Impact of the Crystal Phase of ZrO2 on Charge Trapping Memtransistor as Synaptic Device for Neural Network Application

Yu Che Chou, Chien Wei Tsai, Chin Ya Yi, Wan Hsuan Chung, Chao-Hsin Chien*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this work, we investigated the effects of the crystal phase of ZrO2 on charge trapping memtransistors (CTMTs) as synaptic devices for neural network applications. The ZrO2 deposited through thermal (t-ZrO2) atomic layer deposition (ALD) and plasma (p-ZrO2) ALD were analyzed using an X-ray diffractometer, which indicated that the t-ZrO2 consisted of pure cubic phase, whereas p-ZrO2 consisted of both cubic and tetragonal phases. Through X-ray photoelectron spectroscopy analysis, we then constructed the energy band diagram of the gate stacks. The \Delta \mathrm E_{C} of t- and p-ZrO2 with respect to tunneling and blocking Al2O3 were 1.84 and 1.19 eV respectively. Because of the relatively large \Delta \text{E}_{\mathrm{ C}} of t-ZrO2, the window of the flat band voltage ( \text{V}_{\mathrm{ FB}} ) shift extracted from charge trapping capacitors was enlarged by 591.9 mV more than the one using p-ZrO2 as the charge trapping layer. Retention was also improved by 10.4% after 10^{5} s in the t-ZrO2 case. Finally, we fabricated the CTMTs with the gate stack of the t-ZrO2 case and demonstrated their characteristics as synaptic devices. With the optimization of pulse schemes, we reduced the nonlinear factors of depression ( {\alpha } _{\mathrm{ d}} ) and potentiation ( {\alpha } _{\mathrm{ p}} ) from -6.72 and 6.47 to 0.03 and 0.01 respectively, enlarged the ON/OFF ratio from 15.6 to 70.4 and increased the recognition accuracy from 27.6% to 86.5% simultaneously.

Original languageEnglish
Article number9091548
Pages (from-to)572-576
Number of pages5
JournalIEEE Journal of the Electron Devices Society
Volume8
DOIs
StatePublished - 2020

Keywords

  • Germanium
  • high-κ dielectrics
  • multilayer perceptron
  • neural network hardware
  • synaptic device
  • zirconium oxide

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