Two‐Dimensional MXene Synapse for Brain‐Inspired Neuromorphic Computing. Issue 34 (17th July 2021)

• Record Type: Journal Article
• Authors: Ju, Jae Hyeok; Seo, Seunghwan; Baek, Sungpyo; Lee, Dongyoung; Lee, Seojoo; Lee, Taeran; Kim, Byeongchan; Lee, Je‐Jun; Koo, Jiwan; Choo, Hyeongseok; Lee, Sungjoo; Park, Jin‐Hong
• Is Part Of: Small. Volume 17:Issue 34(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-07-17
• Subjects: artificial synapse -- brain‐inspired neuromorphic computing -- convolutional neural network -- two‐dimensional MXene; Nanotechnology -- Periodicals; Nanoparticles -- Periodicals; Microtechnology -- Periodicals; 620.5
• DOI: 10.1002/smll.202102595
• Languages: English
• ISSNs: 1613-6810

Abstract

Abstract: MXenes, an emerging class of two‐dimensional (2D) transition metal carbides and nitrides, have attracted wide attention because of their fascinating properties required in functional electronics. Here, an atomic‐switch‐type artificial synapse fabricated on Ti3 C2 Tx MXene nanosheets with lots of surface functional groups, which successfully mimics the dynamics of biological synapses, is reported. Through in‐depth analysis by X‐ray photoelectron spectroscopy, transmission electron microscopy, and energy dispersive X‐ray spectroscopy, it is found that the synaptic dynamics originated from the gradual formation and annihilation of the conductive metallic filaments on the MXene surface with distributed functional groups. Subsequently, via training and inference tasks using a convolutional neural network for the Canadian‐Institute‐For‐Advanced‐Research‐10 dataset, the applicability of the artificial MXene synapse to hardware neural networks is demonstrated. Abstract : An atomic‐switch‐type artificial synapse fabricated on Ti3 C2 TX MXene nanosheets with lots of surface functional groups successfully mimics the dynamics of biological synapse. The synaptic dynamics originate from the gradual formation and annihilation of the conductive metallic filaments on the MXene surface with distributed functional groups. Subsequently, its feasibility for an HW‐NN with learning ability is demonstrated using a convolutional neural network composed MXene synapse devices.