ArticleResearch (Washington, D.C.)2020
Perovskite-Enhanced Silicon-Nanocrystal Optoelectronic Synaptic Devices for the Simulation of Biased and Correlated Random-Walk Learning.
Article in Research (Washington, D.C.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Self-Powered Photonic Synapses with Rapid Optical Erasing Ability for Neuromorphic Visual Perception.Research (Washington, D.C.) · 2024Article
- A bioinspired flexible neuromuscular system based thermal-annealing-free perovskite with passivation.Nature communications · 2022Article
- Light-Emitting Artificial Synapses for Neuromorphic Computing.Research (Washington, D.C.) · 2022Review
- Thermal Disproportionation for the Synthesis of Silicon Nanocrystals and Their Photoluminescent Properties.Frontiers in chemistry · 2021Review
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Silicon- (Si-) based optoelectronic synaptic devices mimicking biological synaptic functionalities may be critical to the development of large-scale integrated optoelectronic artificial neural networks. As a type of important Si materials, Si nanocrystals (NCs) have been successfully employed to fabricate optoelectronic synaptic devices. In this work, organometal halide perovskite with excellent optical asborption is employed to improve the performance of optically stimulated Si-NC-based optoelectronic synaptic devices. The improvement is evidenced by the increased optical sensitivity and decreased electrical energy consumption of the devices. It is found that the current simulation of biological synaptic plasticity is essentially enabled by photogating, which is based on the heterojuction between Si NCs and organometal halide perovskite. By using the synaptic plasticity, we have simulated the well-known biased and correlated random-walk (BCRW) learning.
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Registered trials
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