HfTaOx Rectifying Layer for HfO x-Based RRAM for High-Accuracy Neuromorphic Computing Applications

Ting Jia Chang, Hoang Hiep Le, Cheng Ying Li, Sheng Yuan Chu, Darsen D. Lu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In this study, a Ta2O5-doped HfOx(HfTaOx) thin film was deposited by cosputtering to serve as the rectifying layer for HfOx-based resistive random-access memory (RRAM) with a final structure of Pt/HfOx/HfTaOx/TiN/SiO2/Si. Incorporating the appropriate proportion of lattice and nonlattice O in the rectifying layer enabled forming-free RRAM operation. Moreover, by modifying the compliance current and making use of the deep reset operation, multilevel resistance states were realized. In neuromorphic computing, when mimicking artificial synapses, potentiation and depression were successfully induced, and low nonlinearity was demonstrated, implying efficient weight modulation and reduced energy and time for neural network training. Software-comparable Modified National Institute of Standards and Technology (MNIST) handwritten digit database inference accuracy (97.54%) was achieved for an RRAM-based fully connected neural network with the HfTaOxrectifying layer.

Original languageEnglish
Pages (from-to)2566-2573
Number of pages8
JournalACS Applied Electronic Materials
Volume5
Issue number5
DOIs
Publication statusPublished - 2023 May 23

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Materials Chemistry
  • Electrochemistry

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