All-optical logic processing unit using Kerr nonlinearity of MXene
Optical logic computing harnesses the speed of light and the high bandwidth of optical signals to achieve ultrafast, highly parallel and energy-efficient operations. In particular, all-optical logic gates can perform Boolean functions using only photons, acting as core elements of future optical computing and communication systems. However, the multifunctional integration and flexible reconfiguration of optical devices remain challenging. Here, we present an electrically reconfigurable all-optical logic processing unit that leverages the Kerr nonlinear effect in conjunction with modulation of high-entropy MXene surface terminations. This architecture enables dynamic switching among seven fundamental Boolean operations - including AND, OR, NOT, NOR, NAND, XOR and XNOR - within a single optical configuration. With our platform we demonstrate handwritten digit recognition on the MNIST dataset, achieving a classification accuracy of 97.7%. By combining reconfigurable nonlinear optics with multifunctional two-dimensional materials, our approach establishes an alternative logic architecture pathway with the potential to enhance throughput and energy efficiency in response to AI workloads.
Authors
- Youqi Zhang (ORCID: https://orcid.org/0000-0003-4555-5061)
- Shoukai Zhang
- Yutao Yue (ORCID: https://orcid.org/0000-0003-4532-0924)
- Shuai Liu (ORCID: https://orcid.org/0000-0002-0699-2296)
- Lei Zhang (ORCID: https://orcid.org/0000-0001-5112-6189)
- Hao Wang (ORCID: https://orcid.org/0000-0001-6881-9977)
- Mengdi Wang (ORCID: https://orcid.org/0000-0002-2101-9507)
- Songrui Wei
- Yanqi Ge
- Jianqing Li
- Bowen Du
- Han Zhang
- Meng Qiu
- Wenkai Wang
- Yuanjiang Zhong
Institutions
- Macau University of Science and Technology (MO)
- Shenzhen University (CN)
- Hong Kong University of Science and Technology (HK)
- Ocean University of China (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- Nature Communications
- Published
- 2026-03-17
- DOI
- https://doi.org/10.1038/s41467-026-70834-0
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00