Synapse behavior characterization and physical mechanism of a TiN/SiOx/p-Si tunneling memristor device. Issue 6 (17th January 2019)

• Record Type: Journal Article
• Authors: Zhou, Zhenyu; Yan, Xiaobing; Zhao, Jianhui; Lu, Chao; Ren, Deliang; Lu, Nianduan; Wang, Jingjuan; Zhang, Lei; Li, Xiaoyan; Wang, Hong; Zhao, Mengliu
• Is Part Of: Journal of materials chemistry. Volume 7:Issue 6(2019)
• Page Start: 1561
• Page End: 1567
• Publication Date: 2019-01-17
• Subjects: Materials -- Periodicals; Chemistry, Analytic -- Periodicals; Optical materials -- Research -- Periodicals; Electronics -- Materials -- Research -- Periodicals; 543.0284
• DOI: 10.1039/c8tc04903c
• Languages: English
• ISSNs: 2050-7526

Abstract

Abstract : The demand for massive deep learning neural networks has driven the development of nanoscale memristor devices, which perform brain-inspired neuromorphic computing. Abstract : The demand for large-scale deep learning neural networks has driven the development of nanoscale memristor devices, which perform brain-inspired neuromorphic computing. In this study, we present an electroforming free-tunneling junction device based on a TiN/SiO x /p-Si structure. This proposed device exhibited artificial synapse behaviors via applying pulse train. The impacts of pulse amplitude, width and interval were investigated for gradually modulating the conduction of the device. Particularly, short-term plasticity (STP) could be continually modulated by successive voltage sweeps or pulses. Symbolizing the relaxation time of memory ability could emulate the excitatory postsynaptic current (EPSC) of different pulse models. It is proposed that the variable-range hopping (VRH) and Fowler–Nordheim (FN) tunneling theories are responsible for gradual conduction change to mimic the bio-synapse based in the TiN/SiO x /p-Si memristor. This study provides further insights into the physical mechanisms of the gradual change in resistance for mimicking bio-synapse.