Lesson 6 : Section 3 : Statistical Optimization
Section 2: Statistical Optimization
Statistical optimization uses a simplex algorithm (a non-linear gradient search), but instead of optimizing on something like match or gain, it optimizes on the yield. Since all of the optimizers used by Mason are technically minimizing functions, the optimizer is set to minimize the failure rate for a series of Monte Carlo Runs.
The Statistical Optimizer requires two inputs: the number of iterations to run the simplex for yield optimization, and the size of the Monte Carlo to run during that iteration. Per a rough understanding, consider that 10 iterations of yield optimization, with each iteration requiring a Monte Carlo run to determine the failure rate (let's say 100 random samples), means no fewer than one thousand circuits need to be evaluated. In practice, the numbers are much larger.
Figure 6.4: Settings for statistical analysis
In reality, the circuit described above is too simple to get much value out of yield optimization. More complex designs, however, can benefit greatly from this technique.
The yield optimizer can tweak the circuit design to maximize the number of devices fabricated which will pass a minimum criteria. This is valuable for more than just high quantity production runs- even if only one circuit is being built, having a design shifted from 50% yield to 90% yield can relieve a lot of frustration down the line. In high production runs, having a yield improve from 90% to 99.9% can mean the difference between getting unpaid overtime or getting bonuses.
Copyright 2010, Gregory Kiesel