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Neuromimetic VO2 memristors for Spiking Neural Networks

Andrea Iaconeta

Neuromimetic VO2 memristors for Spiking Neural Networks.

Rel. Carlo Ricciardi, Mihai Adrian Ionescu. Politecnico di Torino, Corso di laurea magistrale in Nanotechnologies For Icts (Nanotecnologie Per Le Ict), 2023

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Abstract:

This master's thesis focuses on the comprehensive study of VO2 films, exploring their interesting properties. The theoretical background encompasses an analysis of the phase transition phenomena. Experimental methods involving Pulsed Laser Deposition (PLD) and device fabrication techniques are discussed, followed by an examination of the electrical characterization of two-terminal devices. Additionally, circuit analysis and modeling are employed to simulate VO2 two-terminal devices, implemented as voltage-controlled switches, and develop neuron models. The thesis also delves into Spiking Neural Networks (SNN) and their architectures, specifically exploring the implementation of the Winner Takes All (WTA) rule. Furthermore, a study is conducted to analyze the induced strain on VO2 films, presenting both the theoretical idea and the experimental realization. Through these investigations, this thesis aims to contribute to a deeper understanding of VO2 films and their potential applications in various fields.

Relators: Carlo Ricciardi, Mihai Adrian Ionescu
Academic year: 2023/24
Publication type: Electronic
Number of Pages: 35
Subjects:
Corso di laurea: Corso di laurea magistrale in Nanotechnologies For Icts (Nanotecnologie Per Le Ict)
Classe di laurea: New organization > Master science > LM-29 - ELECTRONIC ENGINEERING
Ente in cotutela: Université Paris Cité (FRANCIA)
Aziende collaboratrici: EPFL
URI: http://webthesis.biblio.polito.it/id/eprint/28602
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