Progress of Tensor-Based High-Dimensional Uncertainty Quantification of Process Variations

Abstract

Uncertainty quantification under process variations is an important topic in computational electromagnetics (EM) and electronic design automation (EDA). The popular stochastic methods become computationally inefficient when the number of uncertain parameters grows. A promising technique to address this challenge is tensor computation. This short paper reviews some recent tensor methods for high-dimensional uncertainty quantification in the field of EDA and computational EM.