At the intersection of optics and deep learning: statistical inference, computing, and inverse design

Abstract

Deep learning has been revolutionizing information processing in many fields of science and engineering owing to the massively growing amounts of data and the advances in deep neural network architectures. As these neural networks are expanding their capabilities toward achieving state-of-the-art solutions for demanding statistical inference tasks in various applications, there appears to be a global need for low-power, scalable, and fast computing hardware beyond what existing electronic systems can offer. Optical computing might potentially address some of these needs with its inherent parallelism, power efficiency, and high speed. Recent advances in optical materials, fabrication, and optimization techniques have significantly enriched the design capabilities in optics and photonics, leading to various successful demonstrations of guided-wave and free-space computing hardware for accelerating machine learning tasks using light. In addition to statistical inference and computing, deep learning has also fundamentally affected the field of inverse optical/photonic design. The approximation power of deep neural networks has been utilized to develop optics/photonics systems with unique capabilities, all the way from nanoantenna design to end-to-end optimization of computational imaging and sensing systems. In this review, we attempt to provide a broad overview of the current state of this emerging symbiotic relationship between deep learning and optics/photonics.