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      High sensitivity and wide response range artificial synapse based on polyimide with embedded graphene quantum dots

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          Abstract

          Artificial electronic synapses are commonly used to simulate biological synapses to realize various learning functions, regarded as one of the key technologies in the next generation of neurological computation. This work used a simple spin coating technique to fabricate polyimide (PI):graphene quantum dots(GQDs) memristor structure. As a result, the devices exhibit remarkably stable exponentially decaying postsynaptic suppression current over time, as interpreted in the spike-timing-dependent plasticity phenomenon. Furthermore, with the increase of the applied electrical signal over time, the conductance of the electrical synapse gradually changes, and the electronic synapse also shows plasticity dependence on the amplitude and frequency of the pulse applied. In particular, the devices with the structure of Ag/PI:GQDs/ITO prepared in this study can produce a stable response to the stimulation of electrical signals between millivolt to volt, showing not only high sensitivity but also a wide range of “feelings”, which makes the electronic synapses take a step forwards to emulate biological synapses. Meanwhile, the electronic conduction mechanisms of the device are also studied and expounded in detail. The findings in this work lay a foundation for developing brain-like neuromorphic modeling in artificial intelligence.

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          The missing memristor found.

          Anyone who ever took an electronics laboratory class will be familiar with the fundamental passive circuit elements: the resistor, the capacitor and the inductor. However, in 1971 Leon Chua reasoned from symmetry arguments that there should be a fourth fundamental element, which he called a memristor (short for memory resistor). Although he showed that such an element has many interesting and valuable circuit properties, until now no one has presented either a useful physical model or an example of a memristor. Here we show, using a simple analytical example, that memristance arises naturally in nanoscale systems in which solid-state electronic and ionic transport are coupled under an external bias voltage. These results serve as the foundation for understanding a wide range of hysteretic current-voltage behaviour observed in many nanoscale electronic devices that involve the motion of charged atomic or molecular species, in particular certain titanium dioxide cross-point switches.
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            Memristor-The missing circuit element

            L P Chua (1971)
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              Synaptic electronics: materials, devices and applications.

              In this paper, the recent progress of synaptic electronics is reviewed. The basics of biological synaptic plasticity and learning are described. The material properties and electrical switching characteristics of a variety of synaptic devices are discussed, with a focus on the use of synaptic devices for neuromorphic or brain-inspired computing. Performance metrics desirable for large-scale implementations of synaptic devices are illustrated. A review of recent work on targeted computing applications with synaptic devices is presented.
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                Author and article information

                Contributors
                chaoxing_wu@fzu.edu.cn
                pcooi@gmx.com
                fushanli@hotmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                20 May 2023
                20 May 2023
                2023
                : 13
                : 8194
                Affiliations
                [1 ]School of Computing and Information Sciences, Fuzhou Institute of Technology, Fuzhou, 350506 People’s Republic of China
                [2 ]GRID grid.412113.4, ISNI 0000 0004 1937 1557, Institute of Microengineering and Nanoelectronics (IMEN), , Universiti Kebangsaan Malaysia, ; 43600 Bangi, Selangor Malaysia
                [3 ]GRID grid.411604.6, ISNI 0000 0001 0130 6528, School of Physics and Information Engineering, , Fuzhou University, ; Fuzhou, 350002 People’s Republic of China
                Article
                35183
                10.1038/s41598-023-35183-8
                10199894
                37210533
                91985199-260d-4e26-a491-bf40f78a0b55
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 February 2023
                : 14 May 2023
                Funding
                Funded by: Fundamental Research Grant Scheme
                Award ID: FRGS/1/2022/TK08/UKM/02/13
                Award Recipient :
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                © Springer Nature Limited 2023

                Uncategorized
                materials science,physics
                Uncategorized
                materials science, physics

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