Long-term memory performance with learning behavior of artificial synaptic memristor based on stacked solution-processed switching layers

Zongjie Shen; Chun Zhao; Ka Lok Man; Yina Liu; Cezhou Zhao

doi:10.1109/ISCAS51556.2021.9401493

Long-term memory performance with learning behavior of artificial synaptic memristor based on stacked solution-processed switching layers

Zongjie Shen, Chun Zhao^*, Ka Lok Man, Yina Liu, Cezhou Zhao

^*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

Abstract

In this work, oxide resistive random access memory (OxRRAM) devices with stacked solution-processed (SP) metal oxide (MO) layers were fabricated to investigate artificial synaptic behavior such as long-term potentiation (LTP) and long-term depression (LTD). The stacked RRAM devices exhibited stable and repeated bipolar IV curves with operation voltage lower than the ~0.5 V and a switching ratio larger than 2×10⁴. Also, with the stimuli from external consecutive pulses, the stacked devices demonstrated learning-forgetting-relearning behavior similar to neuron-induced behavior in the human brain. Finally, based on stable long-term memory performance, the pattern recognition system with an artificial neuron network (ANN) algorithm was simulated with the recognition accuracy higher than 95%.

Original language	English
Title of host publication	2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728192017
DOIs	https://doi.org/10.1109/ISCAS51556.2021.9401493
Publication status	Published - 2021
Event	53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Daegu, Korea, Republic of Duration: 22 May 2021 → 28 May 2021

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	2021-May
ISSN (Print)	0271-4310

Conference

Conference	53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021
Country/Territory	Korea, Republic of
City	Daegu
Period	22/05/21 → 28/05/21

Keywords

Human brain
RRAM
Solution-processed
Synaptic behavior

Access to Document

10.1109/ISCAS51556.2021.9401493

Cite this

Shen, Z., Zhao, C., Man, K. L., Liu, Y., & Zhao, C. (2021). Long-term memory performance with learning behavior of artificial synaptic memristor based on stacked solution-processed switching layers. In 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings Article 9401493 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2021-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS51556.2021.9401493

Shen, Zongjie ; Zhao, Chun ; Man, Ka Lok et al. / Long-term memory performance with learning behavior of artificial synaptic memristor based on stacked solution-processed switching layers. 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings - IEEE International Symposium on Circuits and Systems).

@inproceedings{c11a9dc7c2704c81a54efd6355071c6a,

title = "Long-term memory performance with learning behavior of artificial synaptic memristor based on stacked solution-processed switching layers",

abstract = "In this work, oxide resistive random access memory (OxRRAM) devices with stacked solution-processed (SP) metal oxide (MO) layers were fabricated to investigate artificial synaptic behavior such as long-term potentiation (LTP) and long-term depression (LTD). The stacked RRAM devices exhibited stable and repeated bipolar IV curves with operation voltage lower than the \textasciitilde{}0.5 V and a switching ratio larger than 2×104. Also, with the stimuli from external consecutive pulses, the stacked devices demonstrated learning-forgetting-relearning behavior similar to neuron-induced behavior in the human brain. Finally, based on stable long-term memory performance, the pattern recognition system with an artificial neuron network (ANN) algorithm was simulated with the recognition accuracy higher than 95\%.",

keywords = "Human brain, RRAM, Solution-processed, Synaptic behavior",

author = "Zongjie Shen and Chun Zhao and Man, \{Ka Lok\} and Yina Liu and Cezhou Zhao",

note = "Funding Information: ACKNOWLEDGMENT This research was funded in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China Program (19KJB510059), the Suzhou Science and Technology Development Planning Project: Key Industrial Technology Innovation (SYG201924), and the Key Program Special Fund in XJTLU (KSF-P-02, KSF-T-03, KSF-A-04, KSF-A-05, KSF-A-07). Publisher Copyright: {\textcopyright} 2021 IEEE; 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 ; Conference date: 22-05-2021 Through 28-05-2021",

year = "2021",

doi = "10.1109/ISCAS51556.2021.9401493",

language = "English",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings",

}

Shen, Z, Zhao, C , Man, KL , Liu, Y & Zhao, C 2021, Long-term memory performance with learning behavior of artificial synaptic memristor based on stacked solution-processed switching layers. in 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings., 9401493, Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2021-May, Institute of Electrical and Electronics Engineers Inc., 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021, Daegu, Korea, Republic of, 22/05/21. https://doi.org/10.1109/ISCAS51556.2021.9401493

Long-term memory performance with learning behavior of artificial synaptic memristor based on stacked solution-processed switching layers. / Shen, Zongjie; Zhao, Chun ; Man, Ka Lok et al.
2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. 9401493 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2021-May).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Long-term memory performance with learning behavior of artificial synaptic memristor based on stacked solution-processed switching layers

AU - Shen, Zongjie

AU - Zhao, Chun

AU - Man, Ka Lok

AU - Liu, Yina

AU - Zhao, Cezhou

N1 - Funding Information: ACKNOWLEDGMENT This research was funded in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China Program (19KJB510059), the Suzhou Science and Technology Development Planning Project: Key Industrial Technology Innovation (SYG201924), and the Key Program Special Fund in XJTLU (KSF-P-02, KSF-T-03, KSF-A-04, KSF-A-05, KSF-A-07). Publisher Copyright: © 2021 IEEE

PY - 2021

Y1 - 2021

N2 - In this work, oxide resistive random access memory (OxRRAM) devices with stacked solution-processed (SP) metal oxide (MO) layers were fabricated to investigate artificial synaptic behavior such as long-term potentiation (LTP) and long-term depression (LTD). The stacked RRAM devices exhibited stable and repeated bipolar IV curves with operation voltage lower than the ~0.5 V and a switching ratio larger than 2×104. Also, with the stimuli from external consecutive pulses, the stacked devices demonstrated learning-forgetting-relearning behavior similar to neuron-induced behavior in the human brain. Finally, based on stable long-term memory performance, the pattern recognition system with an artificial neuron network (ANN) algorithm was simulated with the recognition accuracy higher than 95%.

AB - In this work, oxide resistive random access memory (OxRRAM) devices with stacked solution-processed (SP) metal oxide (MO) layers were fabricated to investigate artificial synaptic behavior such as long-term potentiation (LTP) and long-term depression (LTD). The stacked RRAM devices exhibited stable and repeated bipolar IV curves with operation voltage lower than the ~0.5 V and a switching ratio larger than 2×104. Also, with the stimuli from external consecutive pulses, the stacked devices demonstrated learning-forgetting-relearning behavior similar to neuron-induced behavior in the human brain. Finally, based on stable long-term memory performance, the pattern recognition system with an artificial neuron network (ANN) algorithm was simulated with the recognition accuracy higher than 95%.

KW - Human brain

KW - RRAM

KW - Solution-processed

KW - Synaptic behavior

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DO - 10.1109/ISCAS51556.2021.9401493

M3 - Conference Proceeding

AN - SCOPUS:85109015427

T3 - Proceedings - IEEE International Symposium on Circuits and Systems

BT - 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021

Y2 - 22 May 2021 through 28 May 2021

ER -

Shen Z, Zhao C , Man KL , Liu Y, Zhao C. Long-term memory performance with learning behavior of artificial synaptic memristor based on stacked solution-processed switching layers. In 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. 9401493. (Proceedings - IEEE International Symposium on Circuits and Systems). doi: 10.1109/ISCAS51556.2021.9401493

Long-term memory performance with learning behavior of artificial synaptic memristor based on stacked solution-processed switching layers

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