Atomic Nb-doping of WS2 for high-performance synaptic transistors in neuromorphic computing

• Published in: Microsystems & nanoengineering 2024-09, Vol.10 (1), p.132-10, Article 132
• Main Authors: Guan, Kejie, Li, Yinxiao, Liu, Lin, Sun, Fuqin, Wang, Yingyi, Zheng, Zhuo, Zhou, Weifan, Zhang, Cheng, Cai, Zhengyang, Wang, Xiaowei, Feng, Simin, Zhang, Ting
• Format: Article
• Language: English
• Subjects: 639/301/357/995; 639/925/927/1007; Controllability; Doping; Engineering; Flakes (defects); Handwriting recognition; Modulation; Niobium; Physical properties; Potentiation; Synaptic density; Synaptic plasticity; Transistors; Transition metal compounds; Tungsten disulfide; Two dimensional materials
• ISSN: 2055-7434; 2096-1030; 2055-7434
• DOI: 10.1038/s41378-024-00779-1

Owing to the controllable growth and large-area synthesis for high-density integration, interest in employing atomically thin two-dimensional (2D) transition-metal dichalcogenides (TMDCs) for synaptic transistors is increasing. In particular, substitutional doping of 2D materials allows flexible modulation of material physical properties, facilitating precise control in defect engineering for eventual synaptic plasticity. In this study, to increase the switch ratio of synaptic transistors, we selectively performed experiments on WS 2 and introduced niobium (Nb) atoms to serve as the channel material. The Nb atoms were substitutionally doped at the W sites, forming a uniform distribution across the entire flakes. The synaptic transistor devices exhibited an improved switch ratio of 10 3 , 100 times larger than that of devices prepared with undoped WS 2 . The Nb atoms in WS 2 play crucial roles in trapping and detrapping electrons. The modulation of channel conductivity achieved through the gate effectively simulates synaptic potentiation, inhibition, and repetitive learning processes. The Nb-WS 2 synaptic transistor achieves 92.30% recognition accuracy on the Modified National Institute of Standards and Technology (MNIST) handwritten digit dataset after 125 training iterations. This study’s contribution extends to a pragmatic and accessible atomic doping methodology, elucidating the strategies underlying doping techniques for channel materials in synaptic transistors.