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https://dspace.iiti.ac.in/handle/123456789/16115
Title: | Ultralow Powered 2D MoS2-Based Memristive Crossbar Array for Synaptic Applications |
Authors: | Yadav, Saurabh Patel, Chandrabhan Dubey, Mayank Mukherjee, Shaibal |
Keywords: | artificial synapses;crossbar array;low power;MoS<sub>2</sub>;reproducibility |
Issue Date: | 2025 |
Publisher: | American Chemical Society |
Citation: | Yadav, S., Patel, C., Rajbhar, M. K., Dubey, M., Kumbhar, D. D., Dongale, T. D., Khandelwal, V., Yuvaraja, S., Li, X., & Mukherjee, S. (2025). Ultralow Powered 2D MoS<inf>2</inf>-Based Memristive Crossbar Array for Synaptic Applications. ACS Applied Materials and Interfaces, 17(18), 26871–26880. https://doi.org/10.1021/acsami.5c00688 |
Abstract: | Two-dimensional materials are increasingly integral to beyond-CMOS electronics, facilitating the development of emerging memristive device technology for information storage and neuromorphic computing. Despite their emergence, some critical challenges including low device yield, substantial device-to-device (D2D), and cycle-to-cycle (C2C) variability factors hinder the development of high-density memristive devices for future low-power electronic applications. Here, we demonstrate a memristive crossbar array (MCA) in which multilayer 2D MoS2 acts as a resistive switching layer that offers lower switching voltages with a few microseconds pulse width. Additionally, the use of 2D MoS2 further excels in integration density and energy efficiency, which significantly helps to achieve a device yield of 94%. Moreover, the 2D MoS2 controlled growth process ensures the uniformity of MoS2 layers across a (10 × 10) crossbar array that enhances the stability of fabricated MCA’s having minimal variability in device switching voltages (VSET: 4.16% and VRESET: 3.60%). The fabricated devices show excellent endurance (∼24,000 cycles) and retention (1.6 × 106 s). Furthermore, due to lower switching voltages and fast switching speed, the fabricated devices consume 53 pW power and 53 aJ energy, making them more energy-efficient and achieving an impressive 97.79% accuracy in MNIST digit recognition through synaptic behavior simulation. © 2025 American Chemical Society. |
URI: | https://doi.org/10.1021/acsami.5c00688 https://dspace.iiti.ac.in/handle/123456789/16115 |
ISSN: | 1944-8244 |
Type of Material: | Journal Article |
Appears in Collections: | Department of Electrical Engineering |
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