报告题目：Chance-Constrained and Yield-Aware Optimization of Photonic ICs With Non-Gaussian Correlated Process Variations

报告人：崔春风 教授（北京航空航天大学数学科学学院）

主持人：寇彩霞 副教授

报告时间：2021年4月29日（周四）下午1:30-3:30

报告地点：主楼1214会议室

报告摘要：

Uncertainty quantification has become an efficient tool for uncertainty-aware prediction, but its power in yieldaware optimization has not been well explored from either theoretical or application perspectives. Yield optimization is a much more challenging task. On the one side, optimizing the generally nonconvex probability measure of performance metrics is difficult. On the other side, evaluating the probability measure in each optimization iteration requires massive simulation data, especially, when the process variations are non-Gaussian correlated. This article proposes a data-efficient framework for the yield-aware optimization of photonic ICs. This framework optimizes the design performance with a yield guarantee, and it consists of two modules: 1) a modeling module that builds stochastic surrogate models for design objectives and chance constraints with a few simulation samples and 2) a novel yield optimization module that handles probabilistic objectives and chance constraints in an efficient deterministic way. This deterministic treatment avoids repeatedly evaluating probability measures at each iteration, thus it only requires a few simulations in the whole optimization flow. We validate the accuracy and efficiency of the whole framework by a synthetic example and two photonic ICs. Our optimization method can achieve more than 30× reduction of simulation cost and better design performance

on the test cases compared with a Bayesian yield optimization approach developed recently.

报告人介绍：

崔春风博士现任北京航空航天大学准聘教授。2016年在中国科学院数学与系统科学研究院获得博士学位。2016—2017年在香港城市大学做博士后研究。2017年—2020年在美国加州大学圣塔巴巴拉分校博士后。研究方向为最优化理论与算法，具体研究兴趣包括张量计算，不确定性量化和机器学习。2018年获得IEEE EPEPS最佳论文奖和《中国科学：数学》优秀论文奖。2019年获得钟家庆数学奖，美国计算数据科学明日之星，和美国电子工程计算科学明日之星。