Recent Advancements of Uncertainty Quantification with Non-Gaussian Correlated Process Variations: Invited Special Session Paper

Uncertainty quantification techniques have been widely used to model devices, circuits, and systems with fabrication process variations. However, most existing techniques assume that all random parameters are mutually independent or Gaussian correlated, which is rarely true in practice. How to handle non-Gaussian correlated random parameters is a fundamental and long-standing challenge. In this paper, we review existing techniques and point out their limitations. Then, we summarize our recent work to address the theoretical and computational challenges caused by non-Gaussian correlations.