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Hybrid architecture based on two-dimensional memristor crossbar array and CMOS integrated circuit for edge computing

Kumar, P and Zhu, K and Gao, X and Wang, S-D and Lanza, M and Thakur, CS (2022) Hybrid architecture based on two-dimensional memristor crossbar array and CMOS integrated circuit for edge computing. In: npj 2D Materials and Applications, 6 (1).

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Official URL: https://doi.org/10.1038/s41699-021-00284-3


The fabrication of integrated circuits (ICs) employing two-dimensional (2D) materials is a major goal of semiconductor industry for the next decade, as it may allow the extension of the Moore�s law, aids in in-memory computing and enables the fabrication of advanced devices beyond conventional complementary metal-oxide-semiconductor (CMOS) technology. However, most circuital demonstrations so far utilizing 2D materials employ methods such as mechanical exfoliation that are not up-scalable for wafer-level fabrication, and their application could achieve only simple functionalities such as logic gates. Here, we present the fabrication of a crossbar array of memristors using multilayer hexagonal boron nitride (h-BN) as dielectric, that exhibit analog bipolar resistive switching in >96 of devices, which is ideal for the implementation of multi-state memory element in most of the neural networks, edge computing and machine learning applications. Instead of only using this memristive crossbar array to solve a simple logical problem, here we go a step beyond and present the combination of this h-BN crossbar array with CMOS circuitry to implement extreme learning machine (ELM) algorithm. The CMOS circuit is used to design the encoder unit, and a h-BN crossbar array of 2D hexagonal boron nitride (h-BN) based memristors is used to implement the decoder functionality. The proposed hybrid architecture is demonstrated for complex audio, image, and other non-linear classification tasks on real-time datasets. © 2022, The Author(s).

Item Type: Journal Article
Publication: npj 2D Materials and Applications
Publisher: Nature Research
Additional Information: The copyright for this article belongs to Authors
Keywords: Boron nitride; CMOS integrated circuits; Computation theory; Computer circuits; Edge computing; Fabrication; Machine learning; Memory architecture; Metals; MOS devices; Network architecture; Neural networks; Nitrides; Oxide semiconductors; Semiconductor device manufacture; Timing circuits, Architecture-based; Complementary metal-oxide-semiconductor technologies; Crossbar arrays; Edge computing; Hybrid architectures; Mechanical exfoliation; Memristor; Semiconductor industry; Simple++; Two-dimensional, Memristors
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited: 08 Feb 2022 10:23
Last Modified: 08 Feb 2022 10:23
URI: http://eprints.iisc.ac.in/id/eprint/71220

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