DOI: https://doi.org/10.29363/nanoge.matnec.2022.018
Publication date: 23rd February 2022
Brain-like architectures for future computing are touted as the next standard for processing information [1]. These neuromorphic architectures can provide a more efficient platform than traditional technologies for specific tasks. Nanoscale networks of such standard technologies have been investigated with respect to nanowire networks or nanoparticle systems [2], [3]. Such networks develop due to the formation of percolating networks inside tunnel junctions which emulate a biological neuron [2]. But comparable efforts have not been performed on technologies that can be scaled up to large area systems. In this work, we demonstrate a scalable 2D material system comprised of CVD-grown hBN on Cu contacted with Ag. Such a structure gives rise to atomic-scale networks due to the diffusion of Ag inside the hBN matrix which also acts as a resistive switching memory device. The formation of nanoscale networks establishes brain-like network dynamics which show critical avalanches as those observed in cortical tissue structures [4]. We observe the avalanche behavior in both low resistance (LRS), as well as high resistance (HRS) states corroborating the role of Ag diffusion in the formation of nanoscale networks leading to change in channel resistance. Reports have suggested that systems with nanoscale networks exhibit critical behavior in a self-organized manner where the distribution function of quantized conductance (∆G) and inter-event intervals (IEI) follow power laws. We observe power-law fits for ∆G and IEI with exponents of the order of 2.45 and 1.54 respectively. Such a range of exponents has been previously observed in neuronal avalanches [4]. The avalanche behavior observed in the Ag-hBN system also exhibits scale-free dynamics as they are autocorrelated and demonstrate spatial and temporal long-range correlation. Thus, we report the first of its kind brain-like avalanche behavior in a 2D material system comprising of Ag-hBN, this kind of system can be scaled up to form large-area devices and the case of hBN being a 2D material allows for engineered structures for future applications in neuromorphic computing.
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