2023/03/13 by Zhengqi Gao, Gao, Zhengqi, Duane S. Boning +1 · 1 citation
Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #Advanced Statistical Process Monitoring #Applications (stat.AP) #FOS: Computer and information sciences #Optimal Experimental Design Methods
paper · pdf · doi:10.48550/arxiv.2304.09723
openalex publication_date 2023/03/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The utilization of Bayesian methods has been widely acknowledged as a viable solution for tackling various challenges in electronic integrated circuit (IC) design under stochastic process variation, including circuit performance modeling, yield/failure rate estimation, and circuit optimization. As the post-Moore era brings about new technologies (such as silicon photonics and quantum circuits), many of the associated issues there are similar to those encountered in electronic IC design and can be addressed using Bayesian methods. Motivated by this observation, we present a comprehensive review of Bayesian methods in electronic design automation (EDA). By doing so, we hope to equip researchers and designers with the ability to apply Bayesian methods in solving stochastic problems in electronic circuits and beyond.