AAAI 2025technical0 citations
Human Interpretable Virtual Metrology in the Semiconductor Manufacturing
Abstract
My PhD research focuses on developing a highly accurate and explainable multi-output virtual metrology system for semiconductor manufacturing. Using machine learning, we predict the physical properties of metal layers from process parameters captured by production equipment sensors. Key contributions include a model-agnostic explanatory method based on projective operators, providing insights into the most influential features for multi-output predictions and feature selection algorithms for these tasks.
BibTeX
@article{Mević_2025, title={Human Interpretable Virtual Metrology in the Semiconductor Manufacturing}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35219}, DOI={10.1609/aaai.v39i28.35219}, abstractNote={My PhD research focuses on developing a highly accurate and explainable multi-output virtual metrology system for semiconductor manufacturing. Using machine learning, we predict the physical properties of metal layers from process parameters captured by production equipment sensors. Key contributions include a model-agnostic explanatory method based on projective operators, providing insights into the most influential features for multi-output predictions and feature selection algorithms for these tasks.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Mević, Amina}, year={2025}, month={Apr.}, pages={29283-29284} }