非高斯相关工艺变化下光子集成电路机会约束成品率感知Optimization
Chance-Constrained and Yield-aware Optimization of Photonic ICs with Non-Gaussian Correlated Process Variations
摘要：
不确定性量化已成为一种有效的工具，不确定性感知预测，但其权力在产量感知优化尚未得到很好的探索，无论是从理论还是应用的角度。产量优化是一项更具挑战性的任务。一方面，优化性能度量的一般非凸概率测度是困难的。另一方面，在每个优化迭代中评估概率测度需要大量的仿真数据，特别是当工艺变化是非高斯相关的时。本文提出了一个数据有效的框架，光子集成电路的成品率感知优化。该框架在保证成品率的前提下优化设计性能，由两个模块组成：一个建模模块，利用少量仿真样本建立设计目标和机会约束的随机代理模型;一个新的成品率优化模块，以高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中重复评估概率测度，因此在整个优化流程中只需要很少的模拟。我们通过一个合成示例和两个光子IC验证了整个框架的准确性和效率。我们的最优化方法可以实现超过$30\times$减少仿真成本和更好的设计性能的测试用例相比，最近开发的贝叶斯成品率优化方法。
英文摘要：
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 one side, optimizing the generally non-convex 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 paper 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: a modeling module that builds stochastic surrogate models for design objectives and chance constraints with a few simulation samples, and 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.
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