Weighted least squares regression for advanced overlay control

Dana Klein; John C. Robinson; Guy Cohen; Chin-Chou Kevin Huang; Bill Pierson

doi:10.1117/12.916396

5 April 2012 Weighted least squares regression for advanced overlay control

Dana Klein, John C. Robinson, Guy Cohen, Chin-Chou Kevin Huang, Bill Pierson

Proceedings Volume 8324, Metrology, Inspection, and Process Control for Microlithography XXVI; 832425 (2012) https://doi.org/10.1117/12.916396
Event: SPIE Advanced Lithography, 2012, San Jose, California, United States

Abstract

Controlling overlay performance has become one of the key lithographic challenges for advanced integrated circuit manufacturing. Overlay error budgets of 4 nm in the 2x node require careful consideration of all potential error sources. Overlay data modeling is a key component for reducing systematic wafer and field variation, and is typically based on ordinary least squares (OLS) regression. OLS assumes that each data point provides equally reliable information about the process variation. Weighted least squares (WLS) regression can be used to improve overlay modeling by giving each data point an amount of influence on the model which depends on its quality. Here we use target quality merit metrics from the overlay metrology tool to provide the regression weighting factors for improved overlay control in semiconductor manufacturing.

Citation Download Citation

Dana Klein, John C. Robinson, Guy Cohen, Chin-Chou Kevin Huang, and Bill Pierson "Weighted least squares regression for advanced overlay control", Proc. SPIE 8324, Metrology, Inspection, and Process Control for Microlithography XXVI, 832425 (5 April 2012); https://doi.org/10.1117/12.916396

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