IEEE Transactions on Semiconductor Manufacturing最新文献_第4页

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

IF 2.5 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2026-08-01 Epub Date: 2026-08-07 DOI: 10.1109/TSM.2026.3716514

引用次数: 0

A Hybrid Degradation Prediction Model for the Flexible Shaft Pulling Device in Crystal Growth Furnace 晶体生长炉柔性轴拉装置的混合退化预测模型

IF 2.5 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2026-08-01 Epub Date: 2026-03-07 DOI: 10.1109/TSM.2026.3691078

Lingxia Mu;Yang Wei;Yuhan Jiang;Shihai Wu;Youmin Zhang;Ding Liu;Biyu Lei

引用次数: 0

Unified Machine Learning Framework for Process–Device–Circuit Variability Quantification of Inner Spacer Etch in GAA FETs GAA场效应管内间隔蚀刻过程-器件-电路可变性量化的统一机器学习框架

IF 2.5 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2026-08-01 Epub Date: 2026-06-09 DOI: 10.1109/TSM.2026.3701836

Om Maheshwari;Nihar Ranjan Mohapatra

{"title":"Unified Machine Learning Framework for Process–Device–Circuit Variability Quantification of Inner Spacer Etch in GAA FETs","authors":"Om Maheshwari;Nihar Ranjan Mohapatra","doi":"10.1109/TSM.2026.3701836","DOIUrl":"https://doi.org/10.1109/TSM.2026.3701836","url":null,"abstract":"The inner spacer etch (ISE) process is a critical step in gate-all-around (GAA) nanosheet FET (NsFET) fabrication that directly defines gate length, spacer thickness, and gate-to-source/drain overlap. At advanced nodes, small ISE variations can induce measurable electrical and circuit-level performance shifts, yet their impact beyond geometric deviations remains insufficiently quantified. This work presents a machine-learning driven framework that unifies process, device, and circuit analysis, enabling rapid and quantitative mapping of ISE induced variations to electrical and circuit-level performance for systematic variability quantification. The study identifies leakage current and parasitic capacitance as the most sensitive electrical parameters to ISE fluctuations. Circuit-level analysis further reveals up to 500 MHz deviation in ring oscillator frequency. By enabling fast and quantitative correlation across process, device, and circuit domains, this framework provides a scalable foundation for variability aware near-real-time design technology co-optimization (DTCO) in advanced technologies.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"39 3","pages":"410-416"},"PeriodicalIF":2.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148694912","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

A Novel Health Index Framework for Semiconductor Equipment Using Multi-View Data 基于多视图数据的半导体设备健康指数框架

IF 2.5 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2026-03-01 Epub Date: 2026-02-23 DOI: 10.1109/TSM.2026.3665568

Jeongsun Ahn;Sang-Hyun Cho;Hong-Yeon Kim;Hongyeon Kim;Hyeonjeong Choi;Dain Ham;Yongjo Kim;Hyun-Jung Kim

{"title":"A Novel Health Index Framework for Semiconductor Equipment Using Multi-View Data","authors":"Jeongsun Ahn;Sang-Hyun Cho;Hong-Yeon Kim;Hongyeon Kim;Hyeonjeong Choi;Dain Ham;Yongjo Kim;Hyun-Jung Kim","doi":"10.1109/TSM.2026.3665568","DOIUrl":"https://doi.org/10.1109/TSM.2026.3665568","url":null,"abstract":"In semiconductor manufacturing, monitoring equipment status is vital in ensuring process stability and efficient equipment operation. Thus, there is an increasing need for a health index that can efficiently reflect the overall condition of the equipment as a unified metric. Previous studies have derived a health index based on real-time time-series data collected from single wafer processing, overlooking other significant perspectives essential for practical equipment monitoring. Parameters such as temperature and pressure with similar trends in time-series data of multiple consecutive wafers indicate that they are sensitive to the cumulative use of equipment and are similarly affected by equipment aging. Hence, it is necessary to consider the similarity of trends for efficient diagnosis and control. The type of physical components such as chambers, pumps, and pipes observed by each parameter is also an important criterion for accurately understanding the condition of the equipment and taking necessary actions. These can be considered as monitoring views. Therefore, this study introduces a new health index framework for monitoring semiconductor equipment, utilizing multi-view data. We propose three key views for monitoring and define multi-view data by suggesting a quantitative method to represent it in graphical form. Based on it, we propose a method for deriving the health index using an autoencoder-based approach and dynamic weighting techniques. We have validated the effectiveness of our approach with real datasets, demonstrating its potential as a valuable tool for monitoring the condition of semiconductor equipment.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"39 2","pages":"330-343"},"PeriodicalIF":2.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507810","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

