All-optical combinational logical units featuring fifth-order cascade

Modern computational technologies are increasingly encountering significant limitations, driving a shift towards alternative paradigms such as optical computing. In this study, we introduce novel all-optical combinational logic units based on diffractive neural networks (D 2 NNs), designed to perform high-order logical operations both efficiently and swiftly using only two modulation layers. This innovative design offers increased processing speed, improved energy efficiency, robust environmental stability, and high error tolerance, making it exceptionally well-suited for a broad spectrum of applications in optical computing and communications. By leveraging transfer learning, we successfully developed a fifth-order cascaded combinational logic circuit for practical information transmission system. Furthermore, we reveal a pioneering application of our device in Optical Time Division Multiplexing (OTDM), demonstrating its capability to manage high-speed data transfer seamlessly without the need for electronic conversion. Extensive simulations and experimental validations highlight the potential of our model as a foundational technology for future optical computing architectures, paving the way toward more sustainable and efficient optical data processing platforms.

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Publication Details

Journal
Chip
Published
2024-10-11
DOI
https://doi.org/10.1016/j.chip.2024.100112
Citations
4
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
1.39
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article

All-optical combinational logical units featuring fifth-order cascade

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4 citations
Chip
Neural Networks and Reservoir Computing
1.39
article

All-optical combinational logical units featuring fifth-order cascade

Chenying Yang, Weidong Shen, Yuchuan Shao, Yueguang Zhang, Shaoqing Yu, Haiqi Gao, Junren Wen, Yipeng Chen
article en
4 citations

Abstract

Modern computational technologies are increasingly encountering significant limitations, driving a shift towards alternative paradigms such as optical computing. In this study, we introduce novel all-optical combinational logic units based on diffractive neural networks (D 2 NNs), designed to perform high-order logical operations both efficiently and swiftly using only two modulation layers. This innovative design offers increased processing speed, improved energy efficiency, robust environmental stability, and high error tolerance, making it exceptionally well-suited for a broad spectrum of applications in optical computing and communications. By leveraging transfer learning, we successfully developed a fifth-order cascaded combinational logic circuit for practical information transmission system. Furthermore, we reveal a pioneering application of our device in Optical Time Division Multiplexing (OTDM), demonstrating its capability to manage high-speed data transfer seamlessly without the need for electronic conversion. Extensive simulations and experimental validations highlight the potential of our model as a foundational technology for future optical computing architectures, paving the way toward more sustainable and efficient optical data processing platforms.

ChipVol. 3(4)
Chinese Academy of Sciences (CN), Shanghai Institute of Optics and Fine Mechanics (CN), University of Chinese Academy of Sciences (CN), State Key Laboratory of Modern Optical Instruments (CN), Zhejiang University (CN)
Openalex Percentile: Top 15%
Neural Networks and Reservoir Computing
1.39
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