Resistance Drift Convergence and Inversion in Amorphous Phase Change Materials. (25th September 2022)

• Record Type: Journal Article
• Authors: Pries, Julian; Stenz, Christian; Schäfer, Lisa; Gutsche, Alexander; Wei, Shuai; Lucas, Pierre; Wuttig, Matthias
• Is Part Of: Advanced functional materials. Volume 32:Number 48(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-25
• Subjects: data storages -- glass dynamics -- phase change materials -- rejuvenation -- resistance drifts -- structural relaxations; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adfm.202207194
• Languages: English
• ISSNs: 1616-301X

Abstract

Abstract: Phase change materials (PCMs) are key to the development of artificial intelligence technologies such as high‐density memories and neuromorphic computing, thanks to their ability for multi‐level data storage through stepwise resistive encoding. Individual resistance levels are realized by adjusting the crystalline and amorphous volume fraction of the memory cell. However, the amorphous phase exhibits a drift in resistance over time that has so far hindered the commercial implementation of multi‐level storage schemes. In this study, the underlying physical process of resistance drift with the goal of modeling is elucidated that will help minimize and potentially overcome drift in PCM memory devices. Clear evidence is provided that the resistance drift is dominated by glass dynamics. Resistivity convergence and drift inversion for the amorphous chalcogenide Ge15 Te85 and the PCM Ge3 Sb6 Te5 are experimentally demonstrated and these changes are successfully predicted with a glass dynamics model. This new insight into the resistance drift process provides tools for the development of advanced PCM devices. Abstract : Resistance drift of amorphous phase‐change materials (PCMs) is usually explained by structural relaxation. It manifests itself in aging, stabilization, and rejuvenation. It is found that these processes lead to resistance drift, convergence, and drift inversion. The resistance evolution is described accurately by conventional glass dynamics. These findings will help to realize drift‐free multi‐level PCM devices essential for brain‐inspired neuromorphic networks and artificial intelligence.