Chance-Constrained and Yield-Aware Optimization of Photonic ICs With Non-Gaussian Correlated Process Variations
Chance-Constrained and Yield-Aware Optimization of Photonic ICs With Non-Gaussian Correlated Process Variations
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DOI：
10.1109/tcad.2020.2968582
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发表时间：
2019-08
影响因子：
2.9
通讯作者：
Chunfeng Cui;Kaikai Liu;Zheng Zhang
中科院分区：
文献类型：
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作者：
Chunfeng Cui;Kaikai Liu;Zheng Zhang
不确定性量化已成为不确定性预测的有效工具，但其在良率感知优化中的力量尚未从理论或应用角度得到很好的探索。产量优化是一项更具挑战性的任务。一方面，优化性能指标的一般非凸概率度量是很困难的。另一方面，评估每次优化迭代中的概率度量需要大量模拟数据，特别是当过程变化非高斯相关时。本文提出了一种用于光子 IC 良率感知优化的数据高效框架。该框架通过良率保证来优化设计性能，它由两个模块组成：1）一个建模模块，通过一些模拟样本为设计目标和机会约束构建随机代理模型；2）一个新颖的良率优化模块，以有效的确定性方式处理概率目标和机会约束。这种确定性处理避免了在每次迭代时重复评估概率度量，因此在整个优化流程中只需要进行几次模拟。我们通过一个综合示例和两个光子 IC 验证了整个框架的准确性和效率。与最近开发的贝叶斯良率优化方法相比，我们的优化方法可以减少超过 30 美元的模拟成本，并在测试用例上实现更好的设计性能。
Uncertainty quantification has become an efficient tool for uncertainty-aware prediction, but its power in yield-aware 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\times $ reduction of simulation cost and better design performance on the test cases compared with a Bayesian yield optimization approach developed recently.