Applications of machine learning at the limits of form-dependent scattering for defect metrology

Author:

Henn Mark-Alexander,Zhou Hui,Silver Richard M.,Barnes Bryan M.

Publisher

SPIE

Reference37 articles.

1. Extending optical inspection to the VUV;Wells,2017

2. Assessing the wavelength extensibility of optical patterned defect inspection;Barnes,2017

3. Optimizing image-based patterned defect inspection through FDTD simulations at multiple ultraviolet wavelengths;Barnes,2017

4. Advanced lithography: wafer defect scattering analysis at DUV;Meshulach,2010

5. Leveraging advanced data analytics, machine learning, and metrology models to enable critical dimension metrology solutions for advanced integrated circuit nodes

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1. Enhancing 6 nm CD Patterned Defect Classification with TSOM and CNNs;2023 34th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC);2023-05-01

2. Optical wafer defect inspection at the 10 nm technology node and beyond;International Journal of Extreme Manufacturing;2022-04-21

3. SEM image quality enhancement: an unsupervised deep learning approach;Metrology, Inspection, and Process Control for Microlithography XXXIV;2020-03-20

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