DIFFRACTIVE

作品数：124被引量：285H指数：12
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Multiplane quantitative phase imaging using a wavelength-multiplexed diffractive optical processor
《Advanced Photonics》2024年第5期51-69,共19页Che-Yung Shen Jingxi Li Yuhang Li Tianyi Gan Langxing Bai Mona Jarrahi Aydogan Ozcan 
supported by the U.S.Department of Energy,Office of Basic Energy Sciences,Division of Materials Sciences and Engineering(Grant No.DE-SC0023088).
Quantitative phase imaging(QPI)is a label-free technique that provides optical path length information for transparent specimens,finding utility in biology,materials science,and engineering.Here,we present QPI of a th...
关键词：quantitative phase imaging three-dimensional imaging label-free imaging diffractive neural networks computational imaging 
Superresolution imaging using superoscillatory diffractive neural networks
《Advanced Photonics》2024年第5期70-80,共11页Hang Chen Sheng Gao Haiou Zhang Zejia Zhao Zhengyang Duan Gordon Wetzstein Xing Lin 
supported by the National Key Research and Development Program of China(Grant No.2021ZD0109902);the National Natural Science Foundation of China(Grant No.62275139);the China Postdoctoral Science Foundation(Grant No.2023M741889).
Optical superoscillation enables far-field superresolution imaging beyond diffraction limits.However,existing superoscillatory lenses for spatial superresolution imaging systems still confront critical performance lim...
关键词：superresolution imaging photonic neural networks optical superoscillation 
Diffraction casting
《Advanced Photonics》2024年第5期81-94,共14页Ryosuke Mashiko Makoto Naruse Ryoichi Horisaki 
supported by Japan Society for the Promotion of Science(Grant Nos.JP20K05361,JP22H05197,and JP23K26567).
Optical computing is considered a promising solution for the growing demand for parallel computing in various cutting-edge fields that require high integration and high-speed computational capacity.We propose an optic...
关键词：optical computing diffractive neural network SIMD operations parallel computing logic operations machine learning 
Large-scale distributed diffractive-interference hybrid photonic chiplets被引量：1
《Advanced Photonics》2024年第4期7-8,共2页Hang Chen Yichen Shen 
The rapid development of artificial general intelligence(AGI)introduces significant performance challenges for next-generation computing.Electronic devices such as graphics processing units(GPUs)are constrained by com...
关键词：GRAPHICS hinder LIMITATIONS 
OAM-based diffractive all-optical classification
《Advanced Photonics》2024年第1期6-7,共2页Md Sadman Sakib Rahman Aydogan Ozcan 
Object classification is an important aspect of machine intelligence.Current practices in object classification entail the digitization of object information followed by the application of digital algorithms such as d...
关键词：consuming DIGIT NEURAL 
Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding被引量：4
《Advanced Photonics》2023年第4期85-99,共15页Yuhang Li Tianyi Gan Bijie Bai Cagatay Isıl Mona Jarrahi Aydogan Ozcan 
supported by the U.S. Department of Energy (DOE), Office of Basic Energy Sciences, Division of Materials Sciences and Engineering under Award No. DE-SC0023088
Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in t...
关键词：optical information transfer electronic encoding optical decoder diffractive neural network DIFFUSERS 
Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network被引量：12
《Advanced Photonics》2023年第1期27-49,共23页Jingxi Li Tianyi Gan Bijie Bai Yi Luo Mona Jarrahi Aydogan Ozcan 
the US Air Force Office of Scientific Research funding(Grant No.FA9550-21-1-0324)。
Large-scale linear operations are the cornerstone for performing complex computational tasks.Using optical computing to perform linear transformations offers potential advantages in terms of speed,parallelism,and scal...
关键词：optical neural network deep learning diffractive optical network wavelength multiplexing optical computing 
Class-specific differential detection in diffractive optical neural networks improves inference accuracy被引量：28
《Advanced Photonics》2019年第4期2-14,共13页Jingxi Li Deniz Mengu Yi Luo Yair Rivenson Aydogan Ozcan 
Optical computing provides unique opportunities in terms of parallelization,scalability,power efficiency,and computational speed and has attracted major interest for machine learning.Diffractive deep neural networks h...
关键词：optical computation optical neural networks deep learning optical machine learning diffractive deep neural networks 
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