Electrooptic Dual-Mode Synergetic Control of Two-Dimensional Nonvolatile Floating-Gate Transistors for Artificial Synapses
Floating-gate memories based on two-dimensional (2D) materials show great potential for mimicking synapses in artificial neural networks (ANNs), offering a promising pathway toward hardware neuromorphic computing. However, current implementations predominantly rely on electrical modulation, limiting their applications in future artificial visual systems. Here, we demonstrate an electrooptic dual-mode artificial synapse based on a van der Waals heterostructured MoS<sub>2</sub> floating-gate transistor. This device uniquely combines electrically controlled nonvolatile programmability with light-tunable multilevel storage states, enabling versatile synaptic functionalities. Specifically, electrical stimulation induces key synaptic behaviors, including excitatory/inhibitory responses and long-term potentiation/depression, while optical excitation facilitates long-term neuroplasticity through persistent multilevel conductance states. It can also work as an optoelectronic heterosynaptic transistor and perform dual-mode modulation through both electrical and optical inputs. With this characteristic, we demonstrate its application in an ANN visual sensor, which achieves a remarkable recognition accuracy of 91.97% in handwritten digit classification.
Authors
- Z. Zhang (ORCID: https://orcid.org/0000-0003-3919-9347)
- Zhe Sheng
- Wennan Hu
- Zheng Zhang (ORCID: https://orcid.org/0000-0002-1568-2732)
- Haoran Sun (ORCID: https://orcid.org/0000-0001-5514-6144)
- Yi Zhao (ORCID: https://orcid.org/0000-0002-6512-5096)
- Yaping He (ORCID: https://orcid.org/0000-0001-7885-5277)
Institutions
- Hangzhou Normal University (CN)
- Jiangsu Second Normal University (CN)
- Fudan University (CN)
- Shanghai Fudan Microelectronics (China) (CN)
Publication Details
- Journal
- ACS Applied Materials & Interfaces
- Published
- 2025-12-09
- DOI
- https://doi.org/10.1021/acsami.5c17888
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73