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.

Highlights

  • With the recent development of artificial intelligence (AI), implementing AI in end-point devices has become an extremely active research topic in both academia and industry

  • For off-the-shelf hardware platforms, NNs are primarily implemented in von Neumann architecture such as graphic processing units [2], field-programmable gate arrays [3], or neural processing units [4] to boost the operation of their neural networks

  • The problem with NN implementation in von Neumann architecture is more in data movement between arithmetic units and memory [5], in which energy and bandwidth are largely consumed by movement of data from the memory to the arithmetic unit or vice versa, than in computation [6]

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Summary

Introduction

With the recent development of artificial intelligence (AI), implementing AI in end-point devices has become an extremely active research topic in both academia and industry. CHOU et al.: IMPACT OF CRYSTAL PHASE OF ZrO2 ON CHARGE TRAPPING MEMTRANSISTOR AS SYNAPTIC DEVICE We fabricated charge trapping memtransistors (CTMTs) with ZrO2 as the charge trapping layer (CTL).

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