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Abstract:
Fluctuations are inherent to any fabrication process.
Integrated circuits and micro-electro-mechanical systems are
particularly affected by these variations, and due to high quality
requirements the effect on the devices performance has to be
understood quantitatively. In recent years it has become possible
to model the performance of such complex systems on the basis
of design specifications, and model-based Sensitivity Analysis
has made its way into industrial engineering. We show how an
efficient Bayesian approach, using a Gaussian process prior, can
replace the commonly used brute-force Monte Carlo scheme,
making it possible to apply the analysis to computationally costly
models. We introduce a number of global, statistically justified
sensitivity measures for design analysis and optimization. Two
models of integrated systems serve us as case studies to introduce
the analysis and to assess its convergence properties. We show
that the Bayesian Monte Carlo scheme can save costly simulation
runs and can ensure a reliable accuracy of the analysis.
