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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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Abstract
Ferroelectric (FE) field-effect transistors are interesting for their non-destructive readout characteristic and energy efficiency but are difficult to integrate on silicon platforms. Here, FE ZrxAl₁₋ₓOY (ZAO) is demonstrated by compressive strain in contact with ZnO. The metal-ferroelectric-semiconductor-metal capacitor exhibits a substantial remnant polarization of 15.2 µC cm², along with a bowknot-like anti-clockwise hysteresis in the capacitance curves. The FE-ZAO gated ZnO thin-film transistor presents 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). The grazing incidence X-ray diffraction and scanning transmission electron microscopy analyses reveal the ferroelectric rhombohedral phase (space group R3m) in the nanocrystal ZAO, containing an angle of ≈71.7° between the [111] and [11-1] directions with d₁₁₁-spacing of 3.037 Å and d₁₁₋₁-spacing of 2.927 Å. Finally, the memory and neuromorphic applications are analyzed by demonstrating multi-level memory and synaptic weight performance with a high learning accuracy of 91.82%.
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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