Publisher DOI: 10.1109/SPI54345.2022.9874946
Title: Parametric S-Parameters for PCB based Power Delivery Network Design Using Machine Learning
Language: English
Authors: Schierholz, Christian Morten 
Erdin, Ihsan 
Balachandran, Jayaprakash 
Yang, Cheng 
Schuster, Christian 
Keywords: Artificial neural network; PB-Modeling; PCB; power delivery network
Issue Date: May-2022
Source: 26th IEEE Workshop on Signal and Power Integrity (SPI 2022)
Abstract (english): 
In this contribution, a methodology using ANN techniques is presented for the analysis and design of power delivery network (PDN) in printed circuit board (PCB) design. The trained artificial neural networks (ANNs) are applied to answer relevant PDN design questions such as PCB resonance frequency and target impedance (TI) violations. Based on PCB geometry and material variations a PDN design space is defined. To train the ANNs, inside the design space a sparse sampling with 10000 physics-based (PB) simulations is performed. The S-parameter database is created using physics based via models which are validated by a commercial full-wave finite element method (FEM) solver in the frequency spectrum of 1MHz to 1GHz. The S-parameters are available in the SI/PI-Database. The flexibility of the unterminated S-parameters is demonstrated by a post processing termination using decoupling capacitor (decap) distributions. Limitations of the data generation are discussed with respect to computational resources and disk space.
Conference: 26th IEEE Workshop on Signal and Power Integrity, SPI 2022 
URI: http://hdl.handle.net/11420/13756
ISBN: 9781665486255
Institute: Theoretische Elektrotechnik E-18 
Document Type: Chapter/Article (Proceedings)
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