Anisotropic ionic transport-controlled synaptic weight update by protonation in a VO2 transistor. Issue 7 (28th January 2021)

• Record Type: Journal Article
• Authors: Park, Jaeseoung; Oh, Chadol; Son, Junwoo
• Is Part Of: Journal of materials chemistry. Volume 9:Issue 7(2021)
• Page Start: 2521
• Page End: 2529
• Publication Date: 2021-01-28
• Subjects: Materials -- Periodicals; Chemistry, Analytic -- Periodicals; Optical materials -- Research -- Periodicals; Electronics -- Materials -- Research -- Periodicals; 543.0284
• DOI: 10.1039/d0tc05628f
• Languages: English
• ISSNs: 2050-7526

Abstract

Abstract : The control of field-driven ionic redistribution guided by crystal anisotropy increases the retention of H + s in VO2 lattices by locating H + into the deep regions from the interfaces, and thus strengthens long-term memory in artificial synaptic devices. Abstract : Ionic–electronic coupling in a lattice strongly influences the memory and learning process by synaptic weight update in electrochemical synaptic transistors. In particular, anisotropic crystal symmetry offers a highly anisotropic diffusion process, which leads to facilitated ion migration and efficient coupling in synaptic devices. Here, we report all-solid-state VO2 synaptic transistors in which the proton diffusion under gate bias can be tuned by utilizing different crystal facets in anisotropic VO2 channels. Synaptic weight update ( i.e., excitatory post-synaptic current) by a proton (H + ) in the VO2 channel was sensitively tuned depending on the empty tunnel alignment of VO2 layers. By emulating synaptic functions using diffusion-pathway-controlled transistors, the alignment of a facile ionic pathway with gating direction increases the retention of H + in VO2 lattices by locating H + into the deep regions from the interfaces, and thus strengthens long-term memory in artificial synaptic devices. These results demonstrate that the control of field-driven ionic redistribution guided by crystal anisotropy provides an opportunity to manipulate the learning and forgetting behavior in artificial synaptic devices.