Samsung Sds Co., Ltd.

Seong Mi Park

Min Sik Chu

Kyong Hee Joo

Jae Hoon Kim

Jiin Jeong

Baek Young Lee

Ho Geun Park

Joo Yeon Chung

A method performed by a computing device for clustering an image according to an embodiment of the present disclosure includes performing a first clustering on feature vectors of the plurality of images, and performing a second clustering for feature vectors belonging to some clusters that do not satisfy a reference score among clusters formed as a result of the first clustering, wherein a clustering parameter of the second clustering and a clustering parameter of the first clustering are different from each other.

Method and apparatus for clustering images

A method for clustering based on unsupervised learning according to an embodiment of the invention enables clustering for newly generated patterns and is robust against noise, and does not require tagging for training data. According to one or more embodiments, noise is accurately removed using three-dimensional stacked spatial auto-correlation, and multivariate spatial probability distribution values and polar coordinate system spatial probability distribution values are used as learning features for clustering model generation, making them robust to noise, rotation, and fine unusual shapes. In addition, clusters resulting from clustering are classified into multi-level clusters, and stochastic automatic evaluation of normal/defect clusters is possible only with measurement data without a label.

Method and apparatus for detecting defect pattern on wafer based on unsupervised learning

A method for clustering based on unsupervised learning according to an embodiment of the invention enables clustering for newly generated patterns and is robust against noise, and does not require tagging for training data. According to one or more embodiments of the invention, noise is accurately removed using three-dimensional stacked spatial auto-correlation, and multivariate spatial probability distribution values and polar coordinate system spatial probability distribution values are used as learning features for clustering model generation, making them robust to noise, rotation, and fine unusual shapes. In addition, clusters resulting from clustering are classified into multi-level clusters, and stochastic automatic evaluation of normal/defect clusters is possible only with measurement data without a label.

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Seoul, South Korea

Seong Mi Park