A Smart Stopper-Less CMP Technology With Optical Monitors for Nano-Scale Control of the Amorphous Si Film Thickness on 3D-Structured FinFETs and GAAFETs 一种智能无挡板CMP技术与光学监视器，用于三维结构finfet和gaafet上非晶硅膜厚度的纳米级控制

IF 2.5 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2026-03-01 Epub Date: 2026-03-30 DOI: 10.1109/TSM.2026.3678941

Yuji Kasashima;Takashi Matsukawa;Kazuya Uejima;Yoshihiro Hayashi

引用次数: 0

AI-Driven Optimization of Thin Film and CMP Processes in 3-D NAND Manufacturing 3d NAND制造中薄膜和CMP工艺的ai驱动优化

IF 2.5 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1109/TSM.2026.3659914

Hsiang-Meng Yu;Han-Yu Hsiao;Meng-Hsun Hsieh;Alex Yang;Yu-Chih Chang;Tuung Luoh;Jung-Yu Hsieh;Ling-Wuu Yang;Tahone Yang;Kuang-Chao Chen

{"title":"AI-Driven Optimization of Thin Film and CMP Processes in 3-D NAND Manufacturing","authors":"Hsiang-Meng Yu;Han-Yu Hsiao;Meng-Hsun Hsieh;Alex Yang;Yu-Chih Chang;Tuung Luoh;Jung-Yu Hsieh;Ling-Wuu Yang;Tahone Yang;Kuang-Chao Chen","doi":"10.1109/TSM.2026.3659914","DOIUrl":"https://doi.org/10.1109/TSM.2026.3659914","url":null,"abstract":"Artificial intelligence (AI) and machine learning (ML) demonstrate transformative potential in optimizing complex semiconductor manufacturing. This study addresses two critical areas: process control for 3D NAND fabrication and predictive maintenance (PdM) of fab equipment. For optimizing Thin Film deposition and Chemical Mechanical Planarization (CMP) processes, Artificial Neural Networks (ANNs) capture complex, non-linear dynamics, achieving prediction accuracies up to ~91.4% for controlling surface topography and roll-off. This significantly outperforms Lasso regression (~63.8%), Transformer models (~86.5%), and other customized ML models. In PdM applications, gradient boosting frameworks like eXtreme Gradient Boosting (XGBoost) deliver superior classification F1 score (exceeding 0.995) for chamber failure prediction on structured tabular data, outperforming Random Forest (0.991) and Convolutional Neural Networks (CNNs) (0.984). The enhanced performance of XGBoost stems from its sequential error-correcting tree building and effective handling of feature interactions. This work underscores the critical principle of selecting AI models aligned with data characteristics: ANNs for complex process optimization with rich feature dynamics, and tree-based ensembles like XGBoost for tabular PdM data. This tailored approach enables enhanced yield, reduced downtime, and higher operational efficiency, advocating for hybrid AI modeling in smart semiconductor Fabs.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"39 2","pages":"214-221"},"PeriodicalIF":2.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148504446","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

Quantifying the Global Impact of Constraint Programming-Based Local Scheduling in Semiconductor Manufacturing 半导体制造中基于约束规划的局部调度的全局影响量化

IF 2.5 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2026-03-01 Epub Date: 2026-03-26 DOI: 10.1109/TSM.2026.3678175

Patrick Stöckermann;Niels Hayen;Pierre Tassel;Alessandro Immordino;Thomas Altenmüller;Martin Gebser;Konstantin Schekotihin;Georg Seidel

