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.
Cite
Text
Mevic. "Human Interpretable Virtual Metrology in the Semiconductor Manufacturing." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35219Markdown
[Mevic. "Human Interpretable Virtual Metrology in the Semiconductor Manufacturing." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/mevic2025aaai-human/) doi:10.1609/AAAI.V39I28.35219BibTeX
@inproceedings{mevic2025aaai-human,
title = {{Human Interpretable Virtual Metrology in the Semiconductor Manufacturing}},
author = {Mevic, Amina},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2025},
pages = {29283-29284},
doi = {10.1609/AAAI.V39I28.35219},
url = {https://mlanthology.org/aaai/2025/mevic2025aaai-human/}
}