Novel ellipsometry metrology-based machine learning technique for low sensitivity characterization of critical dimensions within gate-all-around transistors

Houssam Chouaib; Valeria Dimastrodonato; Anderson Chou; Agostino Cangianoa; Andrew Cross; Derrick Shaughnessy; Zhengquan Tan; Daniel Schmidt; Curtis Durfee; Shanti Pancharatnam; Julien Frougier; Andrew Greene; Mary Breton

doi:10.1117/12.3010790

9 April 2024 Novel ellipsometry metrology-based machine learning technique for low sensitivity characterization of critical dimensions within gate-all-around transistors

Houssam Chouaib, Valeria Dimastrodonato, Anderson Chou, Agostino Cangianoa, Andrew Cross, Derrick Shaughnessy, Zhengquan Tan, Daniel Schmidt, Curtis Durfee, Shanti Pancharatnam, Julien Frougier, Andrew Greene, Mary Breton

Author Affiliations +

Proceedings Volume 12955, Metrology, Inspection, and Process Control XXXVIII; 129550L (2024) https://doi.org/10.1117/12.3010790
Event: SPIE Advanced Lithography + Patterning, 2024, San Jose, California, United States

Abstract

The semiconductor industry has witnessed a fast progression of spectroscopic ellipsometry (SE) techniques aimed at resolving a plethora of complex device characterizations on a nanometric scale. The Mueller Matrix (MM) methodology coupled with rigorous coupled-wave analysis (RCWA) has offered an unprecedented power of investigation and analysis of diverse critical dimensions (CDs), especially when applied to gate-all-around (GAA) structures, as it helps increase the useful spectral signals of the often geometrically buried CDs. However, the sensitivity to the CDs can be often screened by other parameters, hampering the precision and accuracy of the measurement. Combining the most sensitive MM elements has therefore become a critical step of scatterometry critical dimension (SCD) metrology. Driven by the rapid developments of Machine Learning (ML) algorithms, we propose a versatile ellipsometry methodology that overcomes poor sensitivity and increases accuracy through a novel principal component analysis (PCA) method of the ML training algorithm with RCWA assistance. Furthermore, our methodology introduces a new ML training concept based on reference data statistics, rather than raw reference. Our approach has been validated with reference data and proved successful in monitoring GAA sheet-specific indent. The proposed methodology paves the way to measuring low sensitivity CDs with highly accurate, noise-reduced and robust ML-based physical SCD models for any logic and memory application.

Conference Presentation

Citation Download Citation

Houssam Chouaib, Valeria Dimastrodonato, Anderson Chou, Agostino Cangianoa, Andrew Cross, Derrick Shaughnessy, Zhengquan Tan, Daniel Schmidt, Curtis Durfee, Shanti Pancharatnam, Julien Frougier, Andrew Greene, and Mary Breton "Novel ellipsometry metrology-based machine learning technique for low sensitivity characterization of critical dimensions within gate-all-around transistors", Proc. SPIE 12955, Metrology, Inspection, and Process Control XXXVIII, 129550L (9 April 2024); https://doi.org/10.1117/12.3010790

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