Abstract
In this article, the influence of the temperature instability of resistive memory switching on potential neuromorphic computing applications is extensively studied using an Intel TaOx-based analog-type memristor as a synaptic weight modulator in a neural network. Evaluation results show that the effect of ambient temperature during training and interference can degrade the neural network's accuracy due to inefficient weight updates and inevitable resistance or conductance drifting. Our results provide additional insights into device-level physical models and simple circuit-level design guidance for potential hardware-based neuromorphic computing applications.
Original language | English (US) |
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Pages (from-to) | 6102-6105 |
Number of pages | 4 |
Journal | IEEE Transactions on Electron Devices |
Volume | 69 |
Issue number | 11 |
DOIs | |
State | Published - Nov 1 2022 |
Externally published | Yes |
Keywords
- Memristor
- neural network
- neuromorphic computing
- resistive switching
- tantalum oxide
- temperature impact
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Electrical and Electronic Engineering