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Yu, Heshan
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article
Energy Efficient Neuro-inspired Phase Change Memory Based on Ge4 Sb6 Te7 as a Novel Epitaxial Nanocomposite.
Abstract
Phase change memory (PCM) is a promising candidate for neuro-inspired, data-intensive artificial intelligence applications, which relies on the physical attributes of PCM materials including gradual change of resistance states and multilevel operation with low resistance drift. However, achieving these attributes simultaneously remains a fundamental challenge for PCM materials such as Ge2 Sb2 Te5 , the most commonly used material. Here we demonstrate bi-directional gradual resistance changes with ∼10x resistance window using low energy pulses in nanoscale PCM devices based on Ge4 Sb6 Te7 , a new phase change nanocomposite material. These devices show 13 resistance levels with low resistance drift for the first 8 levels, resistance on/off ratio of ∼1000, and low variability. These attributes are enabled by the unique microstructural and electrothermal properties of Ge4 Sb6 Te7 , a nanocomposite consisting of epitaxial SbTe nanoclusters within the Ge-Sb-Te matrix, and a higher crystallization but lower melting temperature than Ge2 Sb2 Te5 . These results advance the pathway towards energy-efficient analog computing using PCM. This article is protected by copyright. All rights reserved.