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An artificial synaptic transistor using an α-In2Se3van der Waals ferroelectric channel for pattern recognition

Mohta, N and Rao, A and Remesh, N and Muralidharan, R and Nath, DN (2021) An artificial synaptic transistor using an α-In2Se3van der Waals ferroelectric channel for pattern recognition. In: RSC Advances, 11 (58). pp. 36901-36912.

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Official URL: https://doi.org/10.1039/d1ra07728g


Despite being widely investigated for their memristive behavior, ferroelectrics are barely studied as channel materials in field-effect transistor (FET) configurations. In this work, we use multilayer α-In2Se3 to realize a ferroelectric channel semiconductor FET, i.e., FeS-FET, whose gate-triggered and polarization-induced resistive switching is then exploited to mimic an artificial synapse. The FeS-FET exhibits key signatures of a synapse such as excitatory and inhibitory postsynaptic current, potentiation/depression, and paired pulsed facilitation. Multiple stable conductance states obtained by tuning the device are then used as synaptic weights to demonstrate pattern recognition by invoking a hidden layer perceptron model. Detailed artificial neural network (ANN) simulations are performed on binary scale MNIST data digits, invoking 784 input (28 � 28 pixels) and 10 output neurons which are used in the training of 42�000 MNIST data digits. By updating the synaptic weights with conductance weight values on 18�000 digits, we achieved a successful recognition rate of 93 on the testing data. Introduction of 0.10 variance of noise pixels results in an accuracy of more than 70 showing the strong fault-tolerant nature of the conductance states. These synaptic functionalities, learning rules, and device to system-level simulation results based on α-In2Se3 could facilitate the development of more complex neuromorphic hardware systems based on FeS-FETs. © The Royal Society of Chemistry.

Item Type: Journal Article
Publication: RSC Advances
Publisher: Royal Society of Chemistry
Additional Information: The copyright for this article belongs to Authors
Keywords: Ferroelectric materials; Ferroelectricity; Neural networks; Pattern recognition; Pixels, Artificial synapse; Channel materials; Conductance state; Field-effect transistor; In-field; Memristive behavior; Resistive switching; Semiconductor field-effect transistors; Synaptic weight; Van der Waal, Field effect transistors
Department/Centre: Division of Interdisciplinary Sciences > Centre for Nano Science and Engineering
Date Deposited: 13 Dec 2021 11:30
Last Modified: 13 Dec 2021 11:30
URI: http://eprints.iisc.ac.in/id/eprint/70721

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