Please use this identifier to cite or link to this item: 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

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetric Badge: