Dissertation/Thesis Abstract

Performance Modelling of Fully-Integrated Buck Converter Circuits with Magnetic Thin-Film Inductors
by Cavallaro, Matthew, M.E., University of Connecticut, 2020, 108; 28150037
Abstract (Summary)

This work covers the creation of two performance modeling tools that aid the design and optimization

of power management integrated circuits with magnetic thin-film inductors. The first modelling

tool calculates power management integrated circuit performance metrics, such as efficiency and current

density, and creates two-dimensional and three-dimensional plots of the results versus a range of input

operating points, such as input voltage, output voltage, output current, and switching frequency. These

calculations were compared to SPICE simulations of the circuit and bench measurement of a prototype,

demonstrating agreement within 1% and 5%, respectively, for efficiency estimates across multiple operating

points. The second modelling tool incorporates a regression model and a decision tree model to

predict magnetic thin-film inductor parameters, such as inductance, resistance, and saturation current,

based upon device layout features, such as turn count, magnetic core dimensions, and winding width.

This module was trained and tested on a simulation data set, demonstrating agreement within ±2% for

inductance at 100 MHz, ±2% for saturation current, ±4% for direct current resistance, and ±7% for

resistance at 100 MHz. The combination of these tools accelerates the power management integrated

circuit design process by optimizing the trade-off between prediction speed and prediction accuracy.

Indexing (document details)
Advisor: Shlayan, Neveen
Commitee:
School: University of Connecticut
Department: Electrical Engineering
School Location: United States -- Connecticut
Source: MAI 82/4(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Electrical engineering, Engineering
Keywords: Analytical, Buck converter, Integrated inductor, Performance modelling, Simulation, Statistical
Publication Number: 28150037
ISBN: 9798678143211
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