저자 | Yusung Kim , Jin-Seop Lee , Jee-Hyong Lee |
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논문지 | IEEE Transactions on Semiconductor Manufacturing |
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No. | |
pp. | |
게재일 | 2023-05-18 |
Automatic defect classification (ADC) systems automatically classify defects that inevitably occur during semiconductor manufacturing processes. ADC is the beginning of defect management that increases the yield of semiconductor chip production, and prevents accidents in the process. It takes a lot of engineer’s labor to classify defects, but ADC can be the answer to classify all defects at low cost. ADC employs the defect image of a wafer surface, captured using scanning electron microscopy (SEM). SEM images can feature a variety of backgrounds based on the defect position on the wafer and the process steps. The manual classification of SEM images involves significant labor costs related to hiring experienced engineers. Despite recent ADC studies reporting good performance, the lack of labeled images and various backgrounds make it difficult to apply ADC in actual manufacturing processes. To address this issue, automated defect classification with defect localization is proposed herein. To this end, a classification model is specifically designed for reducing the effect of varying backgrounds using defect localization. Defect localization uses an object detection model to provide the region information of defects in SEM images. We aimed to design a classification model and defect detection model using semi-supervised learning to reduce labeling costs. Experimental results indicate that the classification performance, over 15 classes, is improved by 12.56% (9.82%p), as compared with that of supervised models.
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2023
Automatic defect classification using semi-supervised learning with defect localization
2023.05.18
저자: Yusung Kim , Jin-Seop Lee , Jee-Hyong Lee
논문지: IEEE Transactions on Semiconductor Manufacturing
게재일: 2023-05-18
READSUM: Retrieval-Augmented Adaptive Transformer for Source Code Summarization
2023.06.27
저자: YunSeok Choi, Cheolwon Na, Hyojun Kim, Jee-Hyong Lee
논문지: IEEE Access
Vol.: 11
pp.: 51155 - 51165
게재일: 2023-05-01
2022
Deep Reinforcement Learning Approach for Material Scheduling Considering High-Dimensional Environment of Hybrid Flow-Shop Problem
2022.12.05
저자: Chang-Bae Gil, Jee-Hyong Lee
논문지: Applied Sciences
Vol.: 12
No.: 9332
게재일: 2022-09-17
Weighted Averaging Federated Learning Based on Example Forgetting Events in Label Imbalanced Non-IID
2022.06.08
저자: 홍만수, 강석규, 이지형
논문지: Applied Sciences
Vol.: 12
No.: 5806
게재일: 2022-06-07
Logit Averaging: Capturing Global Relation for Session-Based Recommendation
2022.04.25
저자: 양희윤, 김가형, 이지형
논문지: Applied Sciences
Vol.: 12
No.: 9
pp.: 4256-4272
게재일: 2022-04-22
2021
Monte Carlo Tree Search-Based Recursive Algorithm for Feature Selection in High-Dimensional Datasets
2021.01.24
저자: Muhammad Umar Chaudhry, Muhammad Yasir ,Muhammad Nabeel Asghar ,Jee-Hyong Lee
논문지: Entropy
Vol.: 22
No.: 10
pp.: 1-15
게재일: 2020-10-01
2019
Adversarial Detection with Gaussian Process Regression-based Detector
2019.09.02
저자: Sangheon Lee, Noo-ri Kim, Youngwha Cho, Jae-Young Choi, Suntae Kim, Jeong-Ah Kim, Jee-Hyong Lee
논문지: KSII Transactions on Internet and Information Systems
Vol.: 13
No.: 8
pp.: 4285-4299
게재일: 2019-08-01
Neural attention model with keyword memory for abstractive document summarization
2019.09.02
저자: YunSeok Choi, Dahae Kim, Jee-Hyong Lee
논문지: Concurrency and Computation: Practice and Experience
게재일: 2019-08-01
A novel recommendation approach based on chronological cohesive units in content consuming logs
2019.01.02
저자: Jaekwang Kim, Jee-Hyong Lee
논문지: Information Science
Vol.: 470
pp.: 141-155
게재일: 2019-01-02
Feature Selection for High Dimensional Data using Monte Carlo Tree Search
2019.09.02
저자: Muhammad Umar Chaudhry, Jee-Hyong Lee
논문지: IEEE Access
Vol.: 6
pp.: 76036-76048
게재일: 2018-11-01
2018
A Television Recommender System Learning a User’s Time-Aware Watching Patterns Using Quadratic Programming
2018.09.03
저자: Noo-ri Kim, Sungtak Oh, Jee-Hyoung Lee
논문지: Applied Sciences
Vol.: 8
No.: 8
pp.: 1323, 1-14
게재일: 2018-08-08
MOTiFS: Monte Carlo Tree Search BasedMOTiFS: Monte Carlo Tree Search Based Feature Selection
2018.09.03
저자: Muhammad Umar Chaudhry, Jee-Hyong Lee
논문지: Entropy
Vol.: 20
No.: 5
pp.: 385
게재일: 2018-05-20
Improved Neighborhood Search for Collaborative Filtering
2018.09.03
저자: Yeounoh Chung, Noo-ri Kim, Chang-yong Park, Jee-Hyong Lee
논문지: International Journal of Fuzzy Logic and Intelligent Systems
게재일: 2018-03-21
Detection of document modification based on deep neural networks
2018.02.06
저자: Noo-ri Kim, YunSeok Choi, HyunSoo Lee, Jae-Young Choi, Suntae Kim, Jeong-Ah Kim, Youngwha Cho, Jee-Hyong Lee
논문지: Journal of Ambient Intelligence and Humanized Computing
게재일: 2017-11-01
Data-Driven Optimization of Incentive-based Demand Response System with Uncertain Responses of Customers
2018.02.06
저자: Jimyung Kang, Jee-Hyong Lee
논문지: Energies
게재일: 2017-10-04
A Machine-Learning Based Approach for Extracting Logical Structure of a Styled Document
2018.02.06
저자: Tae-young Kim, Suntae Kim, Sangchul Choi, Jeong-Ah Kim, Jae-Young Choi, Jong-Won Ko, Jee-Huong Lee, Youngwha Cho
논문지: KSII Transactions on Internet & Information Systems
게재일: 2017-02-28
2017
Detection of Content Changes based on Deep Neural Networks
2017.06.29
저자: Noo-ri KimYunSeok ChoiHyunSoo LeeJee-Hyong Lee
논문지: Lecture Notes in Electrical Engineering
게재일: 2016-11-23
2016
Accurate Lithography Hotspot Detection Using Deep Convolutional Neural Networks
2016.10.04
저자: Moojoon Shin, Jee-Hyong Lee
논문지: Journal of Micro/Nanolithography, MEMS, and MOEMS (Under Review)
게재일: 2016-10-04
Improved Post-Filtering Method Using Context Compensation
2016.08.10
저자: BeDeuRo Kim, Jee-Hyong Lee
논문지: International Journal of Fuzzy Logic and Intelligent Systems
Vol.: 16
No.: 2
pp.: 119-124
게재일: 2016-06-01
Constructing Efficient Regional Hazardous Weather Prediction Models
2016.08.10
저자: Jaedong Lee, Jee-Hyong Lee
논문지: International Journal of Fuzzy Logic and Intelligent Systems
Vol.: 16
No.: 1
pp.: 1-12
게재일: 2016-03-01