Summary
The neuromorphic computing paradigm, inspired by the brain's non von-Neumman architecture, constitutes the most auspicious alternative for the More than Moore era. The electronic implementation of the neuron must reproduce the nonvolatile, multilevel, and scalable properties found in the human cortex. Memristors based on two-dimensional (2D) materials are an ideal candidate to emulate the biological synapse. The investigation of the mechanical, thermal and electrical properties that lead to the memristive behaviour in 2D materials and, in particular, the rationalization of the underlying mechanisms causing the memristive effect, are subject of intense scientific debate. The gap between the experimental 2D memristors realizations and the theoretical understanding of their operating principles demand an hollistic modeling approach that is still lacking. In this project, we aim to develop a bottom-up modeling framework for studying memristive systems and its integration in neuromorphic computing networks. To this purpose, we will employ the multi-scale modeling approximation, a powerful and versatile simulation approach that has become an standard increasingly used in the physical and electronic communities and presents clear advantages regarding the level of accuracy and computational burden. The proposed model will be developed in three stages, 1) 2D Material - Ab initio level 2) Memristor - Device level and 3) Neuromorphic networks - Circuit level. The model will be validated using the experimental characterization data form the 2D memristor fabricated during the secondment phase. This project aims to implement a complete design chain able to boost the state-of-the-art technology. The relevance of this Project is based on the premise of developing a novel and disruptive technology with a great future projection, intending to explore the still insufficiently exploited and enormously promising More Than Moore type of solution.
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| Web resources: | https://cordis.europa.eu/project/id/101032701 |
| Start date: | 01-03-2022 |
| End date: | 29-02-2024 |
| Total budget - Public funding: | 160 932,48 Euro - 160 932,00 Euro |
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Original description
The neuromorphic computing paradigm, inspired by the brain's non von-Neumman architecture, constitutes the most auspicious alternative for the More than Moore era. The electronic implementation of the neuron must reproduce the nonvolatile, multilevel, and scalable properties found in the human cortex. Memristors based on two-dimensional (2D) materials are an ideal candidate to emulate the biological synapse. The investigation of the mechanical, thermal and electrical properties that lead to the memristive behaviour in 2D materials and, in particular, the rationalization of the underlying mechanisms causing the memristive effect, are subject of intense scientific debate. The gap between the experimental 2D memristors realizations and the theoretical understanding of their operating principles demand an hollistic modeling approach that is still lacking. In this project, we aim to develop a bottom-up modeling framework for studying memristive systems and its integration in neuromorphic computing networks. To this purpose, we will employ the multi-scale modeling approximation, a powerful and versatile simulation approach that has become an standard increasingly used in the physical and electronic communities and presents clear advantages regarding the level of accuracy and computational burden. The proposed model will be developed in three stages, 1) 2D Material - Ab initio level 2) Memristor - Device level and 3) Neuromorphic networks - Circuit level. The model will be validated using the experimental characterization data form the 2D memristor fabricated during the secondment phase. This project aims to implement a complete design chain able to boost the state-of-the-art technology. The relevance of this Project is based on the premise of developing a novel and disruptive technology with a great future projection, intending to explore the still insufficiently exploited and enormously promising More Than Moore type of solution.Status
SIGNEDCall topic
MSCA-IF-2020Update Date
28-04-2024
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