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用于全光学复卷积的迈克尔逊干涉测量方法

Michelson Interferometric Methods for Full Optical Complex Convolution.

作者信息

Kang Haoyan, Wang Hao, Ye Jiachi, Hu Zibo, George Jonathan K, Sorger Volker J, Solyanik-Gorgone Maria, Movahhed Nouri Behrouz

机构信息

Optelligence LLC., 10703 Marlboro Pike, Upper Marlboro, MD 20772, USA.

Department of Electrical and Computer Engineering, The George Washington University, 800 22nd St NW, Washington, DC 20052, USA.

出版信息

Nanomaterials (Basel). 2024 Jul 28;14(15):1262. doi: 10.3390/nano14151262.

DOI:10.3390/nano14151262
PMID:39120367
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11314083/
Abstract

Optical real-time data processing is advancing fields like tensor algebra acceleration, cryptography, and digital holography. This technology offers advantages such as reduced complexity through optical fast Fourier transform and passive dot-product multiplication. In this study, the proposed Reconfigurable Complex Convolution Module (RCCM) is capable of independently modulating both phase and amplitude over two million pixels. This research is relevant for applications in optical computing, hardware acceleration, encryption, and machine learning, where precise signal modulation is crucial. We demonstrate simultaneous amplitude and phase modulation of an optical two-dimensional signal in a thin lens's Fourier plane. Utilizing two spatial light modulators (SLMs) in a Michelson interferometer placed in the focal plane of two Fourier lenses, our system enables full modulation in a 4F system's Fourier domain. This setup addresses challenges like SLMs' non-linear inter-pixel crosstalk and variable modulation efficiency. The integration of these technologies in the RCCM contributes to the advancement of optical computing and related fields.

摘要

光学实时数据处理正在推动张量代数加速、密码学和数字全息等领域的发展。这项技术具有诸多优势,例如通过光学快速傅里叶变换和无源点积乘法降低复杂度。在本研究中,所提出的可重构复数卷积模块(RCCM)能够在超过两百万像素上独立调制相位和幅度。这项研究对于光学计算、硬件加速、加密和机器学习等应用具有重要意义,在这些应用中精确的信号调制至关重要。我们在薄透镜的傅里叶平面中展示了光学二维信号的同时幅度和相位调制。利用置于两个傅里叶透镜焦平面的迈克尔逊干涉仪中的两个空间光调制器(SLM),我们的系统能够在4F系统的傅里叶域中实现全调制。这种设置解决了诸如SLM的非线性像素间串扰和可变调制效率等挑战。这些技术在RCCM中的集成推动了光学计算及相关领域的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/817f/11314083/f95d2a9a040c/nanomaterials-14-01262-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/817f/11314083/229c3421fca6/nanomaterials-14-01262-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/817f/11314083/090b3536c60e/nanomaterials-14-01262-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/817f/11314083/c6381537da50/nanomaterials-14-01262-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/817f/11314083/f672b99a19c9/nanomaterials-14-01262-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/817f/11314083/1e248874640f/nanomaterials-14-01262-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/817f/11314083/f95d2a9a040c/nanomaterials-14-01262-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/817f/11314083/229c3421fca6/nanomaterials-14-01262-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/817f/11314083/090b3536c60e/nanomaterials-14-01262-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/817f/11314083/c6381537da50/nanomaterials-14-01262-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/817f/11314083/f672b99a19c9/nanomaterials-14-01262-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/817f/11314083/1e248874640f/nanomaterials-14-01262-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/817f/11314083/f95d2a9a040c/nanomaterials-14-01262-g006.jpg

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Space-efficient optical computing with an integrated chip diffractive neural network.
具有集成芯片衍射神经网络的空间高效光计算。
Nat Commun. 2022 Feb 24;13(1):1044. doi: 10.1038/s41467-022-28702-0.
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Sci Rep. 2021 Mar 11;11(1):5776. doi: 10.1038/s41598-021-85232-3.
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