ArticleNanoscale advances2020
A dual mode electronic synapse based on layered SnSe films fabricated by pulsed laser deposition.
Article in Nanoscale advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Multi-Level Resistive Switching in SnSe/SrTiONanomaterials (Basel, Switzerland) · 2022Article
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Authors and funding
9 authors.
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Abstract
An artificial synapse, such as a memristive electronic synapse, has caught world-wide attention due to its potential in neuromorphic computing, which may tremendously reduce computer volume and energy consumption. The introduction of layered two-dimensional materials has been reported to enhance the performance of the memristive electronic synapse. However, it is still a challenge to fabricate large-area layered two-dimensional films by scalable methods, which has greatly limited the industrial application potential of two-dimensional materials. In this work, a scalable pulsed laser deposition (PLD) method has been utilized to fabricate large-area layered SnSe films, which are used as the functional layers of the memristive electronic synapse with dual modes. Both long-term memristive behaviour with gradually changed resistance (Mode 1) and short-term memristive behavior with abruptly reduced resistance (Mode 2) have been achieved in this SnSe-based memristive electronic synapse. The switching between Mode 1 and Mode 2 can be realized by a series of voltage sweeping and programmed pulses. The formation and recovery of Sn vacancies were believed to induce the short-term memristive behaviour, and the joint action of Ag filament formation/rupture and Schottky barrier modulation can be the origin of long-term memristive behaviour. DFT calculations were performed to further illustrate how Ag atoms and Sn vacancies diffuse through the SnSe layer and form filaments. The successful emulation of synaptic functions by the layered chalcogenide memristor fabricated by the PLD method suggests the application potential in future neuromorphic computers.
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