Diffractive deep neural networks: Theories, optimization, and applications

Abstract

Optical neural networks (ONN) are experiencing a renaissance, driven by the transformative impact of artificial intelligence, as arithmetic pressures are progressively increasing the demand for optical computation. Diffractive deep neural networks (D2NN) are the important subclass of ONN, providing a novel architecture for computation with trained diffractive layers. Given that D2NN directly process light waves, they inherently parallelize multiple tasks and reduce data processing latency, positioning them as a promising technology for future optical computing applications. This paper begins with a brief review of the evolution of ONN and a concept of D2NN, followed by a detailed discussion of the theoretical foundations, model optimizations, and application scenarios of D2NN. Furthermore, by analyzing current application scenarios and technical limitations, this paper provides an evidence-based prediction of the future trajectory of D2NN and outlines a roadmap of research and development efforts to unlock its full potential.