6. pyopus.design — Design automation support

Design automation module

Provides functions and classes for computing sensitivity, sizing a design across corners, worst case performance, worst case distance, yield targeting, and Monte Carlo analysis.

pyopus.design.nSamples(y, deltaY, confidence=0.99)

Computes the number of Monte Carlo samples needed for obtaining a yield estimate that is within +-deltaY of y with confidence level given by confidence.

pyopus.design.wcd2yield(beta)

Computes the yield that corresponds to the worst case distance beta.

pyopus.design.yield2wcd(y)

Computes the worst case distance that corresponds to yield y.

pyopus.design.yieldSigma(y, nSamples)

Computes the standard deviation of estimated yield y computed with nSamples Monte Carlo samples.

• 6.1. pyopus.design.sensitivity — Finite difference sensitivity computation and parameter screening
• 6.2. pyopus.design.wc — Worst case performance computation
• 6.3. pyopus.design.wcd — Worst case distance computation
• 6.4. pyopus.design.mc — Monte Carlo analysis
• 6.5. pyopus.design.cbd — Sizing across corners
• 6.6. pyopus.design.yt — Yield targeting (design for yield)