Memristive Behaviors Dominated by Reversible Nucleation Dynamics of Phase-Change Nanoclusters.
Qin WanFei ZengYiming SunTongjin ChenJunwei YuHuaqiang WuZhen ZhaoJiangli CaoFeng PanPublished in: Small (Weinheim an der Bergstrasse, Germany) (2022)
One of the important steps for realizing artificial intelligence is identifying elementary units that are beneficial for neural network construction. A type of memristive behavior in which phase-change nanoclusters nucleate adaptively in two adjacent dielectric layers with distinct distribution patterns is demonstrated. This memristive system responds in potentiation to increased stimulation strength and fire action potential after threshold stimulation. Reversible nucleation of phase-change nanoclusters is confirmed after both in situ and ex situ examinations using high-resolution transmission electron microscopy. The dynamics at the nanoscale level dominates the actions of the two dielectric layers. The oscillation response over a long period is due to the competition between crystalline and amorphous phases in the layer near the bottom electrode. Weight mutation, that is, action potential firing, is caused by the blockage of the filament in the layer near the top electrode. The memristive system is compact and able to execute complicated functions of a complete neuron and performs an important role in neuromorphic computing.
Keyphrases
- artificial intelligence
- neural network
- sensitive detection
- high resolution
- electron microscopy
- machine learning
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- fluorescent probe
- deep learning
- room temperature
- label free
- weight loss
- energy transfer
- body mass index
- human health
- physical activity
- risk assessment
- solid state
- atomic force microscopy
- high speed
- body weight