Artificial intelligence in meta-optics
Recent years have witnessed promising artificial intelligence (AI) applications in many
disciplines, including optics, engineering, medicine, economics, and education. In particular …
disciplines, including optics, engineering, medicine, economics, and education. In particular …
Inference in artificial intelligence with deep optics and photonics
Artificial intelligence tasks across numerous applications require accelerators for fast and
low-power execution. Optical computing systems may be able to meet these domain-specific …
low-power execution. Optical computing systems may be able to meet these domain-specific …
[HTML][HTML] Space-efficient optical computing with an integrated chip diffractive neural network
Large-scale, highly integrated and low-power-consuming hardware is becoming
progressively more important for realizing optical neural networks (ONNs) capable of …
progressively more important for realizing optical neural networks (ONNs) capable of …
[HTML][HTML] Photonic machine learning with on-chip diffractive optics
Abstract Machine learning technologies have been extensively applied in high-performance
information-processing fields. However, the computation rate of existing hardware is …
information-processing fields. However, the computation rate of existing hardware is …
Artificial intelligence-enabled quantitative phase imaging methods for life sciences
Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and
label-free investigation of the physiology and pathology of biological systems. This review …
label-free investigation of the physiology and pathology of biological systems. This review …
[HTML][HTML] Universal linear intensity transformations using spatially incoherent diffractive processors
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 …
can be designed to perform any arbitrary complex-valued linear transformation between its …
[HTML][HTML] Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive …
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 …
[HTML][HTML] 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 …
[HTML][HTML] Research progress in optical neural networks: theory, applications and developments
J Liu, Q Wu, X Sui, Q Chen, G Gu, L Wang, S Li - PhotoniX, 2021 - Springer
With the advent of the era of big data, artificial intelligence has attracted continuous attention
from all walks of life, and has been widely used in medical image analysis, molecular and …
from all walks of life, and has been widely used in medical image analysis, molecular and …
[HTML][HTML] Terahertz pulse shaping using diffractive surfaces
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 …
inverse problems in optics. At the intersection of machine learning and optics, diffractive …