Abstract
Recent advances in artificial intelligence (AI) and computing technologies are currently disrupting the modeling and design paradigms in photonics. In this work, we present our perspective on the utilization of current AI models for photonic device modeling and design. Initially, through the physics-informed neural networks (PINNs) framework, we embark on the task of modal analysis, offering a unique neural networks-based solver and utilizing it to predict propagating modes and their corresponding effective indices for slab waveguides. We compare our model’s predictions against theoretical benchmarks and a finite differences solver. Evidently, using 349 analysis points, the PINN approach had a relative percentage error of 0.69272% compared to the finite differences method, which had a percentage error of 1.28142% with respect to the analytical solution, indicating that the PINN approach was more accurate in conducting modal analysis. Our model’s continuity over the entire solution domain enhances its performance and flexibility while requiring no training data due to its guidance by Maxwell’s equations, setting it apart from most AI approaches. Our model design also flexibly enables simultaneous prediction of multiple modes over any specified intervals of effective indices. In addition, we present a novel reinforcement learning (RL)-based paradigm, employing an actor–critic model for inverse design. We utilize this paradigm to optimize the transmittance of a grating coupler by manipulating the device geometry. Comparing the obtained design to that obtained using the Particle Swarm Optimization (PSO) algorithm, our RL-based approach effectively produced a significant enhancement of 34% in 14 iterations only over the initial design compared to the PSO, which prematurely scored 27% enhancement in 30 iterations, proving that our model navigates the design space more efficiently, achieving a better design than PSO and resulting in a superior design. Based on these approaches, we discuss the future of AI in photonics in forward modeling and inverse design and the untapped potential in bringing these worlds together.
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