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Electroformed, Self‐Connected Tin Oxide Nanoparticle Networks for Electronic Reservoir Computing
Advanced Electronic Materials ( IF 6.2 ) Pub Date : 2020-05-20 , DOI: 10.1002/aelm.202000081
Phuong Y. Le 1 , Billy J. Murdoch 1 , Anders J. Barlow 2 , Anthony S. Holland 1 , Dougal G. McCulloch 1 , Chris F. McConville 1 , Jim G. Partridge 1
Affiliation  

Interconnected SnOx nanoparticle (NP) networks are electroformed within a semi‐insulating SnOx thin‐film and between lateral electrodes. During this top‐down process, Joule heating, disproportionation, and de‐wetting of the SnOx thin‐film precede the formation of the NP networks. The same lateral electrodes used for electroforming are used to probe the network and reveal its complex electrical characteristics. Higher‐order harmonic generation is observed and the internal short‐term memory effects of the NP networks enable temporal inputs to be mapped into reservoir states for subsequent linear readout without training. Reservoir computing functionality is demonstrated with no requirement for high‐vacuum or cryo‐cooled environments.

中文翻译:

电沉积,自连接氧化锡纳米粒子网络,用于电子储层计算

互连的SnO x 纳米颗粒(NP)网络在半绝缘的SnO x 薄膜内和侧面电极之间电铸。在此自上而下的过程中,SnO x 薄膜的焦耳加热,歧化和去湿先于NP网络的形成。用于电铸的相同侧电极用于探测网络并揭示其复杂的电气特性。观察到高次谐波的产生,并且NP网络的内部短期记忆效应使时间输入可以映射到储层状态,以便随后进行线性读出而无需训练。展示了储层计算功能,无需高真空或低温冷却环境。
更新日期:2020-07-13
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