Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning methodology.

ABSTRACT: We propose a neural network (NN)-based surrogate modeling framework for photonic device optimization, especially in domains with imbalanced feature importance and high data generation costs. Our framework, which comprises physics-based transfer learning (PBTL)-enhanced surrogate modeling and scalarized multi-objective genetic algorithms (GAs), offers a generalizable solution for photonic design automation with minimal data resources. To validate the framework, we optimize quantum cascade laser (QCL) structures consisting of two regions: active and injection, which have different levels of feature importance. The optimization targets include five key QCL performance metrics, resulting in multiple local optimum structures. To address the challenge of multiple local optima in the output laten

SUBMITTER: Kim G 

PROVIDER: S-EPMC12615652 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature