Mr. Dereje Shewaseged Woldeamanual Profile

Dereje Shewaseged Woldeamanual

R&D Engineer

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Publications (3)

SPIE Journal Paper | 16 November 2021 Open Access

Accurate prediction of EUV lithographic images and 3D mask effects using generative networks

Abdalaziz Awad, Philipp Brendel, Peter Evanschitzky, Dereje Woldeamanual, Andreas Rosskopf, Andreas Erdmann

JM3, Vol. 20, Issue 04, 043201, (November 2021) https://doi.org/10.1117/12.10.1117/1.JMM.20.4.043201

KEYWORDS: 3D modeling, Photomasks, 3D image processing, Extreme ultraviolet, Data modeling, Lithography, Extreme ultraviolet lithography, Process modeling, Computer programming, 3D acquisition

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Proceedings Article | 12 September 2021 Presentation

Accurate prediction of EUV lithographic images using data-efficient generative networks

Abdalaziz Awad, Philipp Brendel, Dereje Woldeamanual, Andreas Rosskopf, Andreas Erdmann

Proceedings Volume 11875, 118750I (2021) https://doi.org/10.1117/12.2597309

KEYWORDS: 3D modeling, Photomasks, Data modeling, Extreme ultraviolet, Lithography, 3D image processing, Extreme ultraviolet lithography, Image processing, Computer simulations, Machine learning

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Proceedings Article | 28 May 2018 Paper

Application of deep learning algorithms for Lithographic mask characterization

Dereje S. Woldeamanual, Andreas Erdmann, Andreas Maier

Proceedings Volume 10694, 1069408 (2018) https://doi.org/10.1117/12.2312478

KEYWORDS: Photomasks, Extreme ultraviolet, Lithography, Network architectures, Image classification, Defect detection, Extreme ultraviolet lithography, Convolution, Reflectivity, Image processing

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The appearance of defects on the photomask is a key challenge in lithographic printing. Printable defects affect the lithographic process by causing errors in both the phase and magnitude of the light and of the sizes and location of the printed features. Presently 193 nm optical inspection tools are still the main ones for detecting pattern defects on EUV masks.1 However, pattern sizes on EUV masks could not be detected due to the resolution limit of 193 nm inspection tools. We propose and investigate the application of Convolutional Neural Networks (CNNs) to characterize and classify defects on lithographic masks. This paper details the training and evaluation of the CNNs to classify defects in simulated aerial images of an EUV setting. The simulation software Dr.LiTHO is used to simulate aerial images of defect-free masks and of masks with different types and locations of defects. Specifically we compute images of regular arrays of squares to be imaged with typical settings of EUV lithography (λ = 13.5 nm, NA= 0.33). We consider five types of absorber defects (extrusion, intrusion, oversize, undersize and center spot). The architecture of the CNN contains 4 convolutional layers (conv. layers) with a mixed size of filter,(3x3) and (5x5). The convolution stride and the spatial padding is 1 pixel for all conv. layers. Spatial pooling is carried out by 4 max-pooling layers. Two separate networks are trained for detection of the defect types and location, whereas a third algorithm combines the results. When an image is presented to the implemented algorithm and trained networks, it will return the defect type with its location. An accuracy of 99.9% on the training set and 99.3% on the test set is achieved for detection of the defect type. The network trained for location detection results in 98.7% training accuracy and 98.0% for the test set. Having a sufficient amount of training images the trained CNNs classify the types of defects and their location in the aerial image with high accuracy. The proposed method can be also applied to other defect types and simulation settings.

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