Data fusion by artificial neural network for hybrid metrology development

L. Penlap Woguia; J. Reche; M. Besacier; P. Gergaud; G. Rademaker

doi:10.1117/12.2583590

22 February 2021 Data fusion by artificial neural network for hybrid metrology development

L. Penlap Woguia, J. Reche, M. Besacier, P. Gergaud, G. Rademaker

Proceedings Volume 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV; 116112L (2021) https://doi.org/10.1117/12.2583590
Event: SPIE Advanced Lithography, 2021, Online Only

Conference Poster

Abstract

Hybrid metrology is a promising approach to access to the critical dimensions of line gratings with precisions. The objective of this work is about using artificial intelligence (AI), mainly artificial neural network (ANN) to improve metrology at nanoscale characterization by hybridization of several techniques. Namely, optical critical dimension (OCD) or scatterometry, CD–Scanning electron microscopy (CDSEM), CD–Atomic force microscopy (CDAFM) and CD–Small angle x-rays scattering (CDSAXS). With virtual data of tabular–type generated by modelling, the ANN is able to predict the geometrical parameters compared to true measured values with high accuracies and detect irregularities in input data.

Citation Download Citation

L. Penlap Woguia, J. Reche, M. Besacier, P. Gergaud, and G. Rademaker "Data fusion by artificial neural network for hybrid metrology development", Proc. SPIE 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV, 116112L (22 February 2021); https://doi.org/10.1117/12.2583590

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