Spatial‐Wavelength Multiplexing Error‐Controlled Photonic Analog Computing System

In the "post-Moore era," the growing challenges in traditional digital computing have driven renewed interest in analog computing. Photonic analog computing has emerged as an effective paradigm for overcoming the fundamental bottlenecks that constrain conventional analog accelerators, especially suited for high-speed signal processing and next-generation 5G/6G RF systems. However, existing photonic analog computing frameworks lack the flexibility required to accommodate diverse application scenarios. To address these challenges, a novel silicon photonic chip is proposed in this paper that leverages fully optical analog computation. This system features multichannel architectures to enable spatial multiplexing, a parallel array of four reconfigurable microring resonators performs wavelength division multiplexed differentiation computing. In addition, an FPGA-based error correction algorithm is developed to monitor processing operations in real time, ensuring the fidelity of computational results. Experimental demonstrations show the system's capability to solve ordinary differential equations and its applications in signal generation, coherent fiber communications, microwave photonics, and image feature detection. Taking into account the spectral utilization efficiency across the entire C-band, the theoretical on-chip processing capacity of single unit is evaluated to reach up to 2.725 tera operations per second, providing a novel hardware framework and innovative directions for photonic analog computing.

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Publication Details

Journal
Advanced Science
Published
2026-03-13
DOI
https://doi.org/10.1002/advs.202515689
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00
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article

Spatial‐Wavelength Multiplexing Error‐Controlled Photonic Analog Computing System

Simin Yu, Keren Li, Linlin Wang, Xiaoyun Wu et al.
Advanced Science
Neural Networks and Reservoir Computing
article

Spatial‐Wavelength Multiplexing Error‐Controlled Photonic Analog Computing System

Simin Yu, Keren Li, Linlin Wang, Xiaoyun Wu, Zizheng Cao, Shicheng Zhang, Yazhi Pi, Bowen Zhu, Xiaochuan Xu, X. Xiao, Shanguo Huang, Daigao Chen, Jie Yan, Tao Zhu, Xuwei Xue, Bingli Guo
article en

Abstract

In the "post-Moore era," the growing challenges in traditional digital computing have driven renewed interest in analog computing. Photonic analog computing has emerged as an effective paradigm for overcoming the fundamental bottlenecks that constrain conventional analog accelerators, especially suited for high-speed signal processing and next-generation 5G/6G RF systems. However, existing photonic analog computing frameworks lack the flexibility required to accommodate diverse application scenarios. To address these challenges, a novel silicon photonic chip is proposed in this paper that leverages fully optical analog computation. This system features multichannel architectures to enable spatial multiplexing, a parallel array of four reconfigurable microring resonators performs wavelength division multiplexed differentiation computing. In addition, an FPGA-based error correction algorithm is developed to monitor processing operations in real time, ensuring the fidelity of computational results. Experimental demonstrations show the system's capability to solve ordinary differential equations and its applications in signal generation, coherent fiber communications, microwave photonics, and image feature detection. Taking into account the spectral utilization efficiency across the entire C-band, the theoretical on-chip processing capacity of single unit is evaluated to reach up to 2.725 tera operations per second, providing a novel hardware framework and innovative directions for photonic analog computing.

Advanced Science
Beijing University of Posts and Telecommunications (CN), Shenzhen Institute of Information Technology (CN), Shenzhen University (CN), Peng Cheng Laboratory (CN), Wuhan National Laboratory for Optoelectronics (CN), Communication University of Zhejiang (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 26%
Neural Networks and Reservoir Computing
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