READ ME File For 'Dataset supporting the publication "Reservoir computing using back-end-of-line SiC-based memristors"'

Dataset DOI: 10.5258/SOTON/D2725

Date that the file was created: October, 2023

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GENERAL INFORMATION
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ReadMe Author: Dongkai Guo, University of Southampton 


Information about geographic location of data collection: Southampton, UK

Related projects:
Royal Society - Research Grant (RGS/R2/222171). EPSRC and AWE Ltd - ICASE studentship No. 16000087.


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SHARING/ACCESS INFORMATION
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Licenses/restrictions placed on the data, or limitations of reuse: CC BY


This dataset supports the publication:
Guo, Dongkai et al (2023) "Reservoir computing using back-end-of-line SiC-based memristors", 
Materials Advances, vol 4 pp 5305-5313 http://dx.doi.org/10.1039/D3MA00141E


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DATA & FILE OVERVIEW
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The data includes 2 files:
data.xlsx
checkpoint.pt


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METHODOLOGICAL INFORMATION
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This data demonstrates results of 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. The results demonstrated good robustness to noisy pixels. The results indicate that our SiC-based memristor devices are strong contenders for potential applications in artificial intelligence, particularly in temporal and sequential data processing.