Modelling and analysis of memristor-based electrical networks for neuromorphic computing hardware

Anne-Men Huijzer

Research output: ThesisThesis fully internal (DIV)

Abstract

Recent developments in artificial intelligence (AI) have stimulated the design of software programs that use AI techniques, also called AI applications. These new applications are very convenient for their users. However, a big drawback is that they consume a lot of energy. This motivates the development of new computing technology that makes AI applications more energy-efficient. The field of neuromorphic computing aims to develop this technology by taking inspiration from the brain. To do so, new analogue computing hardware is required.
The new nanoscale materials used in this computing hardware are developed in specialised labs. Due to the small scale of these materials, it is difficult to grasp the behaviour of these materials. However, it is predicted that these materials behave like electrical networks constructed of electrical components such as resistors, memristors (i.e., resistors with memory), and capacitors.
Motivated by this, in this thesis, we use mathematical modelling and analysis techniques to study the behaviour of electrical networks comprising resistors, memristors, and capacitors. The objective of this study is, first, to obtain more insight into the behaviour of networks constructed of these electrical components, and, second, to understand if and how these networks can be used to mimic the behaviour of neurons and synapses in the brain.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Groningen
Supervisors/Advisors
  • Besselink, Bart, Supervisor
  • van der Schaft, Arjan, Supervisor
  • Noheda, Beatriz, Supervisor
Award date24-Feb-2025
Place of Publication[Groningen]
Publisher
DOIs
Publication statusPublished - 2025

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