A hybrid solution using computational prediction and measured data to accurately determine process corrections with reduced overlay sampling

Ben F. Noyes III; Babak Mokaberi; Ram Mandoy; Alex Pate; Ralph Huijgen; Mike McBurney; Owen Chen

doi:10.1117/12.2257486

28 March 2017 A hybrid solution using computational prediction and measured data to accurately determine process corrections with reduced overlay sampling

Ben F. Noyes III, Babak Mokaberi, Ram Mandoy, Alex Pate, Ralph Huijgen, Mike McBurney, Owen Chen

Author Affiliations +

Proceedings Volume 10145, Metrology, Inspection, and Process Control for Microlithography XXXI; 1014508 (2017) https://doi.org/10.1117/12.2257486
Event: SPIE Advanced Lithography, 2017, San Jose, California, United States

Abstract

Reducing overlay error via an accurate APC feedback system is one of the main challenges in high volume production of the current and future nodes in the semiconductor industry. The overlay feedback system directly affects the number of dies meeting overlay specification and the number of layers requiring dedicated exposure tools through the fabrication flow. Increasing the former number and reducing the latter number is beneficial for the overall efficiency and yield of the fabrication process. An overlay feedback system requires accurate determination of the overlay error, or fingerprint, on exposed wafers in order to determine corrections to be automatically and dynamically applied to the exposure of future wafers. Since current and future nodes require correction per exposure (CPE), the resolution of the overlay fingerprint must be high enough to accommodate CPE in the overlay feedback system, or overlay control module (OCM). Determining a high resolution fingerprint from measured data requires extremely dense overlay sampling that takes a significant amount of measurement time. For static corrections this is acceptable, but in an automated dynamic correction system this method creates extreme bottlenecks for the throughput of said system as new lots have to wait until the previous lot is measured. One solution is using a less dense overlay sampling scheme and employing computationally up-sampled data to a dense fingerprint. That method uses a global fingerprint model over the entire wafer; measured localized overlay errors are therefore not always represented in its up-sampled output. This paper will discuss a hybrid system shown in Fig. 1 that combines a computationally up-sampled fingerprint with the measured data to more accurately capture the actual fingerprint, including local overlay errors. Such a hybrid system is shown to result in reduced modelled residuals while determining the fingerprint, and better on-product overlay performance.

Conference Presentation

Citation Download Citation

Ben F. Noyes III, Babak Mokaberi, Ram Mandoy, Alex Pate, Ralph Huijgen, Mike McBurney, and Owen Chen "A hybrid solution using computational prediction and measured data to accurately determine process corrections with reduced overlay sampling", Proc. SPIE 10145, Metrology, Inspection, and Process Control for Microlithography XXXI, 1014508 (28 March 2017); https://doi.org/10.1117/12.2257486

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