Fully CMOS-compatible passive TiO2-based memristor crossbars for in-memory computing
In: ISSN: 0167-9317, 2022
Online
academicJournal
Zugriff:
International audience ; Brain-inspired computing and neuromorphic hardware are promising approaches that offer great potential to overcome limitations faced by current computing paradigms based on traditional von-Neumann architecture. In this regard, interest in developing memristor crossbar arrays has increased due to their ability to natively perform in-memory computing and fundamental synaptic operations required for neural network implementation. For optimal efficiency, crossbar-based circuits need to be compatible with fabrication processes and materials of industrial CMOS technologies. Herein, we report a complete CMOS-compatible fabrication process of TiO2-based passive memristor crossbars with 700 nm wide electrodes. We show successful bottom electrode fabrication by a damascene process, resulting in an optimised topography and a surface roughness as low as 1.1 nm. DC sweeps and voltage pulse programming yield statistical results related to synaptic-like multilevel switching. Both cycle-to-cycle and device-to-device variability are investigated. Analogue programming of the conductance using sequences of 200 ns voltage pulses suggest that the fabricated memories have a multilevel capacity of at least 3 bits due to the cycle-to-cycle reproducibility.
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Fully CMOS-compatible passive TiO2-based memristor crossbars for in-memory computing
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Autor/in / Beteiligte Person: | El Mesoudy, Abdelouadoud ; Lamri, Gwénaëlle ; Dawant, Raphaël ; Arias-Zapata, Javier ; Gliech, Pierre ; Beilliard, Yann ; Ecoffey, Serge ; Ruediger, Andreas ; Alibart, Fabien ; Drouin, Dominique ; Laboratoire Nanotechnologies et Nanosystèmes Sherbrooke (LN2) ; Université de Sherbrooke (UdeS)-École Centrale de Lyon (ECL) ; Université de Lyon-Université de Lyon-École Supérieure de Chimie Physique Électronique de Lyon (CPE)-Institut National des Sciences Appliquées de Lyon (INSA Lyon) ; Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA) ; Institut Interdisciplinaire d'Innovation Technologique Sherbrooke (3IT) ; Université de Sherbrooke (UdeS) ; Institut National de la Recherche Scientifique Québec (INRS) ; Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN) ; Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA) ; Université catholique de Lille (UCL)-Université catholique de Lille (UCL) ; Nanostructures, nanoComponents & Molecules - IEMN (NCM - IEMN) ; Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA) ; This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) HIDATA project 506289-2017 and ERC-CoG IONOS (# GA 773228). A.R. gratefully acknowledges financial support through an NSERC discovery grant (RGPIN-2019-07023). This work was also supported by the CHIST-ERA UNICO project and Fond de Recherche du Québec Nature et Technologies (FRQNT). We would like to acknowledge Abdelatif Jaouad, Julien Pezard, and 3IT.Nano platform for their valuable support with device fabrication. We would also like to thank Yuanyang Guo for her assistance with electrical characterisation. ; ANR-19-CHR3-0006,UNICO,Unsupervised spiking neural networks with analog memristive devices for edge computing(2019) ; European Project: 773228,H2020,ERC-2017-COG,IONOS(2018) |
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Zeitschrift: | ISSN: 0167-9317, 2022 |
Veröffentlichung: | HAL CCSD ; Elsevier, 2022 |
Medientyp: | academicJournal |
DOI: | 10.1016/j.mee.2021.111706 |
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