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Energy landscape models for conduction and drift in phase change memory

Published online by Cambridge University Press:  19 December 2011

D. Ielmini ,
D. Fugazza  and
M. Boniardi
D. Ielmini*
Affiliation:
Dipartimento di Elettronica e Informazione and IU.NET, Politecnico di Milano, Piazza L. da Vinci, 32 – 20133 Milano, Italy.
D. Fugazza
Affiliation:
Dipartimento di Elettronica e Informazione and IU.NET, Politecnico di Milano, Piazza L. da Vinci, 32 – 20133 Milano, Italy.
M. Boniardi
Affiliation:
Dipartimento di Elettronica e Informazione and IU.NET, Politecnico di Milano, Piazza L. da Vinci, 32 – 20133 Milano, Italy.
*
*Email: ielmini@elet.polimi.it
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Abstract

The physical modeling of carrier conduction and material-related effects such as crystallization, structural relaxation (SR), electromigration and ion migration in chalcogenide materials is a key challenge toward the development and scaling of phase change memory (PCM) devices. In particular, future scaling to 10 nm and below may require addressing variability effects in the programming, switching and retention properties of the cell. Variability is deeply linked with the nanometer-scale fluctuations of potential, atomic structure and material composition that affect conduction, structure relaxation and crystallization. Therefore, the physical modeling of conduction and reliability in PCM devices requires energy landscape models, describing the random fluctuations of e.g. the potential energy dictating the carrier transport and the free energy controlling the atomic rearrangement of the amorphous chalcogenide structure. This work discusses energy landscape models for a physical description of (i) electrical conduction in the amorphous phase and (ii) SR responsible for resistance drift in the amorphous chalcogenide phase. The link between the effective energy barrier in conduction and relaxation will be clarified, and analytical models for the prediction of drift depending on time and temperature will be introduced. These models provide the first comprehensive approach for a physics-based prediction of resistance window, resistance drift and their corresponding statistical variability within large PCM arrays.

Keywords

deviceselectrical propertiesphase transformation
Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 1338: Symposium R – Phase-Change Materials for Memory and Reconfigurable Electronics Applications , 2011 , mrss11-1338-r01-04
Copyright
Copyright © Materials Research Society 2011

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