Tracing the Si Dangling Bond Nanopathway Evolution ina-SiNx:H Resistive Switching Memory by the Transient Current

• Published in: Nanomaterials (Basel, Switzerland) Vol. 13; no. 1; p. 85
• Main Authors: Chen, Tong, Leng, Kangmin, Ma, Zhongyuan, Jiang, Xiaofan, Chen, Kunji, Li, Wei, Xu, Jun, Xu, Ling
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-01-2023; MDPI
• Subjects: Artificial intelligence; Controllability; Electrodes; Evolution; Random access memory; Ratios; resistive switching memory; Silicon nitride; Switching; Synapses; Thickness; Transient current; trap state
• ISSN: 2079-4991; 2079-4991
• DOI: 10.3390/nano13010085

With the big data and artificial intelligence era coming, SiNx-based resistive random-access memories (RRAM) with controllable conductive nanopathways have a significant application in neuromorphic computing, which is similar to the tunable weight of biological synapses. However, an effective way to detect the components of conductive tunable nanopathways in a-SiNx:H RRAM has been a challenge with the thickness down-scaling to nanoscale during resistive switching. For the first time, we report the evolution of a Si dangling bond nanopathway in a-SiNx:H resistive switching memory can be traced by the transient current at different resistance states. The number of Si dangling bonds in the conducting nanopathway for all resistive switching states can be estimated through the transient current based on the tunneling front model. Our discovery of transient current induced by the Si dangling bonds in the a-SiNx:H resistive switching device provides a new way to gain insight into the resistive switching mechanism of the a-SiNx:H RRAM in nanoscale.