Model aggregation for virtual metrology in high-volume manufacturing

Minsuk Shin; Minju Jung; Simon Zabrocki; Doh-Hyung Ro; Hyeon-Kyeong Jeong; Dongkyun Yim

doi:10.1117/12.3010063

9 April 2024 Model aggregation for virtual metrology in high-volume manufacturing

Minsuk Shin, Minju Jung, Simon Zabrocki, Doh-Hyung Ro, Hyeon-Kyeong Jeong, Dongkyun Yim

Proceedings Volume 12955, Metrology, Inspection, and Process Control XXXVIII; 129552F (2024) https://doi.org/10.1117/12.3010063
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Conference Poster

Abstract

Virtual metrology (VM) plays a pivotal role in enhancing productivity, improving quality, and reducing maintenance costs in semiconductor manufacturing by replacing traditional physical metrology. Many implementations of VM rely on the predictive modeling approach that leverages equipment sensor data to estimate crucial process outcome variables. Despite its inherent advantages, VM has not been deployed widely in actual manufacturing processes due to the lack of accuracy and scalability. Most machine learning algorithms struggle to effectively address practical challenges such as data drift, data shift, sparse ground-truth data, and inconsistent data quality prevalent in semiconductor manufacturing processes. To address these limitations, we introduce the aggregated adaptive online model (AggAOM), a novel approach that effectively tackles the challenge of data scarcity in VM. By leveraging the hierarchical structure of manufacturing equipment, AggAOM captures and utilizes the underlying commonalities among equipment chambers within the same hierarchy besides their individual variations. This methodology enables more efficient use of limited data and substantially improves the prediction accuracy of VM running in mega-fabs for high-volume manufacturing. We present the experimental results by utilizing the datasets collected from SK hynix over nine months and demonstrate that AggAOM outperforms existing models significantly in accuracy. This progress marks a significant step forward in optimizing VM for semiconductor manufacturing.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Minsuk Shin, Minju Jung, Simon Zabrocki, Doh-Hyung Ro, Hyeon-Kyeong Jeong, and Dongkyun Yim "Model aggregation for virtual metrology in high-volume manufacturing", Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 129552F (9 April 2024); https://doi.org/10.1117/12.3010063

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available