Bersuker, G;
Farmer, J;
Veksler, D;
El-Sayed, AM;
Durrant, T;
Gao, DZ;
Shluger, A;
(2023)
Materials-to-applications evaluation framework: assessing memristor technologies for neural network implementations.
In:
Proceedings of the IEEE Nanotechnology Materials and Devices Conference (NMDC) 2023.
(pp. pp. 603-607).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
Practical needs in technology capability assessment for extremely low-energy neuromorphic computing is addressed via a novel development/analysis concept integrating atomic-level material modeling, statistical simulations of charge transport in a device material stack and verification of the modeling scheme against measurements emulating circuitry operation conditions for applications in specific neural networks (NN). This multi-scale concept - from materials to applications - directly links materials to their electrical properties, and the latter to NN algorithms. Such link enables identifying structural features controlling device characteristics and the range of operation conditions delivering performance targets for a given technology implementation. In comparison to widely employed memristor analyses primarily based on TCAD-type methodology with adjustable phenomenological parameters, the proposed approach allows to deliver feedback on favorable material compositions and cell architecture/dimensions to modify memristor fabrication process. Implementation of this technology evaluation approach to carbon nanotube (CNT) memristors enables identifying structural and operation conditions delivering optimal performance ahead of actual circuitry fabrication.
| Type: | Proceedings paper |
|---|---|
| Title: | Materials-to-applications evaluation framework: assessing memristor technologies for neural network implementations |
| Event: | 2023 IEEE Nanotechnology Materials and Devices Conference (NMDC) |
| Location: | Paestum, Italy |
| Dates: | 22nd-25th October 2023 |
| ISBN-13: | 979-8-3503-3546-0 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/NMDC57951.2023.10343611 |
| Publisher version: | http://dx.doi.org/10.1109/nmdc57951.2023.10343611 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10196859 |
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