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.

中文翻译：

---

具有非高斯相关工艺变化的光子 IC 的机会约束和良率感知优化

不确定性量化已成为不确定性感知预测的有效工具，但其在产量感知优化中的能力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困难的。另一方面，在每次优化迭代中评估概率度量需要大量的模拟数据，特别是当过程变化是非高斯相关时。本文提出了一个数据高效的框架，用于光子 IC 的良率感知优化。该框架通过保证良率优化设计性能，它由两个模块组成：1) 一个建模模块，它使用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中重复评估概率度量，因此它只需要在整个优化流程中进行几次模拟。我们通过一个合成示例和两个光子 IC 来验证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实现 30 多美元的仿真成本降低和更好的测试用例设计性能。