Reliability Impact of Chalcogenide-Structure Relaxation in Phase-Change Memory (PCM) Cells—Part I: Experimental Study

2009 ◽  
Vol 56 (5) ◽  
pp. 1070-1077 ◽  
Author(s):  
Daniele Ielmini ◽  
Deepak Sharma ◽  
Simone Lavizzari ◽  
Andrea. L. Lacaita
Keyword(s):  
Experimental Study ◽  
Phase Change ◽  
Phase Change Memory ◽  
Structure Relaxation ◽  
Change Memory

Reliability Impact of Chalcogenide-Structure Relaxation in Phase-Change Memory (PCM) Cells—Part II: Physics-Based Modeling

2009 ◽  
Vol 56 (5) ◽  
pp. 1078-1085 ◽  
Author(s):  
Simone Lavizzari ◽  
Daniele Ielmini ◽  
Deepak Sharma ◽  
Andrea L. Lacaita
Keyword(s):  
Phase Change ◽  
Phase Change Memory ◽  
Structure Relaxation ◽  
Change Memory

Energy landscape models for conduction and drift in phase change memory

2011 ◽  
Vol 1338 ◽  
Author(s):  
D. Ielmini ◽  
D. Fugazza ◽  
M. Boniardi
Keyword(s):  
Phase Change ◽  
Physical Modeling ◽  
Carrier Transport ◽  
Energy Landscape ◽  
Phase Change Memory ◽  
Analytical Models ◽  
Structure Relaxation ◽  
Amorphous Chalcogenide ◽  
Landscape Models ◽  
Change Memory

ABSTRACTThe 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.

scholarly journals 8-bit Precision In-Memory Multiplication with Projected Phase-Change Memory

2018 ◽  
Author(s):  
I. Giannopoulos ◽  
A. Sebastian ◽  
M. Le Gallo ◽  
V.P. Jonnalagadda ◽  
M. Sousa ◽  
...  
Keyword(s):  
Phase Change ◽  
Phase Change Memory ◽  
Change Memory

Precision of synaptic weights programmed in phase-change memory devices for deep learning inference

2020 ◽  
Author(s):  
S. R. Nandakumar ◽  
Irem Boybat ◽  
Jin-Ping Han ◽  
Stefano Ambrogio ◽  
Praneet Adusumilli ◽  
...  
Keyword(s):  
Deep Learning ◽  
Phase Change ◽  
Phase Change Memory ◽  
Memory Devices ◽  
Change Memory

scholarly journals Enhanced reliability of phase-change memory via modulation of local structure and chemical bonding by incorporating carbon in Ge2Sb2Te5

RSC Advances ◽  
2021 ◽  
Vol 11 (36) ◽  
pp. 22479-22488
Author(s):  
Jeong Hwa Han ◽  
Hun Jeong ◽  
Hanjin Park ◽  
Hoedon Kwon ◽  
Dasol Kim ◽  
...  
Keyword(s):  
Phase Change ◽  
Charge Density ◽  
Chemical Bonding ◽  
Local Structure ◽  
Phase Change Memory ◽  
Change Memory

Charge density differences (CDDs) on Ge–C–Sb bonds in CGST(5%) and Ge–C–Sb in CGST(10%).

Revisiting Hash Table Design for Phase Change Memory

2016 ◽  
Vol 49 (2) ◽  
pp. 18-26 ◽  
Author(s):  
Biplob Debnath ◽  
Alireza Haghdoost ◽  
Asim Kadav ◽  
Mohammed G. Khatib ◽  
Cristian Ungureanu
Keyword(s):  
Phase Change ◽  
Phase Change Memory ◽  
Hash Table ◽  
Change Memory
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