AI-Guided Design of Integrated Photonic Directional Couplers

Directional couplers (DCs) are fundamental building blocks in photonic integrated circuits (PICs), yet achieving their efficient and accurate design in high-dimensional structural design parameter (SDP) spaces remains challenging for existing mode simulation approaches. Here, we propose a machine learning (ML) framework that learns from low-resolution three-dimensional (3D) mode-simulation results, allowing accurate prediction of wavelength-dependent coupling strengths of DCs with high-resolution SDPs across a five-dimensional design space. To improve the cost-effectiveness of training dataset construction, Shapley additive explanations (SHAP) analysis is further introduced to guide the sampling of SDPs. Results show that the ML framework, trained with 1792 samples, completes the prediction of a single structure in ~1 ms and a full-parameter-space sweep in 20 – 35 s, both of which are at least three orders of magnitude faster than 3D mode simulations. In addition, the framework enables inverse design to meet user-defined requirements. The prediction results also show good agreement with experimental results measured from fabricated DCs, achieving absolute deviations (ADs) below 0.05. These results validate the effectiveness of our approach for efficient and accurate DC design in high-dimensional parameter spaces, informing analogous strategies for the design of other photonic devices.