@article{3963,
  title = {Model-based Design Analysis and Yield Optimization},
  journal = {IEEE Transactions on Semiconductor Manufacturing},
  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.},
  volume = {19},
  number = {4},
  pages = {475-486},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = feb,
  year = {2006},
  slug = {3963},
  author = {Pfingsten, T. and Herrmann, D. and Rasmussen, CE.},
  month_numeric = {2}
}