Energy Efficient Neuro‐Inspired Phase–Change Memory Based on Ge₄Sb₆Te₇ 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 bi-directional gradual resistance changes with ≈10× resistance window using low energy pulses are demonstrated 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, a resistance on/off ratio of ≈1000, and low variability. These attributes are enabled by the unique microstructural and electro-thermal 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 toward energy-efficient analog computing using PCM.