Ion-gating synaptic transistors with long-term synaptic weight modulation. Issue 16 (8th March 2021)

• Record Type: Journal Article
• Authors: Park, Youngjun; Kim, Min-Kyu; Lee, Jang-Sik
• Is Part Of: Journal of materials chemistry. Volume 9:Issue 16(2021)
• Page Start: 5396
• Page End: 5402
• Publication Date: 2021-03-08
• Subjects: Materials -- Periodicals; Chemistry, Analytic -- Periodicals; Optical materials -- Research -- Periodicals; Electronics -- Materials -- Research -- Periodicals; 543.0284
• DOI: 10.1039/d1tc00048a
• Languages: English
• ISSNs: 2050-7526

Abstract

Abstract : This paper presents synaptic transistors that show long-term synaptic weight modulation via injection of ions. Linear and symmetric weight update is achieved, which enables high recognition accuracy in artificial neural networks. Abstract : Neuromorphic devices that emulate the human brain are required for efficient computing systems. To develop efficient neuromorphic devices, artificial synapses that are capable of linear and symmetric synaptic weight updates are necessary. Artificial synapses that exploit ion dynamics are suitable for achieving these properties, but stable and long-term synaptic weight modulation is difficult to be achieved because ions can be easily dissipated at the interfaces or in electrolytes. To prevent spontaneous ion dissipation, we design synaptic transistors that operate by ion injection into the channel layer; this process allows long-term synaptic weight updates. We also use a threshold switch as an access device for synaptic transistors. The threshold switch shows low resistance during weight updates of a synapse, and high resistance otherwise to prevent self-discharge of injected ions into the channel layer, which can improve the data retention of synaptic transistors. Linear and symmetric synaptic weight updates are achieved with a large dynamic range (>20), which enables high recognition accuracy (91.4%) of handwritten digits by artificial neural networks. These results provide insights into applications of synaptic transistors for future neuromorphic systems.