{"title":"用于 TFT-LCD 阵列制造中跨工序缺陷分类的新型多模式学习方法","authors":"Yi Liu;Wei-Te Lee;Hsueh-Ping Lu;Hung-Wen Chen","doi":"10.1109/TSM.2024.3448359","DOIUrl":null,"url":null,"abstract":"In the field of thin-film transistor liquid crystal display (TFT-LCD) manufacturing, the challenge of automated defect classification across multi-layered array processes is profound due to the intricate patterns involved. Traditional deep learning approaches, while promising, often fail to achieve high accuracy in cross-process recognition tasks. To address this gap, we propose a multi-modal learning approach that synergistically combines a knowledge engineering technique called Descriptive Embedding Generation (DEG) with a cross-modal contrastive learning strategy. Unlike conventional methods that primarily rely on visual data, our approach incorporates fine-grained descriptive information generated by DEG, enhancing the discriminative power of the learned model. The performance of this innovative training strategy is demonstrated through rigorous experiments, which show a notable accuracy improvement ranging from 0.92% to 7.89% over existing methods. Our approach has been validated by a leading TFT-LCD manufacturer in Taiwan, confirming its practical relevance and setting a new benchmark in cross-process and multi-product defect classification. This study not only advances the state of defect classification in smart manufacturing but also paves the way for future research in complex recognition tasks.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"527-534"},"PeriodicalIF":2.3000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Novel Multi-Modal Learning Approach for Cross-Process Defect Classification in TFT-LCD Array Manufacturing\",\"authors\":\"Yi Liu;Wei-Te Lee;Hsueh-Ping Lu;Hung-Wen Chen\",\"doi\":\"10.1109/TSM.2024.3448359\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the field of thin-film transistor liquid crystal display (TFT-LCD) manufacturing, the challenge of automated defect classification across multi-layered array processes is profound due to the intricate patterns involved. Traditional deep learning approaches, while promising, often fail to achieve high accuracy in cross-process recognition tasks. To address this gap, we propose a multi-modal learning approach that synergistically combines a knowledge engineering technique called Descriptive Embedding Generation (DEG) with a cross-modal contrastive learning strategy. Unlike conventional methods that primarily rely on visual data, our approach incorporates fine-grained descriptive information generated by DEG, enhancing the discriminative power of the learned model. The performance of this innovative training strategy is demonstrated through rigorous experiments, which show a notable accuracy improvement ranging from 0.92% to 7.89% over existing methods. Our approach has been validated by a leading TFT-LCD manufacturer in Taiwan, confirming its practical relevance and setting a new benchmark in cross-process and multi-product defect classification. This study not only advances the state of defect classification in smart manufacturing but also paves the way for future research in complex recognition tasks.\",\"PeriodicalId\":451,\"journal\":{\"name\":\"IEEE Transactions on Semiconductor Manufacturing\",\"volume\":\"37 4\",\"pages\":\"527-534\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Semiconductor Manufacturing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10644141/\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Semiconductor Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10644141/","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A Novel Multi-Modal Learning Approach for Cross-Process Defect Classification in TFT-LCD Array Manufacturing
In the field of thin-film transistor liquid crystal display (TFT-LCD) manufacturing, the challenge of automated defect classification across multi-layered array processes is profound due to the intricate patterns involved. Traditional deep learning approaches, while promising, often fail to achieve high accuracy in cross-process recognition tasks. To address this gap, we propose a multi-modal learning approach that synergistically combines a knowledge engineering technique called Descriptive Embedding Generation (DEG) with a cross-modal contrastive learning strategy. Unlike conventional methods that primarily rely on visual data, our approach incorporates fine-grained descriptive information generated by DEG, enhancing the discriminative power of the learned model. The performance of this innovative training strategy is demonstrated through rigorous experiments, which show a notable accuracy improvement ranging from 0.92% to 7.89% over existing methods. Our approach has been validated by a leading TFT-LCD manufacturer in Taiwan, confirming its practical relevance and setting a new benchmark in cross-process and multi-product defect classification. This study not only advances the state of defect classification in smart manufacturing but also paves the way for future research in complex recognition tasks.
期刊介绍:
The IEEE Transactions on Semiconductor Manufacturing addresses the challenging problems of manufacturing complex microelectronic components, especially very large scale integrated circuits (VLSI). Manufacturing these products requires precision micropatterning, precise control of materials properties, ultraclean work environments, and complex interactions of chemical, physical, electrical and mechanical processes.