Mr. Stewart Wu Profile

Stewart Wu

at Siemens EDA

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Publications (3)

Proceedings Article | 31 October 2022 Poster

Deep-learning denoiser-assisted framework for robust SEM contour extraction and analysis for advanced semiconductor node

Bappaditya Dey, Sandip Halder, Argho Das, SAYANTAN DAS, STEWART WU, Germain Fenger

Proceedings Volume PC12292, PC122920Z (2022) https://doi.org/10.1117/12.2645403

KEYWORDS: Scanning electron microscopy, Semiconductors, Data modeling, Photomasks, Metrology, Calibration, Wafer-level optics, Stochastic processes, Shape analysis, Semiconducting wafers

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

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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

Better prediction on patterning failure mode with hotspot aware OPC modeling

Chih-I Wei, Stewart Wu, Yunfei Deng, Gurdaman Khaira, Ir Kusnadi, Germain Fenger, Seulki Kang, Yosuke Okamoto, Kotaro Maruyama, Yuichiro Yamazaki, Sayantan Das, Sandip Halder, Werner Gillijns, Gian Lorusso

Proceedings Volume 11611, 1161112 (2021) https://doi.org/10.1117/12.2583837

KEYWORDS: Optical proximity correction, Electron beam lithography, 3D modeling, Inspection, Calibration, Lithography, Data modeling, Time metrology, Semiconducting wafers, Scanning electron microscopy

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