NaMes - NanoMechanical Sensing and NanoMemristive Systems
Tesi al Politecnico
Modeling and simulation of memristor-based random electrical networks using SPICE
Parole chiave MEMRISTORS, SISTEMI NEUROMORFICI, BIOELETTRONICA
Riferimenti CARLO RICCIARDI
Gruppi di ricerca NaMes - NanoMechanical Sensing and NanoMemristive Systems
Descrizione Modeling and simulation of memristor-based random electrical networks using SPICE
Host: Prof. Enrique Miranda, Department of Electronics Engineering, Universitat Autònoma de Barcelona, Spain. https://scholar.google.com/citations?user=vKLI8ewAAAAJ&hl=en
As we all know, artificial intelligence is relentlessly arriving to almost every corner of our life. From smartphones to home appliances, from unmanned vehicles to face recognition algorithms, from financial data analysis to YouTube preferences, all electrical and computer systems will include in the near future a piece of technology able to evaluate a complex situation and take some kind of action. This is currently performed using specialized processing units and high-level computer languages, but what if the core computations associated with these tasks were performed by a single chip at much less cost and energy requirements? Why not take advantage of the lessons Nature teach us and use a system that mimics the operations carried out by our brain?, a system comprising artificial neurons and synapses able to establish connections and pass the information among them. This may sound science-fiction, but believe me, it is not. We are at the doors of a new revolution in electronics, likely similar to the invention of the vacuum tube, the bipolar transistor, or even the MOS transistor, and this revolution is closer than ever. Experts in the area claim that this objective is almost at our hands and that can be reached using devices called memristors: i.e. resistors with memory. Devices that, when connected forming an array or network, are able to respond to a specific electrical stimulus in the sense they were previously taught or more advanced, devices that can learn from new experiences or data. The electrical activity of memristors formed at the connections of a random network of nanowires is the central topic of this research proposal. In particular, we will explore the behavioral representation of a single memristive device as well as the collective response of interconnected cells using SPICE (Simulation Program with Integrated Circuits Emphasis). It is clear that to achieve this goal, first, we need to understand the physics behind an isolated device and later extend the model to the random network considering different biasing schemes. It is worth mentioning that this activity will be carried out in close collaboration with Prof. Carlo Ricciardi from DISAT (Department of Applied Science and Technology), Politecnico di Torino.
• F. Aguirre et al, “Application of the quasi-static memdiode model in cross-point arrays for large dataset pattern recognition”, IEEE Access 8, 202174 (2020).
• G. Milano et al, “Brain-inspired structural plasticity through reweighting and rewiring in multi-terminal self-organizing memristive nanowires networks”, Advanced Intelligent Systems 2, 2000096 (2020).
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Scadenza validita proposta 22/12/2023 PROPONI LA TUA CANDIDATURA