Improved overlay control using robust outlier removal methods

John C. Robinson; Osamu Fujita; Hiroyuki Kurita; Pavel Izikson; Dana Klein; Inna Tarshish-Shapir

doi:10.1117/12.879494

20 April 2011 Improved overlay control using robust outlier removal methods

John C. Robinson, Osamu Fujita, Hiroyuki Kurita, Pavel Izikson, Dana Klein, Inna Tarshish-Shapir

Proceedings Volume 7971, Metrology, Inspection, and Process Control for Microlithography XXV; 79711G (2011) https://doi.org/10.1117/12.879494
Event: SPIE Advanced Lithography, 2011, San Jose, California, United States

Abstract

Overlay control is one of the most critical areas in advanced semiconductor processing. Maintaining optimal product disposition and control requires high quality data as an input. Outliers can contaminate lot statistics and negatively impact lot disposition and feedback control. Advanced outlier removal methods have been developed to minimize their impact on overlay data processing. Rejection methods in use today are generally based on metrology quality metrics, raw data statistics and/or residual data statistics. Shortcomings of typical methods include the inability to detect multiple outliers as well as the unnecessary rejection of valid data. As the semiconductor industry adopts high-order overlay modeling techniques, outlier rejection becomes more important than for linear modeling. In this paper we discuss the use of robust regression methods in order to more accurately eliminate outliers. We show the results of an extensive simulation study, as well as a case study with data from a semiconductor manufacturer.

Citation Download Citation

John C. Robinson, Osamu Fujita, Hiroyuki Kurita, Pavel Izikson, Dana Klein, and Inna Tarshish-Shapir "Improved overlay control using robust outlier removal methods", Proc. SPIE 7971, Metrology, Inspection, and Process Control for Microlithography XXV, 79711G (20 April 2011); https://doi.org/10.1117/12.879494

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available