Model-based contour extraction: an enabler for very low-frame SEM images metrology

Élie Sezestre; Juline Scoarnec; Jonathan Pradelles; Loïc Perraud; Aurélien Fay; Sébastien Bérard-Bergery; Jessy Bustos; Jean-Baptiste Henry; Olivier Dubreuil; Ivanie Mendes; Charles Valade; Alexandre Moly; Alice Batte; Nivea Schuch; Frederic Robert; Thiago Figueiro

doi:10.1117/12.2616527

26 May 2022 Model-based contour extraction: an enabler for very low-frame SEM images metrology

Élie Sezestre, Juline Scoarnec, Jonathan Pradelles, Loïc Perraud, Aurélien Fay, Sébastien Bérard-Bergery, Jessy Bustos, Jean-Baptiste Henry, Olivier Dubreuil, Ivanie Mendes, Charles Valade, Alexandre Moly, Alice Batte, Nivea Schuch, Frederic Robert, Thiago Figueiro

Author Affiliations +

Proceedings Volume 12053, Metrology, Inspection, and Process Control XXXVI; 120531G (2022) https://doi.org/10.1117/12.2616527
Event: SPIE Advanced Lithography + Patterning, 2022, San Jose, California, United States

Conference Poster

Abstract

Among metrology tools in the semi-conductor manufacturing, critical dimension scanning electron microscopes (CD-SEM) are the most broadly used, especially due to their high resolution, low destructivity, and high throughput. Contour metrology on CD-SEM images has become essential for characterization, modelling, and control of advanced lithography processes. In particular, OPC model’s accuracy can be highly improved using contours metrology. One of the issues when dealing with CD-SEM metrology is that the results are noise sensitive. Moreover, diminishing noise in CD-SEM acquisition leads to resist shrinkage due to exposure time increase. In addition, post-treatment of these shrinkage effects requires compensation algorithms such as artificial intelligence (AI)- driven algorithms, that are another contributor to the error budget of metrology systems. There is thus a need for an accurate, robust to noise, and purely deterministic edge detection algorithm. In this article, we evaluate the benefits of relying on a model-based contour extraction approach for performing measurements. This approach is applied onto both synthetic and experimental CD-SEM images with various patterns (mostly 2D) and noise levels to assess the influence of image integration (frame number) on the contour detection and CD measurement. We demonstrate that a model-based contour extraction algorithm is able to precisely characterize SEM-induced 2D resist shrinkage. We observe that this model-based approach is more robust to noise than standard algorithms by 21% on synthetic data and by 36% on experimental data. Another way of seeing it is, while keeping the same precision, a model-based contour extraction approach can significantly reduce the requested image frame number. The benefits of adopting this approach range from reducing the shrinkage effects to improving SEM image acquisition time. Eventually, no step of shrinkage modelling calibration nor AI-driven image post processing are needed which implies a gain on simplicity and avoids modelling errors.

Citation Download Citation

Élie Sezestre, Juline Scoarnec, Jonathan Pradelles, Loïc Perraud, Aurélien Fay, Sébastien Bérard-Bergery, Jessy Bustos, Jean-Baptiste Henry, Olivier Dubreuil, Ivanie Mendes, Charles Valade, Alexandre Moly, Alice Batte, Nivea Schuch, Frederic Robert, and Thiago Figueiro "Model-based contour extraction: an enabler for very low-frame SEM images metrology", Proc. SPIE 12053, Metrology, Inspection, and Process Control XXXVI, 120531G (26 May 2022); https://doi.org/10.1117/12.2616527

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