Sol–Gel-Processed Y2O3 Multi-level Resistive Memory Cells for Neural Networks

Taehun Lee; Hae-In Kim; Yoonjin Cho; Sangwoo Lee; Won-Yong Lee; Jin-Hyuk Bae; In Man Kang; Kwangeun Kim; Sin-Hyung Lee; Jaewon Jang

doi:10.20944/preprints202307.1776.v1

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preprints.org > engineering > electrical and electronic engineering > doi: 10.20944/preprints202307.1776.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Sol–Gel-Processed Y2O3 Multi-level Resistive Memory Cells for Neural Networks

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Version 1 : Received: 26 July 2023 / Approved: 26 July 2023 / Online: 27 July 2023 (08:33:25 CEST)

A peer-reviewed article of this Preprint also exists.

Lee, T.; Kim, H.-I.; Cho, Y.; Lee, S.; Lee, W.-Y.; Bae, J.-H.; Kang, I.-M.; Kim, K.; Lee, S.-H.; Jang, J. Sol–Gel-Processed Y₂O₃ Multilevel Resistive Random-Access Memory Cells for Neural Networks. Nanomaterials 2023, 13, 2432. Lee, T.; Kim, H.-I.; Cho, Y.; Lee, S.; Lee, W.-Y.; Bae, J.-H.; Kang, I.-M.; Kim, K.; Lee, S.-H.; Jang, J. Sol–Gel-Processed Y2O3 Multilevel Resistive Random-Access Memory Cells for Neural Networks. Nanomaterials 2023, 13, 2432.

Abstract

Yttrium oxide (Y2O3) resistive random-access memory (RRAM) devices were fabricated using the sol–gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics, without requiring a high voltage forming process. The effect of current compliance on the Y2O3 RRAM devices was investigated and revealed that as the set current compliance values increased, the resistance values gradually decreased. Consequently, intermediate resistance values were obtained, which are suitable for multi-level cell (MLC) switching. The fabricated Y2O3 RRAM devices were capable of functioning as an MLC with a capacity of 2 bits in one cell, utilizing three low resistance states and one common high resistance state. The potential of the Y2O3 RRAM for neural networks was further explored through numerical simulations. Hardware neural networks based on the Y2O3 RRAM demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This suggests that the proposed RRAM has the potential to be utilized as a memory component in practical neuromorphic systems.

Keywords

sol-gel; Y2O3; RRAM; Multi level cell

Subject

Engineering, Electrical and Electronic Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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