{"title":"Quantifying the Global Impact of Constraint Programming-Based Local Scheduling in Semiconductor Manufacturing","authors":"Patrick Stöckermann;Niels Hayen;Pierre Tassel;Alessandro Immordino;Thomas Altenmüller;Martin Gebser;Konstantin Schekotihin;Georg Seidel","doi":"10.1109/TSM.2026.3678175","DOIUrl":"https://doi.org/10.1109/TSM.2026.3678175","url":null,"abstract":"The efficiency of semiconductor frontend manufacturing highly depends on the optimization of resource allocation. In academic works, scheduling methods, i.e., based on Constraint Programming (CP) or Mixed Integer Programming (MIP), are popular tools for solving this optimization problem. As the problem is NP-hard, complete optimization methods do not scale to the problem size and complexity required for realistic fab-wide scheduling. Therefore, the problem is often decomposed and solved locally. While we can track the solution gap or even compute exact solutions for sufficiently small local problem instances, the fab-wide impact of decomposition strategies is yet to be determined in a realistic setting. In this work, we empirically quantify the impact of locally applied optimization on the fab-wide throughput and tardiness. For this evaluation, we use a local CP-based optimization method to compute large-scale schedules in the dynamic and stochastic environment of a semiconductor frontend manufacturing facility. We investigate the local and global impact of our method in different settings using a high-fidelity simulation environment. The results show that the CP-based scheduling method is able to significantly improve locally over advanced expert-drafted heuristics. On the global scale, we show that local optimization delivers inconsistent and varying results, stressing the need for an injection of global information into the local problem formulation.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"39 2","pages":"299-309"},"PeriodicalIF":2.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11456594","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computational Modeling and Optimization of Large-Area Showerhead Configurations in PECVD Reactors PECVD反应器中大面积淋浴喷头配置的计算建模与优化

IF 2.5 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2026-03-01 Epub Date: 2026-04-03 DOI: 10.1109/TSM.2026.3680778

Emad Barakat;Yeonchan Kim;Joo Pyo Hong;Seungkyung Park

{"title":"Computational Modeling and Optimization of Large-Area Showerhead Configurations in PECVD Reactors","authors":"Emad Barakat;Yeonchan Kim;Joo Pyo Hong;Seungkyung Park","doi":"10.1109/TSM.2026.3680778","DOIUrl":"https://doi.org/10.1109/TSM.2026.3680778","url":null,"abstract":"This Plasma enhanced chemical vapor deposition (PECVD), a key process in semiconductor manufacturing, typically requires optimization of device geometry and operating conditions to achieve uniform deposition across large substrates, ensuring high-quality production. Computational fluid dynamics (CFD) serves as a critical tool for simulating and designing flow fields; however, the complexity of device geometries, particularly in large-scale PECVD reactors with thousands to hundreds of thousands of nozzles, often poses significant challenges to numerical modeling due to substantial computational cost. In this study, we propose a novel series porous resistance model (SPRM) that significantly reduces computational cost by representing the complex nozzle geometry as simplified porous media. The SPRM provides an analogous mathematical representation for each individual nozzle without requiring the actual geometric modeling, thereby enabling efficient simulation and optimization of nozzle arrays without recreating a mesh model for each design case. The efficiency of the proposed SPRM is demonstrated through 28 distinct showerhead configurations, with flow fields and deposition characteristics evaluated and compared. The results show that the SPRM achieves up to a 90% reduction in computational load compared to conventional CFD approaches and eliminates the need for remeshing during optimization. Thus, the proposed method offers a computationally efficient pathway for the design and performance optimization of industrial-scale PECVD reactors, with potential applications in semiconductor manufacturing and other thin-film deposition processes.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"39 2","pages":"350-357"},"PeriodicalIF":2.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509395","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

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

IF 2.3 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2026-03-01 Epub Date: 2026-03-15 DOI: 10.1109/TSM.2026.3673812

引用次数: 0

Cross Domain Anomaly Detection in Semiconductor Manufacturing via Projection Comparison and Reconstruction in Latent Space 基于投影比较和隐空间重构的半导体制造跨域异常检测

IF 2.3 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2026-03-01 Epub Date: 2026-02-17 DOI: 10.1109/TSM.2026.3665531

Xinwei Lv;Haiyong Chen;Zhaoyang Wang

引用次数: 0