This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …

Artificial intelligence (AI) is transforming diffractive optics development through its advanced
capabilities in design optimization, pattern generation, fabrication enhancement …

Photonic computing enables faster and more energy-efficient processing of vision data,,,–.
However, experimental superiority of deployable systems remains a challenge because of …

Wireless sensing of the wave propagation direction from radio sources lays the foundation
for communication, radar, navigation, etc. However, the existing signal processing paradigm …

Develo** tunable luminescent materials for high throughput information storage is highly
desired following the explosive growth of global data. Although considerable success has …

Image denoising, one of the essential inverse problems, targets to remove noise/artifacts
from input images. In general, digital image denoising algorithms, executed on computers …

Computational imaging, as a novel technology utilizing encoded image acquisition, relies on
intelligent decoding methods for effective image restoration and sensing. Optical computing …

All-optical diffractive neural networks, as analog artificial intelligence accelerators, leverage
parallelism and analog computation for complex data processing. However, their low space …

During machine visual perception, the optical signal from a scene is transferred into the
electronic domain by detectors in the form of image data, which are then processed for the …

As a successful case of combining deep learning with photonics, the research on optical
machine learning has recently undergone rapid development. Among various optical …