Physics-Informed Multi-Task Learning for Material Removal Rate Prediction in Semiconductor Chemical Mechanical Planarization

Published

August 13, 2024

Author(s)

M. Wahidur Rahman, Gregory W. Vogl, Xiaodong Jia, Yongzhi Qu

Abstract

The chemical mechanical planarization (CMP) process is a complex and critical operation in the semiconductor manufacturing industry, involving a wide variety of process parameters. As a universally employed technique in semiconductor fabrication, it has received extensive research attention over the past several decades. Despite the development of various physics-based and data-driven models aimed at assessing the quality and productivity of CMP tools, particularly through the metric of material removal rate (MRR), a universally accepted prediction method has yet to emerge, primarily due to the complicated chemical/physical process. To help remedy this situation, this paper explores multi-task learning and the integration of physics-based knowledge constraints into convolutional neural networks (CNNs) to enhance the predictive accuracy of CNNs. The hybrid approach aims to leverage the strengths of both data-driven models and physics-based relationships, offering a robust framework for predicting MRRs with higher confidence. The validation results show that the auxiliary tasks in multi-task learning improve the accuracy of the primary task. Also, the physics-informed strategy can clearly increase the generalization capability of the baseline CNN model.

Proceedings Title

Proceedings of the 2024 IEEE International Conference on Prognostics and Health Management (ICPHM)

Conference Dates

June 17-19, 2024

Conference Location

Spokane, WA, US

Conference Title

2024 IEEE International Conference on Prognostics and Health Management (ICPHM)

Pub Type

Conferences

Download Paper

https://doi.org/10.1109/ICPHM61352.2024.10627679

Local Download

Keywords

Physics-Informed, Multi-Task Learning, Material Removal Rate (MRR), Chemical Mechanical Planarization (CMP), Semiconductor Manufacturing

Citation

Rahman, M. , Vogl, G. , Jia, X. and Qu, Y. (2024), Physics-Informed Multi-Task Learning for Material Removal Rate Prediction in Semiconductor Chemical Mechanical Planarization, Proceedings of the 2024 IEEE International Conference on Prognostics and Health Management (ICPHM), Spokane, WA, US, [online], https://doi.org/10.1109/ICPHM61352.2024.10627679, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957637 (Accessed March 13, 2025)