Academic Journal
Controllable digital resistive switching for artificial synapses and pavlovian learning algorithm.
| Title: | Controllable digital resistive switching for artificial synapses and pavlovian learning algorithm. |
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| Authors: | Kumar M; Photoelectric and Energy Device Application Lab (PEDAL), Multidisciplinary Core Institute for Future Energies (MCIFE), Incheon, 22012, Republic of Korea and Department of Electrical Engineering, Incheon National University, 119 Academy Rd. Yeonsu, Incheon, 22012, Republic of Korea. joonkim@incheon.ac.kr., Abbas S; Photoelectric and Energy Device Application Lab (PEDAL), Multidisciplinary Core Institute for Future Energies (MCIFE), Incheon, 22012, Republic of Korea and Department of Electrical Engineering, Incheon National University, 119 Academy Rd. Yeonsu, Incheon, 22012, Republic of Korea. joonkim@incheon.ac.kr., Lee JH; Department of Materials and Chemical Engineering, Hanyang University, Ansan, Kyunggido 15588, Korea. jungho@hanyang.ac.kr., Kim J; Photoelectric and Energy Device Application Lab (PEDAL), Multidisciplinary Core Institute for Future Energies (MCIFE), Incheon, 22012, Republic of Korea and Department of Electrical Engineering, Incheon National University, 119 Academy Rd. Yeonsu, Incheon, 22012, Republic of Korea. joonkim@incheon.ac.kr. |
| Source: | Nanoscale [Nanoscale] 2019 Sep 07; Vol. 11 (33), pp. 15596-15604. Date of Electronic Publication: 2019 Aug 12. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: RSC Pub Country of Publication: England NLM ID: 101525249 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2040-3372 (Electronic) Linking ISSN: 20403364 NLM ISO Abbreviation: Nanoscale Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Cambridge, UK : RSC Pub. |
| MeSH Terms: | Algorithms*, Nanowires/chemistry ; Silicon/chemistry ; Silver/chemistry ; Synapses/physiology ; Zinc Oxide/chemistry ; Electricity |
| Abstract: | The fundamental unit of the nervous system is a synapse, which is involved in transmitting information between neurons as well as learning, memory, and forgetting processes. Two-terminal memristors can fulfil most of these requirements; however, their poor dynamic changes in resistance to input electric stimuli remain an obstacle, which must be improved for accurate and quick information processing. Herein, we demonstrate the synaptic properties of ZnO-based memristors, which were significantly enhanced (∼340 times) by geometrical modulation due to the localized electric field enhancement. Specifically, by inserting Ag-nanowires and Ag-dots into the ZnO/Si interface, the resistive switching could be controlled from a digital to analog mode. A finite element simulation revealed that the presence of Ag could enhance the localized electric field, which in turn improved the migration of ionic species. Further, the device showed a variety of comprehensive synaptic functions, for instance, paired-pulse facilitation and transformation from short-term plasticity to long-term plasticity, including the Pavlovian associative learning process in a human brain. Our study presents a novel architecture to enhance the synaptic sensitivity, and its uses in practical applications, including the artificial learning algorithm. |
| Substance Nomenclature: | 3M4G523W1G (Silver) SOI2LOH54Z (Zinc Oxide) Z4152N8IUI (Silicon) |
| Entry Date(s): | Date Created: 20190813 Date Completed: 20200102 Latest Revision: 20200102 |
| Update Code: | 20260130 |
| DOI: | 10.1039/c9nr02027f |
| PMID: | 31403638 |
| Database: | MEDLINE |
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