Ultrafast reconfigurable topological-modulators-based photonic processing accelerator
The rise of artificial intelligence has triggered exponential growth in data volume, demanding rapid and efficient processing. High-speed, energy-efficient, and parallel-scalable computing hardware is thus increasingly critical. We demonstrate a wafer-scale non-volatile photonic computing chip using topological modulators. Leveraging the GHz-speed electro-optic response and nonvolatility of ferroelectric lead zirconate titanate (PZT) thin films via topological photonic confinement, our chip enables 1,000× accelerated reconfiguration, near-zero static-power operation, and a computational density of 266 trillion operations per second per square millimeter (TOPS/mm²). A 16-channel wavelength-space multiplexed chip delivers 1.92 TOPS throughput with 95.64% digit-recognition accuracy and 94.5% precision for solving time-varying partial differential equations. Additionally, the chip supports functional reconfiguration for high bandwidth density optical I/O. This work establishes ferroelectric topological photonics for efficient high-speed photonic tensor processing. This work demonstrates a non-volatile photonic computing chip based on topological modulators on a PZT platform. The chip achieves 110 GHz bandwidth, 266 TOPS/mm² compute density, and near-zero static power. A 16-channel chip delivers 1.92 TOPS throughput with over 94.5% accuracy for digit recognition and PDE solving.
Authors
- Ya-Hui Zhu (ORCID: https://orcid.org/0000-0003-0230-8271)
- X Wang
- Yikai Su (ORCID: https://orcid.org/0000-0002-1526-8187)
- P D Zhang
- Jungan Wang (ORCID: https://orcid.org/0009-0009-7424-5012)
- Bin Li (ORCID: https://orcid.org/0000-0002-7774-5452)
- Xi Zhang (ORCID: https://orcid.org/0000-0002-4638-3997)
- Jingchi Li (ORCID: https://orcid.org/0000-0001-5077-0343)
- Yuqi Chen (ORCID: https://orcid.org/0000-0002-5635-3409)
- Xiong Ni
- Yan Jiang (ORCID: https://orcid.org/0009-0006-1219-7770)
- Min Sun (ORCID: https://orcid.org/0009-0007-6886-3565)
- Mingrui Yuan (ORCID: https://orcid.org/0009-0005-0332-6254)
- W Zhou
- Qingqing Han
- Yong Zhang
- Feng Qiu
- Chen Yang
Institutions
- Zhejiang Sci-Tech University (CN)
- Shanghai Jiao Tong University (CN)
- Hangzhou Academy of Agricultural Sciences (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- Nature Communications
- Published
- 2026-07-23
- DOI
- https://doi.org/10.1038/s41467-026-75993-8
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Science and Technology Commission of Shanghai Municipality