A neural network based method for predicting PCB glass weave induced skew

This paper proposes the use of a neural network based tool to predict the skew factor of PCB laminate differential channel designs. A multitude of differential stripline design scenarios are 3D modelled, each with a different expected within differential pair skew factor. The modelled data is used t...

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Bibliographic Details
Main Authors: Hejase, Jose A., Paladhi, Pavel Roy, Krabbenhoft, Roger S., Zhaoqing Chen, Junyan Tang, Boday, Dylan J.
Format: Conference Proceeding
Language:English
Subjects:
Artificial Neural Network (ANN)
Data models
Dielectrics
Differential Pair
Glass
Glass Weave
Laminates
Machine Learning
Neural networks
Printed Circuit Board (PCB)
Skew
Stripline
Weaving
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Description
Summary:This paper proposes the use of a neural network based tool to predict the skew factor of PCB laminate differential channel designs. A multitude of differential stripline design scenarios are 3D modelled, each with a different expected within differential pair skew factor. The modelled data is used to train a neural network. The neural network is tested using an unseen set of design data in order to evaluate the goodness of its predictions. Preliminary results show this machine learned technique to be a viable way to predict PCB glass weave skew without the need to resort to intensive 3D modelling. This method has potential to shorten design cycles and simplify analysis while still achieving good simulation accuracy.
ISSN:2165-4115
DOI:10.1109/EPEPS.2016.7835439