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diffractive deep neural networks at Visible Wavelengths

Engineering 2021年第10期7卷 1483-1491页

作者： Hang Chen Jianan Feng Minwei Jiang Yiqun Wang Jie Lin Jiubin Tan Peng Jin Center of Ultra-precision Optoelectronic Instrument Harbin Institute of TechnologyHarbin 150001China Nanofabrication Facility Suzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China Key Laboratory of Micro-Systems and Micro-Structures Manufacturing Ministry of EducationHarbin Institute of TechnologyHarbin 150001China

Optical deep learning based on diffractive optical elements offers unique advantages for parallel processing,computational speed,and power *** landmark method is the diffractive deep neural network(D^(2) NN)based on three-dimensional printing technology operated in the terahertz spectral *** the terahertz bandwidth involves limited interparticle coupling and material losses,this paper extends D^(2) NN to visible wavelengths.A general theory including a revised formula is proposed to solve any contradictions between wavelength,neuron size,and fabrication limitations.A novel visible light D^(2) NN classifier is used to recognize unchanged targets(handwritten digits ranging from 0 to 9)and targets that have been changed(i.e.,targets that have been covered or altered)at a visible wavelength of 632.8 *** obtained experimental classification accuracy(84%)and numerical classification accuracy(91.57%)quantify the match between the theoretical design and fabricated system *** presented framework can be used to apply a D^(2) NN to various practical applications and design other new applications.

关键词： Optical computation Optical neural networks deep learning Optical machine learning diffractive deep neural networks

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Pluggable multitask diffractive neural networks based on cascaded metasurfaces

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Opto-Electronic Advances 2024年第2期7卷 23-31页

作者： Cong He Dan Zhao Fei Fan Hongqiang Zhou Xin Li Yao Li Junjie Li Fei Dong Yin-Xiao Miao Yongtian Wang Lingling Huang Beijing Engineering Research Center of Mixed Reality and Advanced Display Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of ChinaSchool of Optics and PhotonicsBeijing Institute of TechnologyBeijing 100081China Institute of Modern Optics Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network TechnologyNankai UniversityTianjin 300350China Department of Physics and Optoelectronics Faculty of ScienceBeijing University of TechnologyBeijing 100124China Beijing National Laboratory for Condensed Matter Physics Institute of PhysicsChinese Academy of SciencesBeijing 100191China Beijing Aerospace Institute for Metrology and Measurement Technology Beijing 100076China

Optical neural networks have significant advantages in terms of power consumption,parallelism,and high computing speed,which has intrigued extensive attention in both academic and engineering *** has been considered as one of the powerful tools in promoting the fields of imaging processing and object ***,the existing optical system architecture cannot be reconstructed to the realization of multi-functional artificial intelligence systems *** push the development of this issue,we propose the pluggable diffractive neural networks(P-DNN),a general paradigm resorting to the cascaded metasurfaces,which can be applied to recognize various tasks by switching internal *** the proof-of-principle,the recognition functions of six types of handwritten digits and six types of fashions are numerical simulated and experimental demonstrated at near-infrared ***,the proposed paradigm not only improves the flexibility of the optical neural networks but paves the new route for achieving high-speed,low-power and versatile artificial intelligence systems.

关键词： optical neural networks diffractive deep neural networks cascaded metasurfaces

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Class-specific differential detection in diffractive optical neural networks improves inference accuracy

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Advanced Photonics 2019年第4期1卷 2-14页

作者： Jingxi Li Deniz Mengu Yi Luo Yair Rivenson Aydogan Ozcan University of California at Los Angeles Department of Electrical and Computer EngineeringLos AngelesCaliforniaUnited States University of California at Los Angeles Department of BioengineeringLos AngelesCaliforniaUnited States University of California at Los Angeles California NanoSystems InstituteLos AngelesCaliforniaUnited States

Optical computing provides unique opportunities in terms of parallelization,scalability,power efficiency,and computational speed and has attracted major interest for machine *** deep neural networks have been introduced earlier as an optical machine learning framework that uses task-specific diffractive surfaces designed by deep learning to all-optically perform inference,achieving promising performance for object classification and *** demonstrate systematic improvements in diffractive optical neural networks,based on a differential measurement technique that mitigates the strict nonnegativity constraint of light *** this differential detection scheme,each class is assigned to a separate pair of detectors,behind a diffractive optical network,and the class inference is made by maximizing the normalized signal difference between the photodetector *** this differential detection scheme,involving 10 photodetector pairs behind 5 diffractive layers with a total of 0.2 million neurons,we numerically achieved blind testing accuracies of 98.54%,90.54%,and 48.51%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,***,by utilizing the inherent parallelization capability of optical systems,we reduced the cross-talk and optical signal coupling between the positive and negative detectors of each class by dividing the optical path into two jointly trained diffractive neural networks that work in *** further made use of this parallelization approach and divided individual classes in a target dataset among multiple jointly trained diffractive neural *** this class-specific differential detection in jointly optimized diffractive neural networks that operate in parallel,our simulations achieved blind testing accuracies of 98.52%,91.48%,and 50.82%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively,coming close to the performance of some of the earlier generations of all-electronic deep n

关键词： optical computation optical neural networks deep learning optical machine learning diffractive deep neural networks

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Intelligent optoelectronic processor for orbital angular momentum spectrum measurement

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PhotoniX 2023年第1期4卷 558-578页

作者： Hao Wang Ziyu Zhan Futai Hu Yuan Meng Zeqi Liu Xing Fu Qiang Liu State Key Laboratory of Precision Measurement of Technology and Instruments Department of Precision InstrumentTsinghua UniversityBeijing 100084China Key Laboratory of Photonic Control Technology Ministry of EducationBeijing 100084China

Orbital angular momentum(OAM)detection underpins almost all aspects of vortex beams’advances such as communication and quantum *** schemes are frustrated by low speed,complicated system,limited detection ***,we devise an intelligent processor composed of photonic and electronic neurons for OAM spectrum measurement in a fast,accurate and direct ***,optical layers extract invisible topological charge information from incoming light and a shallow electronic layer predicts the exact *** integration of optical-computing promises us a compact single-shot system with high speed and energy efficiency(optical operations/electronic operations~10^(3)),neither necessitating reference wave nor repetitive ***,our processor is endowed with salient generalization ability and robustness against diverse structured light and adverse effects(mean squared error~10^((−5))).We further raise a universal model interpretation paradigm to reveal the underlying physical mechanisms in the hybrid processor,as distinct from conventional‘black-box’*** interpretation algorithm can improve the detection efficiency up to *** also complete the theory of optoelectronic network enabling its efficient *** work not only contributes to the explorations on OAM physics and applications,and also broadly inspires the advanced links between intelligent computing and physical effects.

关键词： Orbital angular momentum spectrum deep learning Optoelectronic neural networks Optical computing diffractive deep neural networks

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