A Fast Non-Monte-Carlo Yield Analysis and Optimization by Stochastic Orthogonal Polynomials

Authors:
Fang Gong;Xuexin Liu;Hao Yu;Sheldon X. D. Tan;Junyan Ren;Lei He
Affiliations:
University of California, Los Angeles;University of California, Riverside;Nanyang Technological University, Singapore;University of California, Riverside;Fudan University, China;University of California, Los Angeles
Venue:
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Year:
2012

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Abstract

Performance failure has become a significant threat to the reliability and robustness of analog circuits. In this article, we first develop an efficient non-Monte-Carlo (NMC) transient mismatch analysis, where transient response is represented by stochastic orthogonal polynomial (SOP) expansion under PVT variations and probabilistic distribution of transient response is solved. We further define performance yield and derive stochastic sensitivity for yield within the framework of SOP, and finally develop a gradient-based multiobjective optimization to improve yield while satisfying other performance constraints. Extensive experiments show that compared to Monte Carlo-based yield estimation, our NMC method achieves up to 700X speedup and maintains 98% accuracy. Furthermore, multiobjective optimization not only improves yield by up to 95.3% with performance constraints, it also provides better efficiency than other existing methods.