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.
|School:||University of Connecticut|
|School Location:||United States -- Connecticut|
|Source:||MAI 82/4(E), Masters Abstracts International|
|Subjects:||Electrical engineering, Engineering|
|Keywords:||Analytical, Buck converter, Integrated inductor, Performance modelling, Simulation, Statistical|
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