Publications
Detailed Information
Improvement of Labeling Performance using K-Means Clustering in TFT-LCD Defect Classification Process : TFT-LCD결함 분류 공정에서의 K-Means Clustering을 이용한 라벨링 자동화 성능 향상에 대한 연구
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 박희재 | - |
dc.contributor.author | 김성욱 | - |
dc.date.accessioned | 2018-05-29T03:15:09Z | - |
dc.date.available | 2018-05-29T03:15:09Z | - |
dc.date.issued | 2018-02 | - |
dc.identifier.other | 000000150378 | - |
dc.identifier.uri | https://hdl.handle.net/10371/141387 | - |
dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2018. 2. 박희재. | - |
dc.description.abstract | The paper focuses on the improvement of defect classification for
a TFT-LCD by enhancing labeling performance. Defects occur in the manufacturing process of TFT-LCD, which have to be repaired or disposed depending on the size and type, thereby lowering the productivity. Defects are detected through optical systems and image processing algorithms. Machine learning algorithms are used to classify the detected defects, and information gathered from the process provides feedback. The process requires an initial input of labeled defects that is crucial to the later learning process. Any faulty inputs given at the initial stage are consequential to impede a proper learning process. I propose a method for effectively labeling defects using a k means clustering algorithm to solve this problem. Previous research only used features that can be visually confirmed. I argue that adding the values obtained by passing kernel over the defect data in addition to visually confirmed features. Using this feature, we could better classify defects that were not previously classified. Experimental defect data occur during the TFT-LCD process. | - |
dc.description.tableofcontents | Chapter 1. Introduction 1
Chapter 2. Defects and features 4 Chapter 3. Distinction methods 9 Chapter 4. Experiment & Evaluation 14 Chapter 5. Conclusion 20 Bibliography 21 Abstract in Korean 23 | - |
dc.format | application/pdf | - |
dc.format.extent | 838188 bytes | - |
dc.format.medium | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | 서울대학교 대학원 | - |
dc.subject | TFT-LCD | - |
dc.subject | Defect | - |
dc.subject | Classification | - |
dc.subject | Labeling | - |
dc.subject | K-Means Clustering | - |
dc.subject | Features | - |
dc.subject | Kernel | - |
dc.subject.ddc | 621 | - |
dc.title | Improvement of Labeling Performance using K-Means Clustering in TFT-LCD Defect Classification Process | - |
dc.title.alternative | TFT-LCD결함 분류 공정에서의 K-Means Clustering을 이용한 라벨링 자동화 성능 향상에 대한 연구 | - |
dc.type | Thesis | - |
dc.description.degree | Master | - |
dc.contributor.affiliation | 공과대학 기계항공공학부 | - |
dc.date.awarded | 2018-02 | - |
- Appears in Collections:
- Files in This Item:
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.