Predictive Quality of In-Fabrication Products in Smart Manufacturing Using Graph-Based Deep Learning

Peter Davison, Peter Davison, Muhammad Fahim, Roger Woods, Scott Fischaber, Marcus Haron, Cormac McAteer

2025

Abstract

Graph neural networks are a very powerful way to learn about relationships between entities in graphs. With the rise of IoT devices in manufacturing, more data is being collected to minimise the waste of both valuable resources and time for fabrication. In this paper, we introduce a methodology for predictive quality of in-fabrication products using graph neural networks. Data is collected from a live-working semiconductor wafer fabrication facility and used to produce heterogeneous graphs that represent the fabrication timeline of a wafer. The model uses the graph attention network architecture to classify whether a timeline is scrap or non-scrap. It uses historical graph-level labelled data and achieves an F1-score of 0.928, compared to baselines models of a LSTM and a Homogeneous Graph Attention Network with scores of 0.424 and 0.786 respectively. It gives a foundational framework for future anomaly detection with semiconductor fabrication, allowing real-world data to be analysed with graph-based deep learning tools to provide interpretation and accessible graph-based results.

Paper Citation

in Harvard Style

Davison P., Fahim M., Woods R., Fischaber S., Haron M. and McAteer C. (2025). Predictive Quality of In-Fabrication Products in Smart Manufacturing Using Graph-Based Deep Learning. In Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-770-2, SciTePress, pages 145-153. DOI: 10.5220/0013855100003982

in Bibtex Style

@conference{icinco25,
author={Peter Davison and Muhammad Fahim and Roger Woods and Scott Fischaber and Marcus Haron and Cormac McAteer},
title={Predictive Quality of In-Fabrication Products in Smart Manufacturing Using Graph-Based Deep Learning},
booktitle={Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2025},
pages={145-153},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013855100003982},
isbn={978-989-758-770-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Predictive Quality of In-Fabrication Products in Smart Manufacturing Using Graph-Based Deep Learning
SN - 978-989-758-770-2
AU - Davison P.
AU - Fahim M.
AU - Woods R.
AU - Fischaber S.
AU - Haron M.
AU - McAteer C.
PY - 2025
SP - 145
EP - 153
DO - 10.5220/0013855100003982
PB - SciTePress