Evolving paradigms in photonics design: the shift toward artificial intelligence

Abstract

Abstract Photonics design has undergone a significant transformation, evolving from empirically guided trial-and-error methodologies to highly automated computational frameworks. This review traces the progression of design paradigms in photonics, beginning with traditional physics-based approaches grounded in Maxwell’s equations and advancing through the development of numerical simulation tools and inverse-design optimization techniques. Methods such as genetic algorithms, particle swarm optimization, gradient-based adjoint optimization, and topology optimization have enabled the systematic discovery of photonic structures tailored to specific performance objectives. More recently, artificial intelligence (AI), including machine learning, deep learning, and reinforcement learning, has emerged as a powerful paradigm for modeling, optimization, and device discovery in high-dimensional design spaces. These approaches offer new opportunities for accelerating design cycles, improving scalability, and uncovering non-intuitive geometries. This review critically examines the evolution of these methodologies, discusses their respective strengths and limitations, and outlines emerging directions that aim to integrate physical constraints, fabrication awareness, and system-level optimization into next-generation AI-driven photonic design frameworks.