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Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network

Advanced Photonics 2023年第1期5卷 27-49页

作者： Jingxi Li Tianyi Gan Bijie Bai Yi Luo Mona Jarrahi Aydogan Ozcan University of California Electrical and Computer Engineering DepartmentLos AngelesCaliforniaUnited States University of California Bioengineering DepartmentLos AngelesCaliforniaUnited States University of California California NanoSystems InstituteLos AngelesCaliforniaUnited States

Large-scale linear operations are the cornerstone for performing complex computational *** optical computing to perform linear transformations offers potential advantages in terms of speed,parallelism,and ***,the design of successive spatially engineered diffractive surfaces forming an optical network was demonstrated to perform statistical inference and compute an arbitrary complex-valued linear transformation using narrowband *** report deep-learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily selected,complex-valued linear transformations between an input and output field of view,each with Ni and No pixels,*** broadband diffractive processor is composed of Nw wavelength channels,each of which is uniquely assigned to a distinct target transformation;a large set of arbitrarily selected linear transformations can be individually performed through the same diffractive network at different illumination wavelengths,either simultaneously or sequentially(wavelength scanning).We demonstrate that such a broadband diffractive network,regardless of its material dispersion,can successfully approximate Nw unique complex-valued linear transforms with a negligible error when the number of diffractive neurons(N)in its design is≥*** further report that the spectral multiplexing capability can be increased by increasing N;our numerical analyses confirm these conclusions for Nw>180 and indicate that it can further increase to Nw∼2000,depending on the upper bound of the approximation *** parallel,wavelength-multiplexed diffractive networks will be useful for designing highthroughput intelligent machine-vision systems and hyperspectral processors that can perform statistical inference and analyze objects/scenes with unique spectral properties.

关键词： optical neural network deep learning diffractive optical network wavelength multiplexing optical computing

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Simultaneous transmission of time-frequency and data with co-amplification over urban fiber links

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Chinese Optics Letters 2024年第1期22卷 28-33页

作者：操前童周刘雷王家亮应康庞拂飞桂有珍 Key Laboratory of Specialty Fiber Optics and Optical Access Networks School of Communication and Information EngineeringShanghai UniversityShanghai 200444China Key Laboratory for Quantum Optics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Key Laboratory of Space Laser Communication and Detection Technology Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China

We demonstrate a simultaneous transmission of time-frequency and data over a 160-km urban business network in *** signals are transmitted through a cascaded optical link consisting of 48 km and 32 km,which are connected by an optical *** metrological signals are inserted into the communication network using dense wavelength division *** influence of the interference between different signals has been *** experimental results demonstrate that the radio frequency(RF)instability can reach 2.1×10^(-14)at 1 s and 2.3×10^(-17)at 10,000 s,and the time interval transfer of one pulse per second(1 PPS)signal with less than 10 ps at 1 s is *** work paves the way for the widespread dissemination of ultra-stable time and frequency signals over the communication networks.

关键词： simultaneous transmission radio frequency transfer communication network wavelength multiplexing

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