), like the one performed in the circuit file shown in
Figure 13-14, can be used to augment this procedure.
Monte Carlo anal
y
sis
Monte Carlo (.MC) analysis may be helpful when worst-case
analysis cannot be used. Monte Carlo analysis can often be used
to verify or improve on worst-case analysis results. Monte Carlo
analysis randomly selects possible parameter values, which can
be thought of as randomly selecting points in the parameter
space. The worst-case analysis assumes that the worst results
occur somewhere on the surface of this space, where parameters
(to which the output is sensitive) are at one of their extreme
values.
If this is not true, the Monte Carlo analysis may find a point at
which the results are worse. To try this, simply replace .WC in
the circuit file with .MC <#runs>, where <#runs> is the number
of simulations you are willing to perform. More runs provide
higher confidence results. To save disk space, do not specify any
OUTPUT options. The Monte Carlo summary in the output file
lists the runs in decreasing order of collating function value.
Now add the following option to the .MC statement, and
simulate again.
OUTPUT LIST RUNS <worst_run#>
This performs only two simulations: the nominal and the worst
Monte Carlo run. The parameter values used during the worst
run are written to the output file, and the results of both
simulations are saved.