Reconfigurable versatile integrated photonic computing chip

Yufei Wang; Kun Liao; Kuo Zhang; Zhuochen Du; Ze Wang; Bo Ni; Tianyu Xu; Shuai Feng; Yan Yang; Qi-Fan Yang; Quan Sun; Xiaoyong Hu; Qihuang Gong

doi:10.1186/s43593-025-00098-6

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Reconfigurable versatile integrated photonic computing chip

eLight 2025年5卷

Research Article | Updated：2025-12-17

- Reconfigurable versatile integrated photonic computing chip
- Reconfigurable versatile integrated photonic computing chip
- Affiliations：
- Author bio：
- Funds：
  
  National Key Research and Development Program of China;National Natural Science Foundation of China;Innovation Program for Quantum Science and Technology;Beijing Natural Science Foundation;International Partnership Program of Chinese Academy of Sciences;173 Technical Field Fund
- DOI：10.1186/s43593-025-00098-6
  中图分类号：
- 收稿：2025-04-08，
  
  修回：2025-07-07，
  
  录用：2025-07-10，
  
  网络出版：2025-08-18，
  
  纸质出版：2025-12
- Accepted：
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Yufei Wang, Kun Liao, Kuo Zhang, 等. Reconfigurable versatile integrated photonic computing chip[J]. eLight, 2025,5.

Yufei Wang, Kun Liao, Kuo Zhang, et al. Reconfigurable versatile integrated photonic computing chip[J]. eLight, 2025, 5.
Yufei Wang, Kun Liao, Kuo Zhang, 等. Reconfigurable versatile integrated photonic computing chip[J]. eLight, 2025,5. DOI： 10.1186/s43593-025-00098-6.

Yufei Wang, Kun Liao, Kuo Zhang, et al. Reconfigurable versatile integrated photonic computing chip[J]. eLight, 2025, 5. DOI： 10.1186/s43593-025-00098-6.

摘要

Abstract

With the rapid development of information technology

artificial intelligence and large-scale models have exhibited exceptional performance and widespread applications. Photonic hardware offers a promising solution to meet the growing demands for

computational power and energy efficiency. Researchers have aimed to develop an efficient integrated photonic computing chip capable of supporting a wide range of application scenarios in both static and dynamic temporal domains. However

with several mainstream photonic components already well-developed

achieving fundamental breakthroughs at the level of basic computing units remains highly challenging. Here

we report a novel algorithm-hardware co-design strategy that enables in situ reconfigurability across diverse neural network models

all within a unified photonic configuration. We unlock the intrinsic capabilities of a compact cross-waveguide coupled microring component to natively support both static and dynamic temporal tasks. As a proof of concept

we experimentally integrated a turnkey soliton microcomb as the light source on the photonic computing platform

demonstrating the realization of fully connected

convolutional

and recurrent neural network models within a unified structure. The chip achieves area computing efficiency of up to 2.45 TOPS/mm

for 208 tunable components. We evaluate the performance of the proposed chip by implementing image classification tasks on the MNIST and CIFAR-10 datasets

achieving measured test accuracies of 92.93% and 56.57%

respectively. Sentiment analysis on the IMDB dataset achieves a measured test accuracy of 80.81%. Furthermore

speech recognition is implemented by combining three neural networks within a scaled-up architecture. This work addresses the challenges of performing versatile computations on integrated photonic platforms

offering a promising solution for chip-integrated multifunctional photonic information processing.

关键词

Keywords

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