Deep learning's impact on contour extraction for design based metrology and design based inspection

Yumiba, Ryo; Ishikawa, Masayoshi; Shinoda, Shinichi; Sakimura, Shigetoshi; Toyoda, Yasutaka; Shindo, Haruo; Izawa, Masayuki

doi:10.1117/12.2514898

Cited by 4 publications

(3 citation statements)

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“…Such encoder–decoder architectures are often applied to metrology and inspection in semiconductor manufacturing 18 – 20 We used the modified U-Net, 16 where we replaced some convolution layers with residual blocks 21 and removed some layers in the decoder that did not contribute to inspection precision to reduce computational cost. Our model predicts the mean and SD from the input design layout under the assumption that pixel values of SEM images are drawn from a Gaussian distribution.…”

Section: Trainable Die-to-database Inspectionmentioning

confidence: 99%

Trainable die-to-database for large field of view e-beam inspection

Fukuda

Ouchi

Ishikawa

et al. 2022

J. Micro/Nanopattern. Mats. Metro.

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Extreme ultraviolet lithography has advanced microfabrication of semiconductor devices toward the sub-10-nm generation. In this situation, stochastic defects increase and hence process evaluation requires an entire wafer inspection at high speed. To satisfy this requirement, a large field of view (FoV) inspection with low-resolution enables us to inspect an entire wafer within an acceptable time because the throughput of e-beam inspection depends on imaging resolution. However, low-resolution images are difficult to inspect at high precision using conventional methods because of a smaller photographed defect size and worse signal-to-noise ratio. Moreover, deformation caused by the manufacturing process and larger distortion caused by large FoV result in false detections when we apply die-to-database (D2DB) inspection. To solve these issues, we propose trainable D2DB inspection, which predicts a pixel-value distribution of normal images from a corresponding design layout. The proposed method is robust to lowresolution images because it considers noise and acceptable deformation as variance of the learned distribution. In addition, by introducing a model to predict a misalignment between a design layout and inspection image, trainable D2DB becomes robust to image distortion. Experiments show that trainable D2DB can perform high-precision inspection on images with large noise and image distortion.

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Section: Trainable Die-to-database Inspectionmentioning

confidence: 99%

Trainable die-to-database for large field of view e-beam inspection

Fukuda

Ouchi

Ishikawa

et al. 2022

J. Micro/Nanopattern. Mats. Metro.

Self Cite

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“…24) In contrast, applications of machine learning in resist studies were few. 9,[25][26][27] Regarding resist analysis, the construction of 3D models was necessary for most of the research studies reported. 28) Although such models can provide accurate information, the computational cost is high.…”

Section: Introductionmentioning

confidence: 99%

“…Another point that should be mentioned is that the main analysis process in these strategies is based on an artificial neural network, or a convolutional neural network. 26,29) The prediction accuracy is high. However, the physical meaning is unclear.…”

Section: Introductionmentioning

confidence: 99%

Classification of lines, spaces, and edges of resist patterns in scanning electron microscopy images using unsupervised machine learning

Jin

Kozawa

2022

Jpn. J. Appl. Phys.

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As key steps of lithography, the development of resist materials and the exploration of new materials are important to meet market demands from semiconductor industry. During the development, resist materials are usually evaluated by the information extracted from their scanning electron microscopy (SEM) images. The information extracted from SEM images is not always accurate owing to the technical limitation. Accurate information extraction is also useful for the prediction of an etched substrate pattern. In this paper, we reported a strategy to classify the image pixels of line-and-space resist patterns into line, space, and edge classes, using unsupervised machine learning. Brightness and coordination information was integrated into the classification method. The high reliability in classification was demonstrated by hierarchical clustering based on its information integrating ability. Among all the methods of hierarchical clustering examined, the centroid method was the most accurate strategy for extracting information from a single SEM image.

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