Accurate characterization of mask defects by combination of phase retrieval and deterministic approach

Min-Chul Park; Thibault Leportier; Wooshik Kim; Jindong Song

doi:10.1117/12.2224712

1 June 2016 Accurate characterization of mask defects by combination of phase retrieval and deterministic approach

Min-Chul Park, Thibault Leportier, Wooshik Kim, Jindong Song

Proceedings Volume 9867, Three-Dimensional Imaging, Visualization, and Display 2016; 986712 (2016) https://doi.org/10.1117/12.2224712
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

Abstract

In this paper, we present a method to characterize not only shape but also depth of defects in line and space mask patterns. Features in a mask are too fine for conventional imaging system to resolve them and coherent imaging system providing only the pattern diffracted by the mask are used. Then, phase retrieval methods may be applied, but the accuracy it too low to determine the exact shape of the defect. Deterministic methods have been proposed to characterize accurately the defect, but it requires a reference pattern. We propose to use successively phase retrieval algorithm to retrieve the general shape of the mask and then deterministic approach to characterize precisely the defects detected.

Citation Download Citation

Min-Chul Park, Thibault Leportier, Wooshik Kim, and Jindong Song "Accurate characterization of mask defects by combination of phase retrieval and deterministic approach", Proc. SPIE 9867, Three-Dimensional Imaging, Visualization, and Display 2016, 986712 (1 June 2016); https://doi.org/10.1117/12.2224712

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