Deep learning-based defect detection using large FOV SEM for 28 nm pitch BEOL layer patterned with 0.33NA single exposure EUV

Sayantan Das; Kaushik Sah; Ardis Liang; Hemanta Roy; Kha Tran; Binesh Babu; Arjun Hegde; Andrew Cross; Philippe Leray; Sandip Halder

doi:10.1117/12.2600954

13 October 2021 Deep learning-based defect detection using large FOV SEM for 28 nm pitch BEOL layer patterned with 0.33NA single exposure EUV

Sayantan Das, Kaushik Sah, Ardis Liang, Hemanta Roy, Kha Tran, Binesh Babu, Arjun Hegde, Andrew Cross, Philippe Leray, Sandip Halder

Author Affiliations +

Proceedings Volume 11854, International Conference on Extreme Ultraviolet Lithography 2021; 118540Y (2021) https://doi.org/10.1117/12.2600954
Event: SPIE Photomask Technology + EUV Lithography, 2021, Online Only

Abstract

As we strive toward smaller and smaller pitches and new 3D constructs to enable device scaling, thorough defect characterization at wafer scale is essential during the early phases of process optimization. Often CD (critical dimension) variations and roughness lead to high SEM (scanning electron microscope) inspection noise. It is important to suppress this noise and increase DOI (defect of interest) SNR (signal-to-noise ratio) for better detection efficiency while maintaining high speed for meaningful wafer coverage. In this work, we describe experiments and show characterization results for capturing EUV (extreme ultraviolet) stochastic defects across various test structures of 28nm pitch devices that have been patterned using single exposure 0.33NA EUV lithography. We have used KLA eSL10TM for SEM inspection and analysis. The tool can also be used for high resolution and high-speed metrology, providing quick feedback on observed defect signatures and further root cause analysis.

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Sayantan Das, Kaushik Sah, Ardis Liang, Hemanta Roy, Kha Tran, Binesh Babu, Arjun Hegde, Andrew Cross, Philippe Leray, and Sandip Halder "Deep learning-based defect detection using large FOV SEM for 28 nm pitch BEOL layer patterned with 0.33NA single exposure EUV", Proc. SPIE 11854, International Conference on Extreme Ultraviolet Lithography 2021, 118540Y (13 October 2021); https://doi.org/10.1117/12.2600954

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