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Title: | Progress, Perspectives, and Future Outlook of Yttrium Oxide-Based Memristive Devices for Data Storage, Multibit Programming, and Neuromorphic Computing: A Systematic Review |
Authors: | Mukherjee, Shaibal |
Keywords: | data storage;memristive device;neuromorphic computing;resistive RAM;synaptic learning;Y<sub>2</sub>O<sub>3</sub> |
Issue Date: | 2025 |
Publisher: | American Chemical Society |
Citation: | Dahiya, A., Kumar, S., Rani, S., & Mukherjee, S. (2025). Progress, Perspectives, and Future Outlook of Yttrium Oxide-Based Memristive Devices for Data Storage, Multibit Programming, and Neuromorphic Computing: A Systematic Review. ACS Applied Electronic Materials. https://doi.org/10.1021/acsaelm.5c01025 |
Abstract: | In the current decade, metal-oxide-based nonvolatile memristive devices have emerged as prominent candidates for numerous potential applications, including next-generation nonvolatile memory, synaptic learning devices, analog/neuromorphic computing, hardware security, in-memory computation, and logic operations. These memristive device applications are possible due to their simple device structure, complementary metal-oxide semiconductor (CMOS)-compatible fabrication process, nanoscale device scalability, fast switching speed, low power/energy consumption, three-dimensional (3D) device architecture acceptability, and low device-to-device (D2D) and ultralow cycle-to-cycle (C2C) variabilities in the crossbar array architecture. Although various transition metal-oxide materials have been utilized to fabricate the memristive devices, Y2O3 has gained significant attention to realize memristive devices because of its low-temperature growth, high band gap, high dielectric constant, and low lattice mismatch with silicon. Additionally, Y2O3-based memristive devices with different electrode systems (including active, reactive, and inert) show promising unipolar/bipolar resistive switching characteristics with remarkable resistance ratios, filamentary and interfacial resistive switching mechanisms, excellent endurance and retention properties, multibit data storage, low-voltage synaptic learning with various neuromorphic responses, and the lowest value of coefficient of variability in both D2D and C2C. These aforementioned capabilities of Y2O3-based memristive devices further lead to advanced research directions by incorporating material engineering, device physics, and growth system approaches that can help achieve desirable milestones in memristive device-based energy-efficient neuromorphic computing. Therefore, this review comprehensively articulates the overall development of Y2O3-based memristive devices to date using different growth methods for various potential applications. © 2025 American Chemical Society. |
URI: | https://dx.doi.org/10.1021/acsaelm.5c01025 https://dspace.iiti.ac.in:8080/jspui/handle/123456789/16387 |
ISSN: | 2637-6113 |
Type of Material: | Journal Article |
Appears in Collections: | Department of Electrical Engineering |
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