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Uncertainty quantification has become an efficient tool for uncertainty-awareprediction, but its power in yield-aware optimization has not been wellexplored from either theoretical or application perspectives. Yieldoptimization is a much more challenging task. On one side, optimizing thegenerally non-convex probability measure of performance metrics is difficult.On the other side, evaluating the probability measure in each optimizationiteration requires massive simulation data, especially when the processvariations are non-Gaussian correlated. This paper proposes a data-efficientframework for the yield-aware optimization of photonic ICs. This frameworkoptimizes the design performance with a yield guarantee, and it consists of twomodules: a modeling module that builds stochastic surrogate models for designobjectives and chance constraints with a few simulation samples, and a novelyield optimization module that handles probabilistic objectives and chanceconstraints in an efficient deterministic way. This deterministic treatmentavoids repeatedly evaluating probability measures at each iteration, thus itonly requires a few simulations in the whole optimization flow. We validate theaccuracy and efficiency of the whole framework by a synthetic example and twophotonic ICs. Our optimization method can achieve more than $30\times$reduction of simulation cost and better design performance on the test casescompared with a Bayesian yield optimization approach developed recently.

Chance-Constrained and Yield-aware Optimization of Photonic ICs with Non-Gaussian Correlated Process Variations