Generating AI modules for decoupling capacitor placement using simulation
Publication date
2023-12-01
Document type
Konferenzbeitrag
Author
Organisational unit
Conference
Kleinheubacher Tagung 2023 ; Miltenberg ; 26.–28. September 2023
Publisher
Copernicus Publications
Series or journal
Advances in Radio Science : Kleinheubacher Berichte
ISSN
Periodical volume
21
First page
49
Last page
55
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Abstract
The effects of parameters affecting the input impedance of a power delivery network (PDN) are investigated. It is considered that the size of the power plane and the number of associated planes in the PCB layout, apart from the decoupling capacitor, have an effect on the impedance behavior within a certain frequency range. An artificial neural network (ANN) is trained using the generated data utilizing a process to generate suitable input for training a machine learning (ML) module, which is able to predict the impedance profile of the PDN. In order to obtain a more accurate prediction, Bayesian optimization is implemented and the results are compared to commercial power integrity (PI) software.
Description
This work is distributed under the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/).
Version
Published version
Access right on openHSU
Metadata only access