期刊
MATERIALS
卷 12, 期 21, 页码 -出版社
MDPI
DOI: 10.3390/ma12213573
关键词
memristor; neuromorphic computing; artificial intelligence; hardware-based deep learning ICs; circuit design
类别
资金
- Hungarian Government [2018-1.2.1-NKP-00008]
- Pazmany Peter Catholic University [KAP19-1.1-ITK]
One of the main obstacles for memristors to become commonly used in electrical engineering and in the field of artificial intelligence is the unreliability of physical implementations. A non-uniform range of resistance, low mass-production yield and high fault probability during operation are disadvantages of the current memristor technologies. In this article, the authors offer a solution for these problems with a circuit design, which consists of many memristors with a high operational variance that can form a more robust single memristor. The proposition is confirmed by physical device measurements, by gaining similar results as in previous simulations. These results can lead to more stable devices, which are a necessity for neuromorphic computation, artificial intelligence and neural network applications.
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