REVOLUTIONIZING SILICON PHOTONIC DEVICE DESIGN THROUGH DEEP GENERATIVE MODELS: AN INVERSE APPROACH AND EMERGING TRENDS

Adrian Velasco; Meera Narayan

doi:10.55640/ijaair-v02i06-02

Authors

Adrian Velasco Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
Meera Narayan Department of Applied Physics, Stanford University, Stanford, CA, USA

DOI:

https://doi.org/10.55640/ijaair-v02i06-02

Keywords:

Silicon Photonics, Deep Generative Models, Inverse Design, Photonic Device Optimization

Abstract

The design of silicon photonic devices has traditionally relied on iterative simulation-based methods, which are time-consuming and often limited in exploring the vast design space. Recent advancements in deep generative models have paved the way for a paradigm shift by enabling inverse design approaches that are both efficient and accurate. This study explores how deep generative networks, particularly variational autoencoders (VAEs) and generative adversarial networks (GANs), are revolutionizing the development of silicon photonic structures by mapping desired optical responses directly to geometrical configurations. The paper reviews current methodologies, evaluates their effectiveness in performance optimization, and identifies emerging trends such as physics-informed learning, hybrid generative-discriminative frameworks, and real-time feedback-based design evolution. This convergence of deep learning and photonics promises to accelerate innovation in optical communications, sensing, and integrated photonic circuits.

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