Selective growth of monolayer semiconductors for diverse synaptic junctions. (17th December 2018)

• Record Type: Journal Article
• Authors: Sun, Linfeng; Yu, Hua; Wang, Dong; Jiang, Jinbao; Kim, Dohyun; Kim, Hyun; Zheng, Shoujun; Zhao, Mali; Ge, Qi; Yang, Heejun
• Is Part Of: 2D materials. Volume 6:Number 1(2019)
• Publication Date: 2018-12-17
• Subjects: two-dimensional materials -- synaptic junctions -- metal-insulator transition -- defect engineering -- chemical vapor deposition; Graphene -- Periodicals; Materials science -- Periodicals; Nanostructured materials -- Periodicals; 620.115
• DOI: 10.1088/2053-1583/aaf3d0
• Languages: English
• ISSNs: 2053-1583

Abstract

Abstract: The information computation through synapse networks in the brain plays a vital role for cognitive behaviors such as image/video recognition, self-learning, and decision-making. Achieving proper synaptic networks by conventional semiconductor and memristive devices has encountered critical issues such as the spatial density requiring a number of transistors for one synapse, reliable filament formation in memristors, or emulating diverse excitatory and inhibitory synaptic plasticity with two-terminal device geometry. Here, we report selective growth of variously doped MoS2 with controllable conductance plasticity, which can be used for emulating diverse synaptic junctions. The conductance plasticity in the monolayer MoS2 was found to originate from resistive-heating near the junctions with electrodes in the two-terminal device geometry and the carrier concentration-dependent metal-insulator transition in the MoS2 channel. A spatiotemporal synaptic summation is demonstrated where the firing of a proper postsynaptic membrane potential can be designed for cognitive processes. Compared with previously reported three terminal synaptic devices with atomically thin materials, our two-terminal devices with flexible synaptic strengths have advantages for integrating three-dimensional neuronal networks. This provides a new insight on two-dimensional materials as a promising arena for integrated synaptic functionalities in artificial neural networks.