Configurable Synaptic and Stochastic Neuronal Functions in ZnTe‐Based Memristor for an RBM Neural Network

This study presents findings that demonstrate the possibility of simplifying neural networks by inducing multifunctionality through separate manipulation within a single material. Herein, two‐terminal memristor W/ZnTe/W devices implemented a multifunctional memristor comprising a selector, synapse,...

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Bibliographic Details
Published in:Advanced science 2024-11, Vol.11 (42), p.e2405768-n/a
Main Authors: Heo, Jungang, Kim, Seongmin, Kim, Sungjun, Kim, Min‐Hwi
Format: Article
Language:English
Subjects:
Data processing
Energy consumption
Metal oxides
Neural networks
neuromorphic system
Neurons
OTS
RBM
Spectrum analysis
stochastic neuron
synaptic devices
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Summary:This study presents findings that demonstrate the possibility of simplifying neural networks by inducing multifunctionality through separate manipulation within a single material. Herein, two‐terminal memristor W/ZnTe/W devices implemented a multifunctional memristor comprising a selector, synapse, and a neuron using an ovonic threshold switching material. By setting the low‐current level (µA) in the forming process, a stable memory‐switching operation is achieved, and the capacity to implement a synapse is demonstrated based on paired‐pulse facilitation/depression, potentiation/depression, spike‐amplitude‐dependent plasticity, and spike‐number‐dependent plasticity outcomes. Based on synaptic behavior, the Modified National Institute of Standards and Technology database image classification accuracy is up to 90%. Conversely, by setting the high‐current level (mA) in the forming process, the stable bipolar threshold switching operation and good selector characteristics (300 ns switching speed, free‐drift, recovery properties) are demonstrated. In addition, a stochastic neuron is implemented using the stochastic switching response in the positive voltage region. Utilizing stochastic neurons, it is possible to create a generative restricted Boltzmann machine model. An Ovonic threshold switching material based multifunctional memristor device is presented. The two‐terminal multifunctional memristor is expected to function as a selector in memory arrays, a synaptic device and a stochastic neuron in neuromorphic systems by setting the appropriate operating current level in the forming process. Utilizing the stochastic neurons, a restricted Boltzmann machine model is created.
ISSN:2198-3844
2198-3844
DOI:10.1002/advs.202405768