We present a multiscale modeling platform that exploits ab-initio calculation results and a material-related description of the most relevant defect-related phenomena in dieledtrics (charge trapping and transport, degradation and atomic species motion) to interpret and understand the electrical characteristics of OxRAM memory devices for non-volatile memories and neuromorphic applications. Simulation results provide a deep and quantitative understanding of the factors controlling device operation. The proposed multiscale modeling platform represents a powerful tool for investigating material properties and optimizing device performances and reliability.

A multiscale modeling approach for the simulation of OxRRAM devices / Padovani, A.; Larcher, L.; Woo, J.; Hwang, H.. - 2017-:(2017), pp. 1-8. ((Intervento presentato al convegno 17th Non-Volatile Memory Technology Symposium, NVMTS 2017 tenutosi a RWTH Aachen University, deu nel 2017 [10.1109/NVMTS.2017.8171306].

A multiscale modeling approach for the simulation of OxRRAM devices

Padovani A.;Larcher L.;
2017

Abstract

We present a multiscale modeling platform that exploits ab-initio calculation results and a material-related description of the most relevant defect-related phenomena in dieledtrics (charge trapping and transport, degradation and atomic species motion) to interpret and understand the electrical characteristics of OxRAM memory devices for non-volatile memories and neuromorphic applications. Simulation results provide a deep and quantitative understanding of the factors controlling device operation. The proposed multiscale modeling platform represents a powerful tool for investigating material properties and optimizing device performances and reliability.
17th Non-Volatile Memory Technology Symposium, NVMTS 2017
RWTH Aachen University, deu
2017
2017-
1
8
Padovani, A.; Larcher, L.; Woo, J.; Hwang, H.
A multiscale modeling approach for the simulation of OxRRAM devices / Padovani, A.; Larcher, L.; Woo, J.; Hwang, H.. - 2017-:(2017), pp. 1-8. ((Intervento presentato al convegno 17th Non-Volatile Memory Technology Symposium, NVMTS 2017 tenutosi a RWTH Aachen University, deu nel 2017 [10.1109/NVMTS.2017.8171306].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1222831
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