Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/16252
Title: Scalable oxide-based memcapacitive crossbar arrays for 1 Kb neuromorphic memory
Authors: Paul, Animesh
Yadav, Saurabh
Hindoliya, Lokesh Kumar
Dubey, Mayank
Mukherjee, Shaibal
Keywords: 1 Kb;crossbar array;memcapacitor;neuromorphic;Y<sub>2</sub>O<sub>3</sub>
Issue Date: 2025
Publisher: Institute of Physics
Citation: Paul, A., Yadav, S., Rokade, K. A., Shembade, U. v, Hindoliya, L. K., Dubey, M., Dongale, T. D., Chueh, Y.-L., & Mukherjee, S. (2025). Scalable oxide-based memcapacitive crossbar arrays for 1 Kb neuromorphic memory. Journal of Physics D: Applied Physics. https://doi.org/10.1088/1361-6463/add8a0
Abstract: Memcapacitors are being investigated as potential candidates for high-density data storage. However, developing high-density memcapacitive devices for complex applications is challenging due to higher cycle-to-cycle (C2C) and device-to-device (D2D) variations. In this work, we demonstrate the fabrication of high-density (32 × 32) memcapacitor crossbar arrays achieving device sizes as small as 10 µm × 10 µm using yttrium oxide (Y2O3) as the switching material, deposited via dual ion beam sputtering system. The arrays exhibit low C2C variability (1.01% for VSET and 2.56% for VRESET) and low D2D variability (1.70% for VSET and 4.83% for VRESET). The Y2O3-based crossbar arrays also display robust switching behavior, with a high on/off current ratio (IRATIO > 150), excellent endurance (∼18 000) cycles, long retention ∼160 000 s) and low power consumption of 17 pW. Electrochemical impedance spectroscopy has been utilized to examine the electrical behavior, providing insights into device performance. Neuromorphic functionalities are further demonstrated through potentiation (learning) and depression (forgetting) mechanisms. Moreover, a (16 × 16) array subset is employed to electrically encode random alphabet patterns and exhibit neuromorphic learning capabilities, underscoring the potential of these devices for analog and neuromorphic applications. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
URI: https://dx.doi.org/10.1088/1361-6463/add8a0
https://dspace.iiti.ac.in:8080/jspui/handle/123456789/16252
ISSN: 0022-3727
Type of Material: Journal Article
Appears in Collections:Centre for Advanced Electronics (CAE)
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: