IEEE Transactions on Semiconductor Manufacturing最新文献_第9页

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

Hotspot Prediction: SEM Image Generation With Potential Lithography Hotspots 热点预测：利用潜在的光刻热点生成 SEM 图像

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-10-26 DOI: 10.1109/TSM.2023.3327784

Jaehoon Kim;Jaekyung Lim;Jinho Lee;Tae-Yeon Kim;Yunhyoung Nam;Kihyun Kim;Do-Nyun Kim

{"title":"Hotspot Prediction: SEM Image Generation With Potential Lithography Hotspots","authors":"Jaehoon Kim;Jaekyung Lim;Jinho Lee;Tae-Yeon Kim;Yunhyoung Nam;Kihyun Kim;Do-Nyun Kim","doi":"10.1109/TSM.2023.3327784","DOIUrl":"10.1109/TSM.2023.3327784","url":null,"abstract":"Since the invention of transistors and integrated circuits, the development of semiconductor processes has advanced rapidly. Current microchips contain hundreds of millions of transistors. The remarkable development of semiconductors thus far has also led to difficulties in designing tightly packed lithography patterns without unwanted defects called hotspots in the manufacturing process. Therefore, research areas focusing on these problems have received much attention. In particular, predicting hotspots during the design stage is essential for high productivity in the semiconductor industry. In this study, we developed a deep learning-based SEM image generation model to predict hotspots from layout patterns at the design stage. Our model combines a segmentation network and an image-to-image translation network based on a conditional generative adversarial network in parallel. Our proposed model can predict and display potential hotspots in scanning electron microscopy images generated from given layouts. Additionally, the model leverages prior knowledge of the optical diameter to predict patterns that are prone to hotspots. Our model shows improved performance over baseline models when evaluated on real-world industrial data.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"103-114"},"PeriodicalIF":2.7,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135211152","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

Learning Priority Indices for Energy-Aware Scheduling of Jobs on Batch Processing Machines 学习优先级指数，在批量处理机上进行节能作业调度

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-10-24 DOI: 10.1109/TSM.2023.3326865

Daniel Sascha Schorn;Lars Mönch

引用次数: 0

Gas-Delivery Fluid-Mechanical Timescales in Semiconductor Manufacturing 半导体制造中的气体输送流体-机械时间尺度

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-10-24 DOI: 10.1109/TSM.2023.3327067

E. Gonzalez-Juez

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Production-Level Artificial Intelligence Applications in Semiconductor Supply Chains 生产级人工智能在半导体供应链中的应用

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-10-13 DOI: 10.1109/TSM.2023.3322142

Chen-Fu Chien;Hans Ehm;John W. Fowler;Karl G. Kempf;Lars Mönch;Cheng-Hung Wu

引用次数: 0

Multi-Scale and Multi-Branch Transformer Network for Remaining Useful Life Prediction in Ion Mill Etching Process 用于离子磨蚀刻工艺剩余使用寿命预测的多尺度多分支变压器网络

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-10-12 DOI: 10.1109/TSM.2023.3324057

Zengwei Yuan;Rui Wang

{"title":"Multi-Scale and Multi-Branch Transformer Network for Remaining Useful Life Prediction in Ion Mill Etching Process","authors":"Zengwei Yuan;Rui Wang","doi":"10.1109/TSM.2023.3324057","DOIUrl":"10.1109/TSM.2023.3324057","url":null,"abstract":"Accurate prediction of the remaining useful life (RUL) of an ion mill is vital for optimizing the overall performance of the ion mill etching (IME) process. However, due to the uneven distribution of important information, and the poorly understood failure mechanisms, fault prognosis in this process presents significant challenges. Deep neural networks have shown promising results for extracting, without domain knowledge, relevant features from condition monitoring data. This study proposes a multi-scale and multi-branch Transformer network based on the vanilla Transformer to predict the RUL of ion mills. To extract features on various scales, multi-scale feature extraction first generates receptive fields of various sizes, which are then integrated to obtain feature representations. The multi-branch Transformer uses the parallel attention mechanism and long short-term memory (LSTM) to capture both the adjacent location information and the crucial information of a given timestamp. Handcrafted features are also incorporated as additional input to enhance the prediction accuracy of the model. The proposed model is evaluated on a dataset from a semiconductor IME process. The experimental results demonstrate that the proposed model outperforms other deep neural network and further highlight the practical feasibility of the proposed method.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"67-75"},"PeriodicalIF":2.7,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136302061","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

