Mr. Steven Chen Profile

Steven Chen

at imec

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Publications (5)

Proceedings Article | 10 April 2024 Presentation + Paper

Towards improved semiconductor defect inspection for high-NA EUVL based on SEMI-SuperYOLO-NAS

Ying-Lin Chen, Jacob Deforce, Vic De Ridder, Bappaditya Dey, Victor Blanco, Sandip Halder, Philippe Leray

Proceedings Volume 12955, 129550W (2024) https://doi.org/10.1117/12.3010940

KEYWORDS: Education and training, Image resolution, Defect detection, Data modeling, Scanning electron microscopy, Defect inspection, Machine learning, Image processing, Semiconductors, Extreme ultraviolet lithography

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Continuous progression of Moore’s law brings new challenges in metrology and defect inspection. As the semiconductor industry embraces High-Numerical Aperture Extreme Ultraviolet Lithography (High-NA EUVL), there is a current industry-wide evaluation of this technology for potential pitch reduction in future nodes. One of the primary hurdles in implementing High-NA EUVL in High Volume Manufacturing (HVM) is its low depth of focus. Consequently, suppliers of resist materials are compelled to opt for thin resist and/or new underlayers/hardmask’s. Experimental combinations of thin resist materials with novel underlayers and hardmask’s seem to pose signal detection challenges due to poor Signal-to-Noise Ratio (SNR). In such a scenario, manual classification of these nano-scale defects faces limitations in terms of required time and workforce, and the robustness and generalizability of outcomes are also questionable. In recent years, vision-based machine learning (ML) algorithms have emerged as an effective solution for image-based semiconductor defect inspection applications. However, developing a robust ML model across various image resolutions without explicit training remains a challenge for nano-scale defect inspection. The goal of this research is to propose a scale-invariant Automated Defect Classification and Detection (ADCD) framework capable to upscale images, addressing this issue. We propose an improvised ADCD framework as SEMI-SuperYOLO-NAS, which builds upon the baseline YOLO-NAS architecture. This framework integrates a Super-Resolution (SR) assisted branch to aid in learning high-resolution (HR) features by the defect detection backbone, particularly for detecting nano-scale defect instances from low-resolution (LR) images. Additionally, the SR-assisted branch can recursively generate or reconstruct upscaled images (∼ ×2/×4/×8...) from their corresponding downscaled counterparts, enabling defect detection inference across various image resolutions without requiring explicit training. Moreover, we investigate improved data augmentation strategy aimed at generating diverse and realistic training datasets to enhance model performance. We have evaluated our proposed approach using two original FAB datasets obtained from two distinct processes and captured using two different imaging tools. Finally, we demonstrate zero-shot inference for our model on a new, originating from a process condition distinct from the training dataset and possessing different CD/Pitch characteristics. Our experimental validation demonstrates that our proposed ADCD framework aids in increasing the throughput of imaging tools (∼ ×8) for defect inspection by reducing the required image pixel resolutions.

Proceedings Article | 1 December 2022 Poster + Paper

Scaling and readiness of underlayers for high-NA EUV lithography

Roberto Fallica, Danilo De Simone, Steven Chen, Muhammad Safdar, Hyo Seon Suh

Proceedings Volume 12292, 122920V (2022) https://doi.org/10.1117/12.2645864

KEYWORDS: Extreme ultraviolet lithography, Photoresist materials, Extreme ultraviolet, Lithography, Photoresist developing

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SPIE Journal Paper | 15 November 2022

Scaling and readiness of underlayers for high-numerical aperture extreme ultraviolet lithography

Roberto Fallica, Danilo De Simone, Steven Chen, Muhammad Safdar, Hyo Seon Suh

JM3, Vol. 21, Issue 04, 044601, (November 2022) https://doi.org/10.1117/12.10.1117/1.JMM.21.4.044601

KEYWORDS: Extreme ultraviolet lithography, Photoresist materials, Surface roughness, Line width roughness, Semiconducting wafers, Optical lithography, Extreme ultraviolet, Lithography, Coating, Thin films

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SPIE Journal Paper | 25 July 2022

Adhesion and collapse of extreme ultraviolet photoresists and the role of underlayers

Roberto Fallica, Steven Chen, Danilo De Simone, Hyo Seon Suh

JM3, Vol. 21, Issue 03, 034601, (July 2022) https://doi.org/10.1117/12.10.1117/1.JMM.21.3.034601

KEYWORDS: Photoresist materials, Photoresist developing, Extreme ultraviolet lithography, Interfaces, Capillaries, Extreme ultraviolet, Liquids, Critical dimension metrology, Optical lithography, Statistical analysis

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Proceedings Article | 13 June 2022 Presentation

Adhesion and collapse of EUV photoresists and the role of underlayers

Roberto Fallica, Steven Chen, Danilo De Simone, Hyo Seon Suh

Proceedings Volume PC12055, PC1205507 (2022) https://doi.org/10.1117/12.2609485

KEYWORDS: Photoresist materials, Extreme ultraviolet lithography, Photoresist developing, Critical dimension metrology, Capillaries, Systems modeling, Optical lithography, Natural surfaces, Line width roughness, Interfaces

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