Analytical modelling of the transport in analog filamentary conductive-metal-oxide/HfOx ReRAM devices

Author:

Falcone Donato Francesco1ORCID,Menzel Stephan2ORCID,Stecconi Tommaso1,Galetta Matteo1,La Porta Antonio1,Offrein Bert Jan1,Bragaglia Valeria1ORCID

Affiliation:

1. IBM Research Europe – Zürich, 8803 Rüschlikon, Switzerland

2. Peter Gruenberg Institute 7, Forschungszentrum Juelich GmbH, 52425 Juelich, Germany

Abstract

The recent co-optimization of memristive technologies and programming algorithms enabled neural networks training with in-memory computing systems.

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

H2020 LEIT Information and Communication Technologies

H2020 LEIT Advanced Materials

Bundesministerium für Bildung und Forschung

H2020 LEIT Space

Publisher

Royal Society of Chemistry (RSC)

Reference52 articles.

1. B.Murmann , IEEE Transactions on Very Large Scale Integration (VLSI) Systems , 2021 , vol. 29, pp. 3–13

2. Memory devices and applications for in-memory computing

3. M.Horowitz , Digest of Technical Papers-IEEE International Solid-State Circuits Conference , 2014 , vol. 57, pp. 10–14

4. Scalable and Programmable Neural Network Inference Accelerator Based on In-Memory Computing

5. A.Sebastian , I.Boybat , M.Dazzi , I.Giannopoulos , V.Jonnalagadda , V.Joshi , G.Karunaratne , B.Kersting , R.Khaddam-Aljameh , S. R.Nandakumar , A.Petropoulos , C.Piveteau , T.Antonakopoulos , B.Rajendran , M. L.Gallo and E.Eleftheriou , IEEE Symposium on VLSI Circuits, Digest of Technical Papers , 2019 , 2019-June, pp. T168–69

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