Machine Learning-Assisted Design Automation of Integrated Photonic Devices

Photonic inverse design has emerged as a trans-formative approach in the development of integrated photonic devices. The inverse design process primarily relies on two key steps: electromagnetic simulation and optimization algorithms. However, traditional numerical methods for EM simulation often face challenges such as computational inefficiencies, limited data utilization, and a lack of universality. Similarly, conventional iterative optimization algorithms used in inverse design suffer from high computational costs, susceptibility to local optima, and sensitivity to initial conditions. With the rapid advancements in artificial intelligence, machine learning offers a promising avenue to overcome these challenges, enabling efficient automated physical design of high-performance integrated photonic devices without the need for extensive photonics expertise. In this paper, we review several advanced methods that integrate ML into EM simulation and inverse design algorithms. Additionally, we emphasize the importance of incorporating manufacturing constraints to ensure the practical feasibility of the designed devices. By highlighting the potential of ML to revolutionize the design automation of integrated photonic devices, we aim to inspire further research and innovation in this field.