Analog parallel processor for broadband multifunctional integrated system based on silicon photonic platform

Sharing the hardware platform between diverse information systems to establish full cooperation among different functionalities has attracted substantial attention. However, broadband multifunctional integrated systems with large operating frequency ranges are challenging due to the bandwidth and computing speed restrictions of electronic circuitry. Here, we report an analog parallel processor (APP) based on the silicon photonic platform that directly discretizes and parallelizes the broadband signal in the analog domain. The APP first discretizes the signal with the optical frequency comb and then adopts optical dynamic phase interference to reassign the analog signal into 2<sup>N</sup> parallel sequences. Via photonic analog parallelism, data rate and data volume in each sequence are simultaneously compressed, which mitigates the requirement on each parallel computing core. Moreover, the fusion of the outputs from each computing core is equivalent to directly processing broadband signals. In the proof-of-concept experiment, two-channel analog parallel processing of broadband radar signals and high-speed communication signals is implemented on the single photonic integrated circuit. The bandwidth of broadband radar signal is 6 GHz and the range resolution of 2.69 cm is achieved. The wireless communication rate of 8 Gbit/s is also validated. Breaking the bandwidth and speed limitations of the single-computing core along with further exploring the multichannel potential of this architecture, we anticipate that the proposed APP will accelerate the development of powerful opto-electronic processors as critical support for applications such as satellite networks and intelligent driving.

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Publication Details

Journal
Light Science & Applications
Published
2025-02-07
DOI
https://doi.org/10.1038/s41377-025-01753-w
Citations
12
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
29.77

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article

Analog parallel processor for broadband multifunctional integrated system based on silicon photonic platform

Xiao Deng, Daoxin Dai, Na Qian, Weiwen Zou et al.
12 citations
Light Science & Applications
Neural Networks and Reservoir Computing
29.77
article

Analog parallel processor for broadband multifunctional integrated system based on silicon photonic platform

Xiao Deng, Daoxin Dai, Na Qian, Weiwen Zou, Ming Zhang, Xingjun Wang, Haowen Shu, Defu Zhou
article en
12 citations

Abstract

Sharing the hardware platform between diverse information systems to establish full cooperation among different functionalities has attracted substantial attention. However, broadband multifunctional integrated systems with large operating frequency ranges are challenging due to the bandwidth and computing speed restrictions of electronic circuitry. Here, we report an analog parallel processor (APP) based on the silicon photonic platform that directly discretizes and parallelizes the broadband signal in the analog domain. The APP first discretizes the signal with the optical frequency comb and then adopts optical dynamic phase interference to reassign the analog signal into 2<sup>N</sup> parallel sequences. Via photonic analog parallelism, data rate and data volume in each sequence are simultaneously compressed, which mitigates the requirement on each parallel computing core. Moreover, the fusion of the outputs from each computing core is equivalent to directly processing broadband signals. In the proof-of-concept experiment, two-channel analog parallel processing of broadband radar signals and high-speed communication signals is implemented on the single photonic integrated circuit. The bandwidth of broadband radar signal is 6 GHz and the range resolution of 2.69 cm is achieved. The wireless communication rate of 8 Gbit/s is also validated. Breaking the bandwidth and speed limitations of the single-computing core along with further exploring the multichannel potential of this architecture, we anticipate that the proposed APP will accelerate the development of powerful opto-electronic processors as critical support for applications such as satellite networks and intelligent driving.

Light Science & ApplicationsVol. 14(1)
Shanghai Jiao Tong University (CN), Peking University (CN), Zhejiang University (CN)
National Natural Science Foundation of China
Partnerships for the goals
Openalex Percentile: Top 0%
Neural Networks and Reservoir Computing
29.77
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