Davide Pilati
Multielectrode characterization of neuromorphic nanowire networks.
Rel. Carlo Ricciardi, Gianluca Milano. Politecnico di Torino, Corso di laurea magistrale in Nanotechnologies For Icts (Nanotecnologie Per Le Ict), 2023
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Abstract
The recent spread of AI and machine learning, as well as computational intensive tasks, launches the challenge of overcoming traditional Von Neumann architectures. Traditional computing architectures are reaching their limit, and the need for alternative computational paradigms arises. One of the most promising solution is represented by neuromorphic systems, computational architectures that mimic the behavior of biological neural networks, by processing and storing informations in the same framework. For this application the memristor is one of the most promising candidate, being an analog device that inherently mimics a biological synapse, exhibiting intrinsic memory and resistive switching features. The aim of this work is to investigate emergent memristive dynamics in nanowire (NW) networks by developing a multiterminal characterization setup.
IV and pulse characterizations have been performed to characterize the electrical behavior of the network, with great focus on the turn-on phase
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