Virtual metrology modeling for plasma etch operations

The objective of this paper is to present the utilization of information produced during plasma etching for the prediction of etch bias. A plasma etching process typically relies on the concentration of chemical species in reaction chambers over time, where each concentration depends on chamber pressure, gas flow rate, power level and other chamber and wafer properties. Plasma properties, as well as equipment factors are nonlinear and vary over time. In this work, we will use various statistical techniques to address challenges due to the nature of plasma data: high dimensionality, collinearity, overall non-linearity of system, variation of data structure due to equipment condition changing, etc.

[1]  H. Hotelling.  The Generalization of Student’s Ratio , 1931 .

[2]  W. Krzanowski.  Between-Groups Comparison of Principal Components , 1979 .

[3]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[4]  J. Rodgers,et al.  Thirteen ways to look at the correlation coefficient , 1988 .

[5]  D. Rubin,et al.  Statistical Analysis with Missing Data , 1988 .

[6]  Reza Shadmehr,et al.  Principal Component Analysis of Optical Emission Spectroscopy and Mass Spectrometry: Application to Reactive Ion Etch Process Parameter Estimation Using Neural Networks , 1992 .

[7]  S. Qin,et al.  Partial least squares regression for recursive system identification , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[8]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[9]  D. Massart,et al.  Elimination of uninformative variables for multivariate calibration. , 1996, Analytical chemistry.

[10]  D. Massart,et al.  The Mahalanobis distance , 2000 .

[11]  A. C. Diebold.  Metrology strategy for next generation semiconductor manufacturing , 2000, Proceedings of ISSM2000. Ninth International Symposium on Semiconductor Manufacturing (IEEE Cat. No.00CH37130).

[12]  Riccardo Leardi,et al.  Application of genetic algorithm–PLS for feature selection in spectral data sets , 2000 .

[13]  Costas J. Spanos,et al.  The economic impact of choosing off-line, inline or in situ metrology deployment in semiconductor manufacturing , 2001, 2001 IEEE International Symposium on Semiconductor Manufacturing. ISSM 2001. Conference Proceedings (Cat. No.01CH37203).

[14]  D. Seborg,et al.  Pattern Matching in Historical Data , 2002 .

[15]  Dale E. Seborg,et al.  Data compression issues with pattern matching in historical data , 2003, Proceedings of the 2003 American Control Conference, 2003..

[16]  S. Zhao,et al.  Monitoring of Processes with Multiple Operating Modes through Multiple Principle Component Analysis Models , 2004 .

[17]  Fan-Tien Cheng,et al.  Application development of virtual metrology in semiconductor industry , 2005, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[18]  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..

[19]  Peter H. Meckl,et al.  Input Selection for Modeling and Diagnostics With Application to Diesel Engines , 2007 .