High-throughput terahertz imaging: progress and challenges
Many exciting terahertz imaging applications, such as non-destructive evaluation,
biomedical diagnosis, and security screening, have been historically limited in practical …
biomedical diagnosis, and security screening, have been historically limited in practical …
Software-defined nanophotonic devices and systems empowered by machine learning
Nanophotonic devices, such as metasurfaces and silicon photonic components, have been
progressively demonstrated to be efficient and versatile alternatives to their bulky …
progressively demonstrated to be efficient and versatile alternatives to their bulky …
Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks
Retrieving the pupil phase of a beam path is a central problem for optical systems across
scales, from telescopes, where the phase information allows for aberration correction, to the …
scales, from telescopes, where the phase information allows for aberration correction, to the …
Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network
Research on optical computing has recently attracted significant attention due to the
transformative advances in machine learning. Among different approaches, diffractive …
transformative advances in machine learning. Among different approaches, diffractive …
Training large-scale optoelectronic neural networks with dual-neuron optical-artificial learning
Optoelectronic neural networks (ONN) are a promising avenue in AI computing due to their
potential for parallelization, power efficiency, and speed. Diffractive neural networks, which …
potential for parallelization, power efficiency, and speed. Diffractive neural networks, which …
All‐optical phase recovery: diffractive computing for quantitative phase imaging
Quantitative phase imaging (QPI) is a label‐free computational imaging technique that
provides optical path length information of specimens. In modern implementations, the …
provides optical path length information of specimens. In modern implementations, the …
Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network
Large-scale linear operations are the cornerstone for performing complex computational
tasks. Using optical computing to perform linear transformations offers potential advantages …
tasks. Using optical computing to perform linear transformations offers potential advantages …
Super-resolution diffractive neural network for all-optical direction of arrival estimation beyond diffraction limits
Wireless sensing of the wave propagation direction from radio sources lays the foundation
for communication, radar, navigation, etc. However, the existing signal processing paradigm …
for communication, radar, navigation, etc. However, the existing signal processing paradigm …
All-optical image classification through unknown random diffusers using a single-pixel diffractive network
Classification of an object behind a random and unknown scattering medium sets a
challenging task for computational imaging and machine vision fields. Recent deep learning …
challenging task for computational imaging and machine vision fields. Recent deep learning …
Intelligent optoelectronic processor for orbital angular momentum spectrum measurement
Orbital angular momentum (OAM) detection underpins almost all aspects of vortex beams'
advances such as communication and quantum analogy. Conventional schemes are …
advances such as communication and quantum analogy. Conventional schemes are …