Realizing avalanche criticality in neuromorphic networks on a 2D hBN platform

Rao, A and Sanjay, S and Dey, V and Ahmadi, M and Yadav, P and Venugopalrao, A and Bhat, N and Kooi, B and Raghavan, S and Nukala, P (2023) Realizing avalanche criticality in neuromorphic networks on a 2D hBN platform. In: Materials Horizons, 10 (11). 5235 -5245.

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Abstract

Networks and systems which exhibit brain-like behavior can analyze information from intrinsically noisy and unstructured data with very low power consumption. Such characteristics arise due to the critical nature and complex interconnectivity of the brain and its neuronal network. We demonstrate a system comprising of multilayer hexagonal boron nitride (hBN) films contacted with silver (Ag), which can uniquely host two different self-assembled networks, which are self-organized at criticality (SOC). This system shows bipolar resistive switching between the high resistance state (HRS) and the low resistance state (LRS). In the HRS, Ag clusters (nodes) intercalate in the van der Waals gaps of hBN forming a network of tunnel junctions, whereas the LRS contains a network of Ag filaments. The temporal avalanche dynamics in both these states exhibit power-law scaling, long-range temporal correlation, and SOC. These networks can be tuned from one to another with voltage as a control parameter. For the first time, two different neural networks are realized in a single CMOS compatible, 2D material platform. © 2023 The Royal Society of Chemistry.

Item Type:	Journal Article
Publication:	Materials Horizons
Publisher:	Royal Society of Chemistry
Additional Information:	The copyright for this article belongs to the Royal Society of Chemistry
Keywords:	Aluminum nitride; Criticality (nuclear fission); III-V semiconductors; Low power electronics; Multilayer films; Multilayer neural networks; Multilayers; Network layers; Silver; Tunnel junctions; Van der Waals forces, 2D-hexagonal; High-resistance state; Low-power consumption; Low-resistance state; Lower-power consumption; Networks and systems; Neuromorphic networks; Noisy data; Self-organised; Unstructured data, Neurons
Department/Centre:	Division of Interdisciplinary Sciences > Centre for Nano Science and Engineering
Date Deposited:	20 Dec 2023 04:07
Last Modified:	20 Dec 2023 04:07
URI:	https://eprints.iisc.ac.in/id/eprint/83521

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