Selecting robust silicon photonic designs after Bayesian optimization without extra simulations

Abstract

Optimization methods are frequently exploited in the design of silicon photonic devices. In this paper, we demonstrate that pushing the objective function to its minimum during optimization often results in devices that gradually become more sensitive to perturbations of design variables. The dominant strategy of selecting the design with the smallest objective function can lead to fabrication failure or yield loss due to manufacturing process variations. To address this issue, we propose an intuitive selection criterion that can identify designs not only possessing small objective functions but that are also robust to variations. Our simulation results on the Y-splitter, direction coupler, and bent waveguide designs demonstrate that the proposed method can achieve 2x higher coverage of robust designs with almost negligible run time, compared to the two baseline methods.