Paper Info
Title
Data-Driven Approach for Fault Detection and Diagnostic in Semiconductor Manufacturing
Abstract
Fault detection and classification (FDC) is important for semiconductor manufacturing to monitor equipment’s condition and examine the potential cause of the fault. Each equipment in the semiconductor manufacturing process is often accompanied by a large amount of sensor readings, also called status variable identification (SVID). Identifying the key SVIDs accurately can make it easier for engineers to monitor the process and maintain the stability of the process and wafer productive yields. This article proposes using the random forests algorithm to analyze the importance of SVIDs of equipment sensors, automatically filters the key SVID by using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -means, and integrates various machine learning methods to verify the key SVIDs and identify key processing time and steps. Upon the key parameters are identified, the key processing time and steps are investigated subsequently. The ensemble models constructed on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbors ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> NNs) and naïve Bayes classifiers are presented for classifying wafers as normal or abnormal. Data visualization of multidimensional key SVIDs is performed by using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula> -distributed stochastic neighbor embedding ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula> -SNE) to create a graphical aid in FDC for the process engineer. An empirical study is conducted to validate the proposed data-driven framework for fault detection and diagnostic. The experimental results demonstrate that the proposed framework can detect abnormality effectively with highly imbalanced classes and also gain insightful information about the key SVIDs and corresponding key processing time and steps. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</italic> —The challenges of equipment sensor data analytics in semiconductor manufacturing include building the classifier to detect wafer abnormality correctly, identification of key status variable identifications (SVIDs) and processing time and steps of abnormality, and data visualization of the abnormality in a high-dimensional feature space. This article proposes a data-driven framework for fault detection and classification (FDC) during the wafer fabrication process by incorporating several useful machine learning approaches. Experimental results demonstrate that the proposed data-driven framework can supply quality fault detection performances and provide valuable information regarding the critical SVIDs and associated key processing time for fault diagnostic. The engineers can utilize the extracted fault patterns to perform a prognosis of the aging effect on process tools or modules for health management.
Year
DOI
Venue
2020
10.1109/TASE.2020.2983061
IEEE Transactions on Automation Science and Engineering
Keywords
DocType
Volume
Fault detection,Monitoring,Manufacturing,Forestry,Machine learning,Mathematical model,Process control
Journal
17
Issue
ISSN
Citations 
4
1545-5955
4
PageRank 
References 
Authors
0.43
0
5
Name
Order
Citations
PageRank
Shu-Kai S. Fan128017.82
Chia-Yu Hsu2112.96
Du-Ming Tsai397068.17
Fei He441.11
Chun-Chung Cheng540.43