Humidity-induced synaptic plasticity of ZnO artificial synapses using peptide insulator for neuromorphic computing

doi:10.1016/j.jmst.2021.12.016

Abstract

Abstract:

Neuromorphic devices inspired by the human brain have attracted significant attention because of their excellent ability for cognitive and parallel computing. This study presents ZnO-based artificial synapses with peptide insulators for the electrical emulation of biological synapses. We demonstrated the dynamic responses of the device under various environmental conditions. The proton-conducting property of the tyrosine-rich peptide enables time-dependent responses under ambient conditions such that various aspects of synaptic behaviors are emulated by the devices. The transition from short-term memory to long-term memory is achieved via electrochemical doping of ZnO by protons. Furthermore, we demonstrate an image classification simulation using a multi-layer perceptron model to evaluate the potential of the device for use in neuromorphic computing. The neural network based on our device achieved a recognition accuracy of 87.47% for the MNIST handwritten digit images. This work proposes a novel device platform inspired by biosystems for brain-mimetic hardware systems.

Key words: Artificial synapse, Neuromorphic computing, Oxide semiconductor, Proton conductor, Artificial neural network

Min-Kyu Song, Hojung Lee, Jeong Hyun Yoon, Young-Woong Song, Seok Daniel Namgung, Taehoon Sung, Yoon-Sik Lee, Jong-Seok Lee, Ki Tae Nam, Jang-Yeon Kwon. Humidity-induced synaptic plasticity of ZnO artificial synapses using peptide insulator for neuromorphic computing[J]. J. Mater. Sci. Technol., 2022, 119: 150-155.

Figures/Tables 4

Fig. 1. (a) Schematic diagram of biological synapse between two neurons. (b) The corresponding schematic image of the ZnO/Y7C synaptic device. Pink and navy-blue arrows indicate presynaptic spikes and the corresponding EPSCs, respectively.

Fig. 2. Electrical characteristics of ZnO/Y7C synaptic device. Transfer characteristics of (a) ZnO/Y7C transistor and (b) ZnO/SiO2 transistor in different humidity conditions. (c) ON current and voltage hysteresis as functions of relative humidity for different insulators. Variation in the transfer curve as the external environment changes (d) from ambient to vacuum and (e) from vacuum to ambient. (f) Variation in voltage hysteresis as the external environment changes. Red and orange dots correspond to (d) and (e), respectively.

Fig. 3. Synaptic behaviors of the ZnO/Y7C synaptic device. (a) Comparison of EPSC responses in various humidity conditions. A pulse with an amplitude of 1 V and a width of 100 ms was applied as a presynaptic spike. (b) EPSC responses as a function of the pulse width of a presynaptic pulse. (c) Paired-pulse facilitation at 50% RH (top) and 0% RH (bottom). Two pulses with an amplitude of 1 V, a width of 100 ms, and an interval of 100 ms were applied. (d) PPF index as a function of the time interval between two pulses. PPF index was calculated by A2/A1, where A1 and A2 indicate the first and second peak, respectively. (e) The memory process of the human brain proposed by Atkinson and Shiffrin [37]. (f) Data retention following application of various stimuli. Inset shows the relaxation time constant calculated by fitting the decaying curves. The transition from STP to LTP was observed. (g) Potentiation and depression of the channel conductance induced by 30 positive pulses (red) and 30 negative pulses (blue), respectively.

Fig. 4. Image classification simulation using an artificial neural network. (a) Schematic illustration of the three-layered multilayer perceptron (MLP) model (784 × 300 × 10). The handwritten images from MNIST datasets (left) were used for training and evaluation. Each synaptic weight follows the updating rule of the ZnO/Y7C synaptic device. (b) Simulated image-classification accuracy of the artificial neural network based on the ZnO/Y7C synaptic device.

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