Chai, Z, Freitas, P, Zhang, WD, Hatem, F, Zhang, JF, Marsland, J, Govoreanu, B, Goux, L and Kar, GS (2018) Impact of RTN on Pattern Recognition Accuracy of RRAM-based Synaptic Neural Network. IEEE Electron Device Letters. ISSN 0741-3106

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Abstract

Resistive switching memory devices can be categorized into either filamentary or non-filamentary ones depending on the switching mechanisms. Both types have been investigated as novel synaptic devices in hardware neural networks, but there is a lack of comparative study between them, especially in random telegraph noise (RTN) which could induce large resistance fluctuations. In this work, we analyze the amplitude and occurrence rate of RTN in both Ta2O5 filamentary and TiO2/a-Si (a-VMCO) non-filamentary RRAM devices and evaluate its impact on the pattern recognition accuracy of neural networks. It is revealed that the non-filamentary RRAM has a tighter RTN amplitude distribution and much lower RTN occurrence rate than its filamentary counterpart which leads to negligible RTN impact on recognition accuracy, making it a promising candidate in synaptic application.

Item Type:	Article
Additional Information:	© 2018 IEEE
Uncontrolled Keywords:	0906 Electrical And Electronic Engineering
Subjects:	T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions:	Electronics & Electrical Engineering (merged with Engineering 10 Aug 20)
Publisher:	Institute of Electrical and Electronics Engineers
Date Deposited:	11 Sep 2018 10:25
Last Modified:	04 Sep 2021 10:07
DOI or ID number:	10.1109/LED.2018.2869072
URI:	https://researchonline.ljmu.ac.uk/id/eprint/9197

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