Monte Carlo analysis is a procedure to assess manufacturing yields by repeating simulation runs with varying applied random variations to part parameters.
Sensitivity analysis repeats runs while perturbing a single parameter for each step. This allows the sensitivity to that parameter of any number of measurements to be evaluated. This makes it possible to identify components and parameters that may require a tight tolerance to maintain a particular specification, or in some cases identify instabilities in the design.
Worst-case analysis attempts to find the combination of component and parameter variances which will lead to the worst possible result. It assigns each component or parameter with a value that is either at the positive extreme or the negative extreme. The decision as to which to use is obtained from the results of a prior sensitivity analysis using the sign of each sensitivity value.
Both Monte Carlo and worst-case may be used to assess production yield and reliability. For many applications, Monte Carlo produces the more realistic results but rarely locates the extremes that are theoretically possible even if statistically unlikely.
It should be noted that worst-case analysis is not guaranteed to locate the worst possible result. The algorithm assumes that the relationship between component or parameter variation and the measured result is linear. This is almost never the case in practice.
The implementation of Monte Carlo analysis in SIMetrix has been designed to be quick to set up for simple cases while still providing the required flexibility for more advanced requirements as might be required for integrated circuit design.
In this chapter we cover the aspects of setting up Monte Carlo, Sensitivity and Worts-case analyses from the front end. This includes setting device tolerances in the schematic, setting up and running the simulation and analysing the results.
This chapter covers Monte Carlo analysis for SIMetrix (SPICE) simulations. Monte Carlo analysis is also available for SIMPLIS simulations, see Multi-step and Monte Carlo Analyses.
Setting model tolerances is not covered here but in the Monte Carlo Analysis chapter in the Simulator Reference Manual/Monte Carlo, Sensitivity and Worst-case/Specifying Tolerances.
| ◄ Function Reference | A Monte Carlo Example ▶ |