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all-optical computing based on convolutional neural networks

Opto-Electronic Advances 2021年第11期4卷 46-54页

作者： Kun Liao Ye Chen Zhongcheng Yu Xiaoyong Hu Xingyuan Wang Cuicui Lu Hongtao Lin Qingyang Du Juejun Hu Qihuang Gong State Key Laboratory for Mesoscopic Physics&Department of Physics Collaborative Innovation Center of Quantum MatterBeijing Academy of Quantum Information SciencesNano-optoelectronics Frontier Center of Ministry of EducationPeking UniversityBeijing 100871China Collaborative Innovation Center of Extreme Optics Shanxi UniversityTaiyuan 030006China College of Mathematics and Physics Beijing University of Chemical TechnologyBeijing 100029China Beijing Key Laboratory of Nanophotonics and Ultrafine Optoelectronic Systems School of PhysicsBeijing Institute of TechnologyBeijing 100081China College of Information Science&Electronic Engineering Zhejiang UniversityHangzhou 310027China Department of Materials Science and Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA

The rapid development of information technology has fueled an ever-increasing demand for ultrafast and ultralow-en-ergy-consumption *** computing instruments are pre-dominantly electronic processors,which use elec-trons as information carriers and possess von Neumann architecture featured by physical separation of storage and *** scaling of computing speed is limited not only by data transfer between memory and processing units,but also by RC delay associated with integrated ***,excessive heating due to Ohmic losses is becoming a severe bottleneck for both speed and power consumption *** photons as information carriers is a promising *** to the weak third-order optical nonlinearity of conventional materials,building integrated photonic com-puting chips under traditional von Neumann architecture has been a ***,we report a new all-optical comput-ing framework to realize ultrafast and ultralow-energy-consumption all-optical computing based on convolutional neural *** device is constructed from cascaded silicon Y-shaped waveguides with side-coupled silicon waveguide segments which we termed“weight modulators”to enable complete phase and amplitude control in each waveguide *** generic device concept can be used for equation solving,multifunctional logic operations as well as many other mathematical *** computing functions including transcendental equation solvers,multifarious logic gate operators,and half-adders were experimentally demonstrated to validate the all-optical computing *** time-of-flight of light through the network structure corresponds to an ultrafast computing time of the order of several picoseconds with an ultralow energy consumption of dozens of femtojoules per *** approach can be further expan-ded to fulfill other complex computing tasks based on non-von Neumann architectures and thus paves a new way for on-chip all-optical comp

关键词： convolutional neural networks all-optical computing mathematical operations cascaded silicon waveguides

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Hybrid integration of 2D materials for on-chip nonlinear photonics

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Light(Advanced Manufacturing) 2023年第3期4卷 311-333页

作者： Vincent Pelgrin Hoon Hahn Yoon Eric Cassan Zhipei Sun QTF Centre of Excellence Department of Electronics and NanoengineeringAalto UniversityP.O.Box 13500FI-00076 AaltoFinland UniversitéParis-Saclay CNRSCentre de Nanosciences et de Nanotechnologies91120PalaiseauFrance

Interests surrounding the development of on-chip nonlinear optical devices have been consistently growing in the past decades due to the tremendous applications,such as quantum photonics,all-optical communications,optical computing,on-chip metrology,and *** efficient on-chip nonlinear optical devices to meet the requirements of those applications brings the need for new directions to improve the existing photonic *** research has directed the field of on-chip nonlinear optics toward the hybrid integration of two-dimensional layered materials(such as graphene,transition metal dichalcogenides,and black phosphorous)with various integrated *** combination of well-known photonic chip design platforms(e.g.,silicon,silicon nitride)and different two-dimensional layered materials has opened the road for more versatile and efficient structures and devices,which has the great potential to unlock numerous new *** review discusses the modeling and characterization of different hybrid photonic integration structures with two-dimensional materials,highlights the current state of the art examples,and presents an outlook for future prospects.

关键词： Integrated photonics 2D materials Nanophotonic Nonlinear optics Quantum computation all-optical computing Spectroscopy Broadband light sources

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Quantitative phase imaging(QPI)through random diffusers using a diffractive optical network

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Light(Advanced Manufacturing) 2023年第3期4卷 206-221页

作者： Yuhang Li Yi Luo Deniz Mengu Bijie Bai Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCalifornia 90095USA Bioengineering Department University of CaliforniaLos AngelesCalifornia 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCalifornia 90095USA

Quantitative phase imaging(QPI)is a label-free computational imaging technique used in various fields,including biology and medical *** QPI systems typically rely on digital processing using iterative algorithms for phase retrieval and image ***,we report a diffractive optical network trained to convert the phase information of input objects positioned behind random diffusers into intensity variations at the output plane,all-optically performing phase recovery and quantitative imaging of phase objects completely hidden by unknown,random phase *** QPI diffractive network is composed of successive diffractive layers,axially spanning in total~70λ,where is the illumination wavelength;unlike existing digital image reconstruction and phase retrieval methods,it forms an all-optical processor that does not require external power beyond the illumination beam to complete its QPI reconstruction at the speed of light *** all-optical diffractive processor can provide a low-power,high frame rate and compact alternative for quantitative imaging of phase objects through random,unknown diffusers and can operate at different parts of the electromagnetic spectrum for various applications in biomedical imaging and *** presented QPI diffractive designs can be integrated onto the active area of standard CCD/CMOS-based image sensors to convert an existing optical microscope into a diffractive QPI microscope,performing phase recovery and image reconstruction on a chip through light diffraction within passive structured layers.

关键词： Quantitative phase imaging optical neural network Diffractive deep neural network Diffusive media all-optical computing

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