IEEE Transactions on Semiconductor Manufacturing最新文献_第10页

Practical Reinforcement Learning for Adaptive Photolithography Scheduler in Mass Production 大规模生产中用于自适应光刻调度的实用强化学习

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-11-28 DOI: 10.1109/TSM.2023.3336909

Eungjin Kim;Taehyung Kim;Dongcheol Lee;Hyeongook Kim;Sehwan Kim;Jaewon Kim;Woosub Kim;Eunzi Kim;Younggil Jin;Tae-Eog Lee

{"title":"Practical Reinforcement Learning for Adaptive Photolithography Scheduler in Mass Production","authors":"Eungjin Kim;Taehyung Kim;Dongcheol Lee;Hyeongook Kim;Sehwan Kim;Jaewon Kim;Woosub Kim;Eunzi Kim;Younggil Jin;Tae-Eog Lee","doi":"10.1109/TSM.2023.3336909","DOIUrl":"https://doi.org/10.1109/TSM.2023.3336909","url":null,"abstract":"This work introduces a practical reinforcement learning (RL) techniques to address the complex scheduling challenges in producing Active Matrix Organic Light Emitting Diode displays. Specifically, we focus on autonomous optimization of the photolithography process, a critical bottleneck in the fabrication. This provides an outperforming scheduling method compared with the existing rule-based approach which requires diverse rules and engineer experience on adapting dynamic environments. Our purposing RL network was designed to make effective schedules aligning with layered structures of the planning and scheduling modules for mass production. In the training phase, historical production data is utilized to create a representative discrete event simulation environment. The RL agent, based on the Deep Q-Network, undergoes episodic training to learn optimal scheduling policies. To ensure safe and reliable scheduling decisions, we further introduce action filters and parallel competing schedulers. The performance of RL-based Scheduler (RLS) is compared to the Rule-Based Scheduler (RBS) over actual fabrication in a year-long period. Based on key performance indicators, we validate the RLS outperforms the RBS, with a remarkable improvement in step target matching, reduced setup times, and enhanced lot assignments. This work also paves a way for the gradual integration of AI-based algorithms into smart manufacturing practices.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"16-26"},"PeriodicalIF":2.7,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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GAGAN: Global Attention Generative Adversarial Networks for Semiconductor Advanced Process Control GAGAN：用于半导体先进过程控制的全局注意力生成对抗网络

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-11-15 DOI: 10.1109/TSM.2023.3332630

Hsiu-Hui Hsiao;Kung-Jeng Wang

引用次数: 0

2023 Index IEEE Transactions on Semiconductor Manufacturing Vol. 36 半导体制造学报，第36卷

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-11-08 DOI: 10.1109/TSM.2023.3329863

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IEEE Transactions on Semiconductor Manufacturing Information for Authors IEEE半导体制造信息汇刊

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-10-30 DOI: 10.1109/TSM.2023.3325126

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Special Issue on Semiconductor Design for Manufacturing (DFM) 半导体制造设计特刊

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-10-30 DOI: 10.1109/TSM.2023.3324270

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IEEE Transactions on Semiconductor Manufacturing Publication Information IEEE半导体制造学报

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-10-30 DOI: 10.1109/TSM.2023.3325122

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Blank Page 空白页

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-10-30 DOI: 10.1109/TSM.2023.3325168

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Guest Editorial Special section on the 2022 International Symposium on Semiconductor Manufacturing 2022年半导体制造国际研讨会特邀编辑特辑

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-10-30 DOI: 10.1109/TSM.2023.3323254

Tsuyoshi Moriya

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Guest Editorial Special Section on Production-Level Artificial Intelligence Applications in Semiconductor Manufacturing 客座编辑关于半导体制造中生产级人工智能应用的特别部分

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-10-30 DOI: 10.1109/TSM.2023.3324469

John W. Fowler;Karl Kempf;Lars Mönch

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A Novel Multiscale Residual Aggregation Network-Based Image Super-Resolution Algorithm for Semiconductor Defect Inspection 用于半导体缺陷检测的基于多尺度残留聚合网络的新型图像超分辨率算法

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-10-26 DOI: 10.1109/TSM.2023.3327767

Yang Liu;Lilei Hu;Bin Sun;Can Ma;Jingxuan Shen;Chang Chen

{"title":"A Novel Multiscale Residual Aggregation Network-Based Image Super-Resolution Algorithm for Semiconductor Defect Inspection","authors":"Yang Liu;Lilei Hu;Bin Sun;Can Ma;Jingxuan Shen;Chang Chen","doi":"10.1109/TSM.2023.3327767","DOIUrl":"10.1109/TSM.2023.3327767","url":null,"abstract":"Single-image super-resolution (SISR) techniques have found wide applications in semiconductor defect inspection. Enhancing image resolution to improve inspection sensitivity and accuracy holds great significance. A novel SISR algorithm, called cross-convolutional residual network (CCRN), is proposed in this study. CCRN comprises a cross-convolutional module (CCM), which incorporates a cross-sharing mechanism that facilitates the fusion of features from different stages, enabling the extraction of more information from the image. Moreover, a global residual aggregation structure (GRA) is introduced. GRA captures and transfers different levels of residual features acquired from learning each CCM to the reconstruction layer. Experimental results demonstrate that the proposed SR algorithm outperforms existing state-of-the-art SR algorithms in terms of both visual and quantitative metrics when applied to optical, SEM, and TEM images of microfluidic chips, CMOS image sensors, and quantum dots, respectively. Additionally, CCRN significantly improves the accuracy of defect classification and inspection of unpatterned wafers, as evaluated using the WM-811K dataset. Notably, an increase in local defection testing accuracy from 79.00% to 89.00% and an improvement in classification accuracy from 93.69% to 96.06% are achieved. These findings underscore the potential applications of the proposed algorithm in improving semiconductor defect inspection and classification accuracies.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"93-102"},"PeriodicalIF":2.7,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134884005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0