InSe Dynamic Memristor-Based Reservoir Computing for Temporal and Spatial Signal Recognition
Multimodal recognition techniques are pivotal for advancing contemporary artificial intelligence, particularly in enhancing visual perception. However, research on electronic devices capable of robust multimodal recognition remains limited. In this study, we employ an InSe/Al<sub>2</sub>O<sub>3</sub>/ Pb(Zr<sub>0</sub>·<sub>2</sub>Ti<sub>0</sub>·<sub>8</sub>)O<sub>3</sub> (PZT) heterostructure as a dynamic memristor. Moreover, the InSe dynamic-memristor-based optoelectronic reservoir computing (RC) system is developed for multimodal recognition of temporal and spatial signals. Under electrical modulation, the InSe dynamic-memristor-based parallel RC system has been employed to efficiently process temporal signal tasks, such as a waveform classification task with a normalized root-mean-square error (NRMSE) of 0.0873, and a spoken-digit recognition task achieving a recognition accuracy of 99%. Under optical modulation, the InSe dynamic-memristor-based RC system has been used for processing a spatial signal task for number-image recognition with 100% accuracy. The InSe dynamic-memristor-based optoelectronic RC system sets the stage for future interactive AI vision applications based on 2D electronic devices.
Authors
- Hengji Li (ORCID: https://orcid.org/0000-0002-4275-915X)
- Tian‐Ling Ren (ORCID: https://orcid.org/0000-0002-7330-0544)
- Xiaofei Yue
- Siqi Lin (ORCID: https://orcid.org/0000-0002-9544-6317)
- Lin Han (ORCID: https://orcid.org/0000-0003-0461-2031)
- Min Jin (ORCID: https://orcid.org/0000-0001-7763-0869)
- Qiuliang Li
- Jianguo Hu (ORCID: https://orcid.org/0000-0003-2501-2588)
- Jing Chen (ORCID: https://orcid.org/0000-0002-2890-6117)
- Zhenhua Wang (ORCID: https://orcid.org/0000-0002-7347-8561)
- Hong Liu (ORCID: https://orcid.org/0000-0003-1640-9620)
- Ping Li (ORCID: https://orcid.org/0000-0003-1343-8768)
- Zheng Zhang (ORCID: https://orcid.org/0000-0003-4174-3364)
- Junqiang Zhu
Institutions
- University of Electronic Science and Technology of China (CN)
- Shandong University (CN)
- Fudan University (CN)
- Suzhou University of Science and Technology (CN)
- Crystal Research (United States) (US)
- City University (BD)
- Istituto di Scienze Marine del Consiglio Nazionale delle Ricerche (IT)
- Shanghai Jian Qiao University (CN)
- Systemic Risk Centre (GB)
- Shanghai Dianji University (CN)
- National Engineering Research Center for Information Technology in Agriculture (CN)
- State Key Laboratory of ASIC and System
- Suzhou City University
- Shandong University of Science and Technology (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Nano Letters
- Published
- 2025-09-04
- DOI
- https://doi.org/10.1021/acs.nanolett.5c03781
- Citations
- 6
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 14.89
Funders
- National Natural Science Foundation of China
- Tsinghua University
- National Key Research and Development Program of China