Nitrogen-Induced Enhancement of Synaptic Weight Reliability in Titanium Oxide-Based Resistive Artificial Synapse and Demonstration of the Reliability Effect on the Neuromorphic System

June Park, Euyjin Park, Sungho Kim, Hyun Yong Yu

Research output: Contribution to journalArticle

Abstract

With the significant technological developments in recent times, the neuromorphic system has been receiving considerable attention owing to its parallel arithmetic, low power consumption, and high scalability. However, the low reliability of artificial synapse devices disturbs calculations and causes inaccurate results in neuromorphic systems. In this paper, we propose a stable resistive artificial synapse (RAS) device with nitrogen-doped titanium oxide (TiOx:N)-based resistive switching (RS) memory. The TiOx:N-based RAS, compared to the TiOx-based RAS, demonstrates more stable RS characteristics in current-voltage (I-V) and pulse measurements. In terms of resistance variability, the TiOx:N-based RAS demonstrates five times lower resistance variability at 1.38%, compared to 6.68% with the TiOx-based RAS. In addition, we verified the relation between the neuromorphic system and the resistance reliability of the synapse device for the first time. The pattern recognition simulation is performed using an artificial neural network (ANN) consisting of artificial synapse devices using the Modified National Institute of Standards and Technology dataset. In the simulation, the ANN with the TiOx:N-based RAS exhibited significant pattern recognition accuracy of 64.41%, while the ANN with TiOx-based RAS demonstrated only low recognition accuracy of 22.07%. According to the results of subsequent simulations, the pattern recognition accuracy exponentially decreases when the resistance variability exceeds 5%. Therefore, for implementing a stable neuromorphic system, the synapse device in the neuromorphic system has to maintain low resistance variability. The proposed nitrogen-doped synapse device is suitable for neuromorphic systems because reliable resistance variability can be obtained with only simple process steps.

Original languageEnglish
Pages (from-to)32178-32185
Number of pages8
JournalACS Applied Materials and Interfaces
Volume11
Issue number35
DOIs
Publication statusPublished - 2019 Sep 4

Keywords

  • artificial neural network
  • artificial synapse
  • memristor
  • neuromorphic system
  • nitrogen doping
  • pattern recognition

ASJC Scopus subject areas

  • Materials Science(all)

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