Mr. Kasem Khalil Profile

Kasem Khalil

at Univ of Louisiana at Lafayette

SPIE Involvement:

Author

Publications (3)

Proceedings Article | 12 October 2021 Presentation + Paper

Unsupervised machine learning based SEM image denoising for robust contour detection

Bappaditya Dey, Stewart Wu, Sayantan Das, Kasem Khalil, Sandip Halder, Philippe Leray, Samir Bhamidipati, Kiarash Ahi, Mark Pereira, Germain Fenger, Magdy Bayoumi

Proceedings Volume 11854, 1185411 (2021) https://doi.org/10.1117/12.2600945

KEYWORDS: Scanning electron microscopy, Denoising, Machine learning, Bridges, Metrology, Stochastic processes, Image processing, Neural networks, Detection and tracking algorithms, Optical proximity correction

Read Abstract +

Contour detection of an object is a fundamental computer vision problem in image processing domain. The goal is to find a concrete boundary for pixel ownership between an OOI (object-of-interest) and its corresponding background. However, contour extraction from low SN SEM images is a very challenging problem as different sources of noise shadow the estimation of underlying structural geometries. As device scaling continues to 3nm node and below, the extraction of accurate CD contour geometries from SEM images especially ADI (after developed inspection) is of utmost importance for a qualitative lithographic process as well as to verify device characterization in aggressive pitches. In this paper, we have applied a U-Net architecture based unsupervised machine learning approach for de-noising CD-SEM images. Unlike other discriminative deep-learning based de-noising approaches, the proposed method does not require any ground-truth as clean/noiseless images or synthetic noiseless images for training. Simultaneously, we have also attempted to demonstrate how de-noising is helping to improve the contour detection accuracy. We have analyzed and validated our result by using a programmable tool (SEMSuiteTM) for contour extraction. We have de-noised SEM images with categorically different geometrical patterns such as L/S (line-space), T2T (tip-to-tip), pillars with different scan types etc. and extracted the contours in both noisy and de-noised images. The comparative analysis demonstrates that de-noised images have higher confidence contour metric than their noisy twins while keeping the same parameter settings for both data input. When the ML algorithm is applied, the contour extraction results would have higher confidence numbers comparing with the ones only applied the conventional Gaussian or Median blur de-noise method. The final goal of this work is to establish a robust de-noising method to reduce the dependency of SEM image acquisition settings and provide more accurate metrology data for OPC calibration.

Proceedings Article | 22 February 2021 Presentation + Paper

SEM image denoising with unsupervised machine learning for better defect inspection and metrology

Bappaditya Dey, Sandip Halder, Kasem Khalil, Gian Lorusso, Joren Severi, Philippe Leray, Magdy A. Bayoumi

Proceedings Volume 11611, 1161115 (2021) https://doi.org/10.1117/12.2584803

KEYWORDS: Scanning electron microscopy, Metrology, Machine learning, Defect inspection, Inspection, Image denoising, Semiconductors, Denoising, Defect detection, Critical dimension metrology

Read Abstract +

Proceedings Article | 23 March 2020 Paper

Unsupervised machine learning based CD-SEM image segregator for OPC and process window estimation

Bappaditya Dey, Dorin Cerbu, Kasem Khalil, Sandip Halder, Philippe Leray, Sayantan Das, Yasser Sherazi, Magdy Bayoumi, Ryoung Han Kim

Proceedings Volume 11328, 113281G (2020) https://doi.org/10.1117/12.2552055

KEYWORDS: Image processing, Optical proximity correction, Machine learning, Computer programming, Photomasks, Stochastic processes, Defect inspection, Semiconducting wafers, Analytical research, Vector spaces

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years