神经形态工程学
计算机科学
非易失性存储器
仿真
MNIST数据库
瓶颈
调制(音乐)
人工神经网络
冯·诺依曼建筑
材料科学
电子工程
计算机数据存储
记忆电阻器
可扩展性
计算
突触重量
维数之咒
光电子学
稳健性(进化)
作者
Yuhang Jia,Zunfa Wang,Dong Li,Kai Liu,Yaodong Dong,Bozhi Feng,Lei Zhang,Hua Xu
出处
期刊:Small
[Wiley]
日期:2026-08-08
卷期号:: e75080-e75080
摘要
ABSTRACT The integration of optical sensing with neuromorphic computing offers a promising pathway to overcome the von Neumann bottleneck in data‐intensive artificial intelligence applications. However, realizing robust nonvolatile storage with synergistic electrical and optical modulation in 2D devices remains a challenge. Here, we demonstrate a high‐performance floating‐gate memory based on a ReS 2 /h‐BN/Ta 2 NiSe 5 van der Waals heterostructure. Under precise photo‐electrical cooperative modulation, the device exhibits excellent nonvolatile memory characteristics, including a large memory window of 96.2 V, long retention exceeding 10 4 s, and stable endurance over 1000 cycles. Additionally, it features multi‐bit storage capabilities enabled by optical erasure and electrical writing. Beyond digital storage, the device effectively mimics biological synaptic plasticity, such as paired‐pulse facilitation and learning processes. Validating its potential for neuromorphic vision systems, a simulated three‐layer artificial neural network achieves a high recognition accuracy of 96.43% on the MNIST dataset. This work establishes a promising paradigm for future integrated sensing, storage, and computation applications based on 2D materials.
科研通智能强力驱动
Strongly Powered by AbleSci AI