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PSpice User Guide Monte Carlo and sensitivity (worst-case) analyses October 2019 601 Product Version 17.4-2019 © 1999-2019 All Rights Reserved. Sensitivity analysis The sensitivity analysis results are printed in the output file (.OUT). For each varied parameter, the percent change in the collating function and the sweep variable value at which the collating function was measured are given. The parameters are listed in worst output order; for example, the collating function was its worst when the first parameter printed in the list was varied. When you use the YMAX collating function, the output file also lists mean deviation and sigma values. These are based on the changes in the output variable from nominal at every sweep point in every sensitivity run. Manual optimization You can use worst-case analysis to perform manual optimization with PSpice. The monotonicity condition is usually met if the parameters have a very limited range. Performing worst-case analysis with tight tolerances on the parameters produces sensitivity and worst-case results (in the output file). You can use these 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. Note: Parametric sweeps (.STEP), like the one performed in the circuit file shown in Figure 13-12, can be used to augment this procedure. Monte Carlo analysis 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.