Abstract
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.
Original language | English |
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Article number | 2405768 |
Journal | Advanced Science |
Volume | 11 |
Issue number | 42 |
DOIs | |
State | Published - 13 Nov 2024 |
Keywords
- OTS
- RBM
- neuromorphic system
- stochastic neuron
- synaptic devices