Machine learning-based novelty detection for faulty wafer detection in semiconductor manufacturing

Since semiconductor manufacturing consists of hundreds of processes, a faulty wafer detection system, which allows for earlier detection of faulty wafers, is required. statistical process control (SPC) and virtual metrology (VM) have been used to detect faulty wafers. However, there are some limitations in that SPC requires linear, unimodal and single variable data and VM underestimates the deviations of predictors. In this paper, seven different machine learning-based novelty detection methods were employed to detect faulty wafers. The models were trained with Fault Detection and Classification (FDC) data to detect wafers having faulty metrology values. The real world semiconductor manufacturing data collected from a semiconductor fab were tested. Since the real world data have more than 150 input variables, we employed three different dimensionality reduction methods. The experimental results showed a high True Positive Rate (TPR). These results are promising enough to warrant further study.

[1]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[2]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[3]  Cheng-Ching Yu,et al.  Control relevant issues in semiconductor manufacturing : Overview with some new results , 2007 .

[4]  Vic Barnett,et al.  Outliers in Statistical Data , 1980 .

[5]  Hyoungjoo Lee,et al.  Focusing on non-respondents: Response modeling with novelty detectors , 2007, Expert Syst. Appl..

[6]  C.H. Yu,et al.  Virtual metrology: a solution for wafer to wafer advanced process control , 2005, ISSM 2005, IEEE International Symposium on Semiconductor Manufacturing, 2005..

[7]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[8]  George W. Irwin,et al.  Neural networks and multivariate SPC , 1997 .

[9]  S. Joe Qin,et al.  Statistical process monitoring: basics and beyond , 2003 .

[10]  Hyoungjoo Lee,et al.  Virtual metrology for run-to-run control in semiconductor manufacturing , 2011, Expert Syst. Appl..

[11]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[12]  Jin Wang,et al.  Fault Detection Using the k-Nearest Neighbor Rule for Semiconductor Manufacturing Processes , 2007, IEEE Transactions on Semiconductor Manufacturing.

[13]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[14]  Hyoungjoo Lee,et al.  Supporting diagnosis of attention-deficit hyperactive disorder with novelty detection , 2008, Artif. Intell. Medicine.

[15]  Yuan Kang,et al.  Virtual Metrology Technique for Semiconductor Manufacturing , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[16]  Sameer Singh,et al.  Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..

[17]  H. Yue,et al.  Fault detection of plasma etchers using optical emission spectra , 2000 .

[18]  Hyoungjoo Lee,et al.  A virtual metrology system for semiconductor manufacturing , 2009, Expert Syst. Appl..

[19]  Theodora Kourti,et al.  Process analysis, monitoring and diagnosis, using multivariate projection methods , 1995 .

[20]  S. Joe Qin,et al.  Semiconductor manufacturing process control and monitoring: A fab-wide framework , 2006 .

[21]  I. Jolliffe.  Principal Component Analysis , 2002 .