Matrix computation, as a fundamental building block of information processing in science
and technology, contributes most of the computational overheads in modern signal …

Recent years have witnessed promising artificial intelligence (AI) applications in many
disciplines, including optics, engineering, medicine, economics, and education. In particular …

Research on optical computing has recently attracted significant attention due to the
transformative advances in machine learning. Among different approaches, diffractive …

Imaging through diffusers presents a challenging problem with various digital image
reconstruction solutions demonstrated to date using computers. Here, we present a …

Many exciting terahertz imaging applications, such as non-destructive evaluation,
biomedical diagnosis, and security screening, have been historically limited in practical …

Recent advances in deep learning have been providing non-intuitive solutions to various
inverse problems in optics. At the intersection of machine learning and optics, diffractive …

Deep learning has been transformative in many fields, motivating the emergence of various
optical computing architectures. Diffractive optical network is a recently introduced optical …

In recent years, the explosive development of artificial intelligence implementing by artificial
neural networks (ANNs) creates inconceivable demands for computing hardware. However …

Abstract Machine vision faces bottlenecks in computing power consumption and large
amounts of data. Although opto-electronic hybrid neural networks can provide assistance …

Under spatially coherent light, a diffractive optical network composed of structured surfaces
can be designed to perform any arbitrary complex-valued linear transformation between its …