Recent advances in the inverse design of silicon photonic devices and related platforms using deep generative models.

Abstract

This article presents an overview of recent research on the inverse design of optical devices using deep generative models. The increasing complexity of modern optical devices necessitates advanced design methodologies that can efficiently navigate vast parameter spaces and generate novel, high-performance structures. Established optimization methods, such as adjoint and topology optimization, have successfully addressed many design challenges. However, the increasing complexity of modern optical devices creates opportunities for complementary approaches. Deep generative models offer additional capabilities by leveraging their ability to learn complex patterns and generate novel designs. This review examines various deep learning methodologies, including multi-layer perceptrons (MLP), convolutional neural networks (CNN), auto-encoders (AE), Generative Adversarial Networks (GAN), and reinforcement learning (RL) approaches. We analyze their applications in the inverse design of photonic devices, comparing their effectiveness and integration in the design process. Our findings indicate that while MLP-based methods were commonly used in early research, recent studies have increasingly employed CNN, GAN, AE, and RL methods, as well as advanced MLP models. Each of these methods offers unique advantages and presents specific challenges in the context of optical device inverse design. This review critically evaluates these deep learning-based inverse design technologies, highlighting their strengths and limitations in the context of optical device design. By synthesizing current research and identifying key trends, this article aims to guide future developments in the application of deep generative models for optical device inverse design.