Open Access Article
This Open Access Article is licensed under a Creative Commons Attribution 3.0 Unported Licence
Correction: ‘Stateful’ threshold switching for neuromorphic learning
Zhijian
Zhong
a,
Zhiguo
Jiang
a,
Jianning
Huang
a,
Fangliang
Gao
*a,
Wei
Hu
b,
Yong
Zhang
a and
Xinman
Chen
*a
aGuangdong Engineering Research Center of Optoelectronic Functional Materials and Devices, Institute of Semiconductors, South China Normal University, Guangzhou 510631, PR China. E-mail: gaofl@m.scnu.edu.cn; chenxinman@m.scnu.edu.cn
bKey Laboratory of Optoelectronic Technology and System of Ministry of Education, College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, PR China
First published on 28th March 2022
Abstract
Correction for ‘‘Stateful’ threshold switching for neuromorphic learning’ by Zhijian Zhong et al., Nanoscale, 2022, DOI: 10.1039/d1nr05502j.
The authors regret that the last sentence of the second paragraph in the [Results and discussion] section, [Emulating associative learning via ‘stateful’ TA] subsection, contained errors. The correct sentence is the following:
“Naturally, high efficiency to build this acquisition can be reached if a larger VUS is applied, upon which only one training cycle is required with a VUS pulse of over 1.2 V (Fig. S8 in the ESI†).”
The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.
| This journal is © The Royal Society of Chemistry 2022 |