Yttrium oxide (YO) resistive random-access memory (RRAM) devices were fabricated using the sol-gel process on indium tin oxide/glass substrates. These devices exhibited conventional bipolar RRAM characteristics without requiring a high-voltage forming process. The effect of current compliance on the YO RRAM devices was investigated, and the results revealed that the resistance values gradually decreased with increasing set current compliance values. By regulating these values, the formation of pure Ag conductive filament could be restricted. The dominant oxygen ion diffusion and migration within YO leads to the formation of oxygen vacancies and Ag metal-mixed conductive filaments between the two electrodes. The filament composition changes from pure Ag metal to Ag metal mixed with oxygen vacancies, which is crucial for realizing multilevel cell (MLC) switching. Consequently, intermediate resistance values were obtained, which were suitable for MLC switching. The fabricated YO RRAM devices could function as a MLC with a capacity of two bits in one cell, utilizing three low-resistance states and one common high-resistance state. The potential of the YO RRAM devices for neural networks was further explored through numerical simulations. Hardware neural networks based on the YO RRAM devices demonstrated effective digit image classification with a high accuracy rate of approximately 88%, comparable to the ideal software-based classification (~92%). This indicates that the proposed RRAM can be utilized as a memory component in practical neuromorphic systems.
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http://dx.doi.org/10.3390/nano13172432 | DOI Listing |
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January 2025
Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Nijenborgh 9, 9747 AG Groningen, The Netherlands.
The increase in computational power demand led by the development of Artificial Intelligence is rapidly becoming unsustainable. New paradigms of computation, which potentially differ from digital computation, together with novel hardware architecture and devices, are anticipated to reduce the exorbitant energy demand for data-processing tasks. Memristive systems with resistive switching behavior are under intense research, given their prominent role in the fabrication of memory devices that promise the desired hardware revolution in our intensive data-driven era.
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Department of Physics, Shanghai Jiao Tong University, 800 Dong Chuan Road, Minhang Area, Shanghai 200240, Shanghai, 200240, CHINA.
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Peter Gruenberg Institut (PGI-7), Forschungszentrum Juelich GmbH, Juelich, Germany.
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Department of Electronic Engineering, Tsinghua University, Beijing, China.
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View Article and Find Full Text PDFNano Lett
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School of Materials Science and Engineering, State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, National Engineering Research Center for Advanced Polymer Processing Technology, Zhengzhou University, Zhengzhou 450001, China.
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