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

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)2566-2573
頁數8
期刊ACS Applied Electronic Materials
5
發行號5
DOIs
出版狀態Published - 2023 5月 23

All Science Journal Classification (ASJC) codes

  • 電子、光磁材料
  • 材料化學
  • 電化學

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