Ultralow Energy Consumption and Fast Neuromorphic Computing Based on La<sub>0.1</sub>Bi<sub>0.9</sub>FeO<sub>3</sub> Ferroelectric Tunnel Junctions.
basic_science · Level V
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- Record sourced from PubMed, PMID 39172999.
- Also identified by DOI 10.1021/acs.nanolett.4c01924.
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Abstract
Low-power and fast artificial neural network devices represent the direction in developing analogue neural networks. Here, an ultralow power consumption (0.8 fJ) and rapid (100 ns) La<sub>0.1</sub>Bi<sub>0.9</sub>FeO<sub>3</sub>/La<sub>0.7</sub>Sr<sub>0.3</sub>MnO<sub>3</sub> ferroelectric tunnel junction artificial synapse has been developed to emulate the biological neural networks. The visual memory and forgetting functionalities have been emulated based on long-term potentiation and depression with good linearity. Moreover, with a single device, logical operations of "AND" and "OR" are implemented, and an artificial neural network was constructed with a recognition accuracy of 96%. Especially for noisy data sets, the recognition speed is faster after preprocessing by the device in the present work. This sets the stage for highly reliable and repeatable unsupervised learning.