Semantic Context Information Modeling With Neural Networks in Customer Order Behavior Classification 基于神经网络的客户订单行为分类语义上下文信息建模

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-10-06 DOI: 10.1109/TSM.2023.3320870

Philipp Ulrich;Nour Ramzy;Marco Ratusny

{"title":"Semantic Context Information Modeling With Neural Networks in Customer Order Behavior Classification","authors":"Philipp Ulrich;Nour Ramzy;Marco Ratusny","doi":"10.1109/TSM.2023.3320870","DOIUrl":"https://doi.org/10.1109/TSM.2023.3320870","url":null,"abstract":"Demand planning in the semiconductor industry can be complicated due to challenges such as extended cycle times, rapid innovation cycles, and the Bullwhip Effect. Approaches that provide a deeper understanding of customer orders and their associated demand are crucial to enhance demand planning accuracy. Previous studies have employed convolutional neural networks (CNNs) on heat map representations of customer order transactions to effectively classify customer order behaviors (COBs), leading to improved insights into customer behavior. However, these approaches have primarily focused on analyzing customer order patterns without considering contextual information, such as financial or market-related data, which can benefit the classification process. Therefore, we propose a Semantic Context Information Modeling methodology for Neural Networks (SCIM-NN) based on ontologies, knowledge graph embeddings, and multi-stream neural networks to include context information for a classification task. We show the application of SCIM-NN on a use case in the domain of COB and evaluate the performance of the context-aware model on customer data of Infineon Technologies AG. Results indicate that including context information improves the overall classification performance compared to a benchmark CNN.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"36 4","pages":"570-577"},"PeriodicalIF":2.7,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71903142","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

Equipment Condition Monitoring of Multiple Oxide-Nitride Stack Layer Deposition Process 多氧化物氮化物叠层沉积工艺的设备状态监测

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-09-28 DOI: 10.1109/TSM.2023.3319113

Min Ho Kim;Sang Jeen Hong

{"title":"Equipment Condition Monitoring of Multiple Oxide-Nitride Stack Layer Deposition Process","authors":"Min Ho Kim;Sang Jeen Hong","doi":"10.1109/TSM.2023.3319113","DOIUrl":"https://doi.org/10.1109/TSM.2023.3319113","url":null,"abstract":"For the 3D NAND memory, the higher oxide/nitride (ON) stacked dielectric is preferred to enhance the storage capacity, and multi-layer dielectric requirements, such as thickness uniformity and interfacial smoothness between films, gathers more interest for the performance of 3D NAND flash memory. Unsatisfactory thickness uniformity between layers is a challenge not only for the device performance but also the following etch process steps. The thickness uniformity can get worse with a little facility degradation. The degradation of the vacuum system, such as the throttle valve position, has the potential to cause process drift. This can have an impact on the thickness repeatability of each layer in a multiple dielectric stack. To reduce the process variation in multi-layer dielectric deposition for 3D NAND fabrication, process monitoring, and equipment diagnostic study is suggested in this paper. Optical emission spectroscopy (OES) is employed for plasma process monitoring and equipment state variable identification (SVID) data are investigated to find the source of the process variation. From the comparison experiments of 5 and 30 paired oxide/nitride stack deposition, we found equipment and/or facility degradation may induce the minute process drift. Among them, we suggest the potential of process drift due to the throttle valve position.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"36 4","pages":"645-652"},"PeriodicalIF":2.7,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71903162","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