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Demonstration of intrinsic STDP learning capability in all-2D multi-state MoS2memory and its application in modelling neuromorphic speech recognition

Paul, T and Mukundan, AA and Tiwari, KK and Ghosh, A and Singh Thakur, C (2021) Demonstration of intrinsic STDP learning capability in all-2D multi-state MoS2memory and its application in modelling neuromorphic speech recognition. In: 2D Materials, 8 (4).

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Official URL: https://doi.org/10.1088/2053-1583/ac210a


The human brain can be characterized by its large number of adaptive synapses, connecting billions of neurons capable of both learning and perceiving the environment. Neuromorphic computing, based on brain-inspired principles, is a promising technology, to build low-power, distributed, fault-tolerant intelligent systems mainly for perception tasks. Here, we demonstrate the intrinsic capability of floating gate (FG) MoS2 device (MoS2 FG-FET) to model the spike time dependent plasticity (STDP) learning rule that is based on the transient response of the MoS2 channel to spikes applied to the source and gate leads. We implemented the STDP learning protocol in a neuromorphic speech recognition system (NSRS), inspired by the human auditory pathway, for various auditory recognition tasks. Our proposed NSRS consists of a cochlea model, an unsupervised feature learning stage, and a simple linear classifier. The unsupervised learning stage uses the biologically plausible STDP learning in novel two-dimensional MoS2 FG-FET memory which circumvents the requirement of any other learning circuitry. Demonstration of STDP modelling in two-dimensional (2D) MoS2 is an important step towards incorporating 2D architectures for reduced device footprints in neuromorphic learning circuits. © 2021 IOP Publishing Ltd.

Item Type: Journal Article
Publication: 2D Materials
Publisher: IOP Publishing Ltd
Additional Information: The copyright for this article belongs to IOP Publishing Ltd
Keywords: Audition; CMOS integrated circuits; E-learning; Intelligent systems; Learning systems; Molybdenum compounds; Neurons; Speech recognition; Students; Transient analysis, Beyond CMOS; Brain-inspired; Brain-inspired learning; Electronic cochlea; Emerging device; Memristor; MoS2memristor; Neuromorphic; Neuromorphic computing; Spike-time dependent plasticities, Brain
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Division of Interdisciplinary Sciences > Centre for Nano Science and Engineering
Division of Physical & Mathematical Sciences > Physics
Date Deposited: 28 Nov 2021 09:48
Last Modified: 28 Nov 2021 09:48
URI: http://eprints.iisc.ac.in/id/eprint/70334

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