Reconfigurable versatile integrated photonic computing chip

作者：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 · 发表于：eLight · 年份：2025 · DOI：10.1186/s43593-025-00098-6 · 被引用次数：21 · 研究领域：Neural Networks and Reservoir Computing、Photonic and Optical Devices、Optical Network Technologies

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