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Transistors with ferroelectric ZrxAl₁₋ₓOY crystallized by ZnO growth for multi-level memory and neuromorphic computing

Engineering and Technology

Transistors with ferroelectric ZrxAl₁₋ₓOY crystallized by ZnO growth for multi-level memory and neuromorphic computing

M. M. Islam, A. Ali, et al.

This research showcases the innovative development of ferroelectric ZrxAl₁₋ₓOY, revealing promising results for non-destructive readout and energy-efficient applications. The integration of this material with ZnO has led to impressive performances in memory and neuromorphic computing, with a learning accuracy of 91.82%. The work was conducted by Md Mobaidul Islam, Arqum Ali, Chanju Park, Taebin Lim, Dong Yeon Woo, Joon Young Kwak, and Jin Jang.

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Playback language: English
Abstract
Ferroelectric (FE) field-effect transistors (FETs) are attractive for their non-destructive readout and energy efficiency but are challenging to integrate on silicon platforms. This research demonstrates FE ZrxAl₁₋ₓOY (ZAO) by utilizing compressive strain in contact with ZnO. The metal-ferroelectric-semiconductor-metal (MFSM) capacitor shows a remnant polarization of 15.2 µC cm², and a bowknot-like anti-clockwise hysteresis in capacitance curves. The FE-ZAO gated ZnO thin-film transistor (TFT) exhibits a large memory window (3.84 V), low subthreshold swing (55 mV dec⁻¹), high Ion/IOFF ratio (≈10⁷), and low off-state current (1 pA). GI-XRD and HAADF-STEM analyses reveal the ferroelectric rhombohedral phase (space group R3m) in nanocrystalline ZAO. Multi-level memory and synaptic weight performance are demonstrated, achieving a high learning accuracy of 91.82% in neuromorphic computing applications.
Publisher
Communications Materials
Published On
Apr 15, 2024
Authors
Md Mobaidul Islam, Arqum Ali, Chanju Park, Taebin Lim, Dong Yeon Woo, Joon Young Kwak, Jin Jang
Tags
ferroelectric transistors
ZrxAl₁₋ₓOY
ZnO
memory performance
neuromorphic computing
energy efficiency
capacitance hysteresis

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