高稳定性氧化镓基忆阻器的构筑和神经形态计算应用

doi:10.11896/cldb.25020107

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Abstract Memristors, promising nanoelectronic devices with the ability to emulate biological synaptic functionalities, have great potential for use in high-performance neuromorphic computing in the post-Moore era. However, realization of high-performance artificial synaptic devices with robustness and low-power consumption remains faces great challenges. Here, we report a memristor based on amorphous gallium oxide (GaO_x) memristive layer structure, deposited via magnetron sputtering on fluorine-doped tin oxide (FTO) substrates. The device exhibits excellent multilevel storage properties, including retention times approaching 10⁴ s, cycling endurance exceeding 150 cycles, and highly linear conductance modulation with over 100 distinct conductance states. Furthermore, classical “Pavlov’s dog” conditioned reflex behavior was successfully emulated. In addition, a convolutional neural network (CNN) based on an Ag/GaO_x/FTO memristor array was constructed, effectively recognizing 60 000 handwritten digit images with an accuracy of 91.34%. Our findings underscore the significant potential of the developed Ag/GaO_x/FTO memristor for applications in neuromorphic computing.

Key words： memristor GaO_x neural synapse convolutional neural network

Published: Online: 2025-05-29

CLC number:

TN60

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	Articles by authors
	WANG Miaoru
	CHAI Xiaojie
	YAN Zeyu
	SUO Dingding
	JI Jianlong

Cite this article:

WANG Miaoru,CHAI Xiaojie,YAN Zeyu, et al. Development and Construction of High-stability Gallium Oxide-based Memristors for Neuromorphic Computing Applications[J]. Materials Reports, 2025, 39(11): 25020107-6.

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https://www.mater-rep.com/EN/10.11896/cldb.25020107 OR https://www.mater-rep.com/EN/Y2025/V39/I11/25020107

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