Reservoir computing using back-end-of-line SiC-based memristors

Abstract

The increasing demand for intellectual computers that can efficiently process substantial amounts of data has resulted in the development of a wide range of nanoelectronics devices. Reservoir computing offers efficient temporal information processing capability with a low training cost. In this work, we demonstrate a back-end-of-line SiC-based memristor that exhibits short-term memory behaviour and is capable of encoding temporal signals. A physical reservoir computing system using our SiC-based memristor as the reservoir has been implemented. This physical reservoir computing system has been experimentally demonstrated to perform the task of pattern recognition. After training, our RC system has achieved 100% accuracy in classifying number patterns from 0 to 9 and demonstrated good robustness to noisy pixels. The results shown here indicate that our SiC-based memristor devices are strong contenders for potential applications in artificial intelligence, particularly in temporal and sequential data processing.

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