Samsung Electronics Co., Ltd.

Min Chul Park

Dae Sin Kim

Je Hyun Lee

Tae Ho Lee

Chang Wook Jeong

Ji Yong Park

Zhiliang Xia

In Huh

Jeong Hoon Ko

Sat Byul Kim

Chan Young Hwang

Sae Jin Kim

A method for analyzing a wafer map using a wafer map analyzer includes generating first wafer maps each displaying characteristics of a first wafer for a corresponding channel of a plurality of channels. The first wafer maps are auto-encoded together to extract a first feature. The method also includes determining whether the first feature is a valid pattern, classifying the type of the first feature based on unsupervised learning when the first feature is a valid pattern and extracting a representative image of features classified into the same type as the first feature.

Wafer map analyzer, method for analyzing wafer map using the same and method for manufacturing semiconductor device

A method for detecting defects in a semiconductor device includes pre-training a pre-trained convolutional neural network (CNN) model using a sampled clean data set extracted from a first data set; training a normal convolutional neural network model and a label-noise convolutional neural network model using first data of the first data set and the pre-trained convolutional neural network model. The method also includes outputting a first prediction result on whether second data of a second data set is good or bad using the second data and the normal convolutional neural network model; and outputting a second prediction result on whether second data is good or bad using the second data and the label-noise convolutional neural network model. The first prediction result is compared with the second prediction result to perform noise correction when there is a label difference. Third data created as results of the noise correction is added to the sampled clean data set. The normal convolutional neural network model and the label-noise convolutional neural network model are additionally using the sampled clean data set with the third data added.

Method for detecting defects in semiconductor device

Provided are a method and system for analyzing grains using a high-resolution transmission electron microscopy (HRTEM) image. The method relates to analyzing nanometer grains, and includes receiving an HRTEM image, setting local windows each having a predetermined size for the HRTEM image, performing at least one Fast Fourier transformation on pixel data determined by the local windows to calculate local transformation data; and analyzing grains based on the local transformation data.

Grain analyzing method and system using HRTEM image

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Hwaseong-si, South Korea

Min Chul Park