Enhanced Virtual Metrology on Chemical Mechanical Planarization Process using an Integrated Model and Data-Driven Approach

Yuan Di, Xiaodong Jia, and Jay Lee
Publication Target: 
IJPHM
Publication Issue: 
2
Submission Type: 
Full Paper
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ijphm_17_031.pdf650.66 KBNovember 10, 2017 - 10:40am

As an essential process in semiconductor manufacturing, Chemical Mechanical Planarization has been studied in recent decades and the material removal rate has been proved to be a critical performance indicator. Comparing with after-process metrology, virtual metrology shows advantages in production time saving and quick response to the process control. This paper presents an enhanced material removal rate prediction algorithm based on an integrated model and data-driven method. The proposed approach combines the physical mechanism and the influence of nearest neighbors, and extracts relevant features. The features are then input to construct multiple regression models, which are integrated to obtain the final prognosis. This method was evaluated by the PHM 2016 Data Challenge data sets and the result obtained the best mean squared error score among competitors.

Publication Year: 
2017
Publication Volume: 
8
Publication Control Number: 
031
Page Count: 
8
Submission Keywords: 
Virtual Metrology; Machine Learning; Chemical Mechanical Planarization
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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