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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 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC 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
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.
这项研究介绍了一种实用的强化学习(RL)技术,用于解决有源矩阵有机发光二极管显示器生产过程中的复杂调度难题。具体来说,我们将重点放在光刻工艺的自主优化上,这是制造过程中的一个关键瓶颈。与现有的基于规则的方法相比,这种方法需要多样化的规则和工程师在适应动态环境方面的经验,因此提供了一种性能更优的调度方法。我们设计的目的性 RL 网络可根据大规模生产的计划和调度模块的分层结构制定有效的调度计划。在训练阶段,我们利用历史生产数据创建了一个具有代表性的离散事件模拟环境。基于深度 Q 网络的 RL 代理通过偶发训练来学习最优调度策略。为确保安全、可靠的调度决策,我们进一步引入了动作过滤器和并行竞争调度器。基于 RL 的调度器(RLS)的性能与基于规则的调度器(RBS)在一年的实际制造中的性能进行了比较。基于关键性能指标,我们验证了 RLS 的性能优于 RBS,在步骤目标匹配、减少设置时间和增强批次分配方面都有显著改善。这项工作还为将基于人工智能的算法逐步融入智能制造实践铺平了道路。
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引用次数: 0
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 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-11-15 DOI: 10.1109/TSM.2023.3332630
Hsiu-Hui Hsiao;Kung-Jeng Wang
This paper addresses the quality control of the photolithography process in the semiconductor industry. Overlay errors in the process seriously affect the wafer yield, and cause the wafer to be forced to rework and affect the production efficiency of the equipment. We examine the current state of its process control, develop a novel overlay predict model, and verify the prediction results. This study proposes a Global Attention Generative Adversarial Networks (GAGAN) model to precisely predict the overlay error for the feed-forward data of the front layer, which is used as the important information and process parameters for the advanced process control of the current layer. Experiment results on a semiconductor shop-floor confirms that our proposed method achieves high predictive performance while maintaining extensibility and visual quality.
本文论述半导体工业中光刻工艺的质量控制。工艺中的叠层误差严重影响晶圆良品率,导致晶圆被迫返工,影响设备的生产效率。我们研究了其工艺控制的现状,开发了一种新型叠加预测模型,并验证了预测结果。本研究提出了一种全局注意力生成对抗网络(GAGAN)模型,用于精确预测前层前馈数据的叠加误差,并将其作为当前层高级过程控制的重要信息和过程参数。在半导体车间的实验结果证实,我们提出的方法在保持可扩展性和视觉质量的同时,实现了较高的预测性能。
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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 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-11-08 DOI: 10.1109/TSM.2023.3329863
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引用次数: 0
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 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-30 DOI: 10.1109/TSM.2023.3325126
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引用次数: 0
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 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-30 DOI: 10.1109/TSM.2023.3324270
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引用次数: 0
IEEE Transactions on Semiconductor Manufacturing Publication Information IEEE半导体制造学报
IF 2.7 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-30 DOI: 10.1109/TSM.2023.3325122
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引用次数: 0
Blank Page 空白页
IF 2.7 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-30 DOI: 10.1109/TSM.2023.3325168
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引用次数: 0
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 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-30 DOI: 10.1109/TSM.2023.3323254
Tsuyoshi Moriya
Since its beginning in 1992 in Japan, International Symposium on Semiconductor Manufacturing (ISSM) has provided unique opportunities to share the best practices of semiconductor manufacturing technologies for professionals. At the symposiums, semiconductor manufacturing professionals discussed the technologies developed to meet the worldwide requirements for advanced manufacturing. It is becoming crucial to re-examine semiconductor manufacturing in terms of fundamental principles to improve the performance of semiconductor devices. Moreover, utilizing artificial intelligence and machine learning technologies to improve semiconductor manufacturing have become a new challenge. These manufacturing technology challenges are showing the need for drastic revolutionary concept and stronger collaborative efforts to find solutions to the precompetitive challenges.
自1992年在日本成立以来,国际半导体制造研讨会(ISSM)为专业人士提供了分享半导体制造技术最佳实践的独特机会。在研讨会上,半导体制造专业人士讨论了为满足全球先进制造要求而开发的技术。从提高半导体器件性能的基本原理角度重新审视半导体制造变得至关重要。此外,利用人工智能和机器学习技术来改善半导体制造已成为一项新的挑战。这些制造技术挑战表明,需要激烈的革命性概念和更强有力的合作努力,以找到应对竞争前挑战的解决方案。
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引用次数: 0
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 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-30 DOI: 10.1109/TSM.2023.3324469
John W. Fowler;Karl Kempf;Lars Mönch
The increasing availability of data, advances in computational and storage capacities of IT systems, and algorithmic advances in Artificial Intelligence (AI), especially Machine Learning (ML) combine to enable significant improvements in the efficiency, operations and throughput of manufacturing systems at the production level. The semiconductor industry is one of the most data-intensive industries and has seen increased use of AI-based technologies over the last few years. In order to develop effective AI-based technologies in the semiconductor manufacturing industry several issues have to be taken into account, including scalability, heterogeneity of data, and the need for interpretability.
数据可用性的提高、IT系统计算和存储能力的进步,以及人工智能(AI),特别是机器学习(ML)的算法进步,使制造系统在生产层面的效率、运营和吞吐量得以显著提高。半导体行业是数据密集度最高的行业之一,在过去几年中,基于人工智能的技术的使用有所增加。为了在半导体制造业中开发有效的基于人工智能的技术,必须考虑几个问题,包括可扩展性、数据的异构性和可解释性的必要性。
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引用次数: 0
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 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2023-10-26 DOI: 10.1109/TSM.2023.3327767
Yang Liu;Lilei Hu;Bin Sun;Can Ma;Jingxuan Shen;Chang Chen
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.
单图像超分辨率(SISR)技术已在半导体缺陷检测领域得到广泛应用。增强图像分辨率对提高检测灵敏度和准确性具有重要意义。本研究提出了一种名为交叉卷积残差网络(CCRN)的新型 SISR 算法。CCRN 包括一个交叉卷积模块(CCM),其中包含一个交叉共享机制,可促进不同阶段特征的融合,从而从图像中提取更多信息。此外,还引入了全局残差聚合结构(GRA)。GRA 可捕捉并将从学习每个 CCM 中获取的不同层次的残差特征传输到重建层。实验结果表明,当应用于微流控芯片、CMOS 图像传感器和量子点的光学、扫描电镜和 TEM 图像时,所提出的 SR 算法在视觉和定量指标方面都优于现有的一流 SR 算法。此外,在使用 WM-811K 数据集进行评估时,CCRN 显著提高了缺陷分类和无图案晶片检测的准确性。值得注意的是,局部缺陷检测准确率从 79.00% 提高到 89.00%,分类准确率从 93.69% 提高到 96.06%。这些发现强调了拟议算法在提高半导体缺陷检测和分类准确性方面的潜在应用。
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引用次数: 0
期刊
IEEE Transactions on Semiconductor Manufacturing
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