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Updated: Dec 30, 2025

In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx
Published on: May 13, 2020
Low-Power Switching through Disorder and Carrier Localization in Bismuth-Doped Germanium Telluride Phase Change
Gaurav Modi1, Eric A Stach1, Ritesh Agarwal1
1Department of Materials Science and Engineering , University of Pennsylvania , Philadelphia , Pennsylvania 19104 , United States.
Abstract:
One of the major problems with phase change memory (PCM) is the high current density required for the crystal-amorphous transformation via a melt-quench process. However, alternative low-energy pathways of amorphization via a defect-assisted process have also been proposed. Here, a defect-assisted amorphization pathway in Bi-doped GeTe nanowires is utilized to establish that carrier localization effects can significantly decrease the energy costs of amorphization. We demonstrate a strategy of doping GeTe nanowires with bismuth to engineer carrier localization effects via Fermi level/mobility edge tuning and increased atomic disorder. Enhanced carrier localization increases the carrier-lattice coupling, and therefore, the energy supplied to carriers via electrical pulses can be more efficiently extracted by the lattice to induce the critical bond distortions required for amorphization without an intermediate melting process. RESET (crystal to amorphous transition) current densities as low as ∼0.3 MA cm-2 are achieved for 8% Bi-doped GeTe nanowires, which is nearly a 3-fold reduction compared to undoped GeTe nanowires and is significantly less than GeTe thin film devices (∼50 MA cm-2). We demonstrate good reversibility of switching in the Bi-doped GeTe nanowires and also demonstrate the existence of intermediate resistance states which can be accessed by controlled electrical pulsing. The combination of low-power switching in conjunction with multiple resistance states indicates that doping strategies in PCM nanowires are beneficial for non-volatile memory and neuromorphic computing applications.
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