IEEE Transactions on Semiconductor Manufacturing最新文献_第3页

Feature Extraction From Diffraction Images Using a Spatial Light Modulator in Scatterometry 在散射测量中使用空间光调制器从衍射图像中提取特征

IF 2.3 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2024-08-23 DOI: 10.1109/TSM.2024.3448458

Jinyang Li;Hung-Fei Kuo

{"title":"Feature Extraction From Diffraction Images Using a Spatial Light Modulator in Scatterometry","authors":"Jinyang Li;Hung-Fei Kuo","doi":"10.1109/TSM.2024.3448458","DOIUrl":"10.1109/TSM.2024.3448458","url":null,"abstract":"The continuous miniaturization of semiconductor devices has increased the demand for advanced process control technologies. This process requires real-time measurement systems to monitor manufacturing parameters to ensure efficiency and high quality. This study introduces a novel optical module that uses a spatial light modulator to extract key-point intensity distributions from diffraction images in scatterometry. The efficacy of this method is demonstrated on a grating target with a pitch of 855 nm using a feature extraction algorithm that identifies key point locations based on calculated diffraction images. A particularly designed off-axis extraction pattern facilitates the acquisition of key-point intensity distributions. Moreover, incorporating a cylindrical lens into the optical setup reduces the image feature dimensionality, thereby decreasing the data storage space and enabling the output in a streamlined vector format conducive to further analysis. Experimental data on the development of this scatterometry-based optical module and the subsequent validation of the key-point extraction method indicate a maximum mean absolute error of 0.0080 and a cosine similarity consistently above 0.9999. This study integrates image analysis and measurement techniques by optics, providing a more efficient pathway for key-point extraction in diffraction images, offering the potential for improving real-time process monitoring in the semiconductor manufacturing industry.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"518-526"},"PeriodicalIF":2.3,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212249","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 Multi-Modal Learning Approach for Cross-Process Defect Classification in TFT-LCD Array Manufacturing 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

IF 2.3 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2024-08-23 DOI: 10.1109/TSM.2024.3448359

Yi Liu;Wei-Te Lee;Hsueh-Ping Lu;Hung-Wen Chen

{"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":"10.1109/TSM.2024.3448359","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":2.3,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212248","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

Optimizing Scanning Acoustic Tomography Image Segmentation With Segment Anything Model for Semiconductor Devices 利用半导体器件分段模型优化扫描声断层扫描图像分割

IF 2.3 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2024-08-23 DOI: 10.1109/TSM.2024.3444850

Thi Thu Ha Vu;Tan Hung Vo;Trong Nhan Nguyen;Jaeyeop Choi;Sudip Mondal;Junghwan Oh

{"title":"Optimizing Scanning Acoustic Tomography Image Segmentation With Segment Anything Model for Semiconductor Devices","authors":"Thi Thu Ha Vu;Tan Hung Vo;Trong Nhan Nguyen;Jaeyeop Choi;Sudip Mondal;Junghwan Oh","doi":"10.1109/TSM.2024.3444850","DOIUrl":"10.1109/TSM.2024.3444850","url":null,"abstract":"In recent decades, Scanning Acoustic Tomography (SAT) has become a vital technique for characterizing semiconductor devices in non-destructive evaluation. Precise and efficient segmentation of SAT images is crucial for detecting defects and assessing material properties in the semiconductor industry. However, current manual methods are often expensive and susceptible to human error. This study enhances the segmentation process of SAT images using the deep learning model SemiSA, which is fine-tuned from the Segment Anything model. In our experiments, SemiSA was trained and evaluated on a large-scale dataset from the Ohlabs TSAM-400 system, encompassing various semiconductor devices such as Flip Chip, Power Semiconductor, 6-inch and 12-inch Wafer, Transistor, and Multilayer Ceramic Capacitor. The results demonstrate that SemiSA significantly improves segmentation tasks across all types of SAT images of semiconductor devices. On average, there was a 17.89% enhancement in Dice Similarity Coefficient scores and a 24.26% improvement in Intersection over Union scores across all tasks. Additionally, this work also proposes an efficient framework tailored specifically for SAT images. The main objective of developing this segmentation tool is to provide researchers and experts with a valuable tool for advancing the semiconductor evaluation and quality control field. The code is available at \u0000<uri>https://github.com/ThuHa96/SemiSA</uri>\u0000.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"591-601"},"PeriodicalIF":2.3,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212253","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

Comparative Study of Nondestructive Mapping of Conformal-Coating Thickness on Microelectronics by Terahertz Time-of-Flight Tomography 利用太赫兹飞行时间断层扫描对微电子共形涂层厚度进行无损测绘的比较研究

IF 2.3 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2024-08-22 DOI: 10.1109/TSM.2024.3447892

Min Zhai;Serena Calvelli;Haolian Shi;Marco Ricci;Stefano Laureti;Prabjit Singh;Haley Fu;Alexandre Locquet;D. S. Citrin

{"title":"Comparative Study of Nondestructive Mapping of Conformal-Coating Thickness on Microelectronics by Terahertz Time-of-Flight Tomography","authors":"Min Zhai;Serena Calvelli;Haolian Shi;Marco Ricci;Stefano Laureti;Prabjit Singh;Haley Fu;Alexandre Locquet;D. S. Citrin","doi":"10.1109/TSM.2024.3447892","DOIUrl":"10.1109/TSM.2024.3447892","url":null,"abstract":"Conformal coatings are used to protect microelectronic circuitry and increasingly optoelectronics and photonics from detrimental effects of the environment, such as moisture, dust, gasses, and mechanical abrasion. The conventional approach to determine the mean time to failure of conformally coated microelectronic components is usually labor-intensive and time-consuming. We recently showed (Shi et al., 2024) that the quasi-optical approach terahertz (THz) time-of-flight tomography (TOFT) could in principle be used to map conformal-coating thickness over a sample of dimensions on the scale of square centimeters. In this study, we employ THz TOFT to characterize several conformal-coating types on microelectronic test samples in a nondestructive and noncontact manner. This study extends previous work on acrylic conformal coatings. THz TOFT is shown to be effective in the thickness characterization of silicone and acrylic conformal coatings, but not nanometric atomic-layer-deposition metal-oxide coating, which is too thin for the technique.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"499-504"},"PeriodicalIF":2.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212250","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

Application of Plasma Information-Based Virtual Metrology (PI-VM) for Etching in C₄F₈/Ar/O₂ Plasma 在 C4F8/Ar/O2 等离子体蚀刻中应用基于等离子体信息的虚拟计量学 (PI-VM)

IF 2.3 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2024-08-21 DOI: 10.1109/TSM.2024.3447074

Gwanjoong Kim;Ji-Won Kwon;Ingyu Lee;Hwiwon Seo;Jong-Bae Park;Jong-Hyeon Shin;Gon-Ho Kim

{"title":"Application of Plasma Information-Based Virtual Metrology (PI-VM) for Etching in C₄F₈/Ar/O₂ Plasma","authors":"Gwanjoong Kim;Ji-Won Kwon;Ingyu Lee;Hwiwon Seo;Jong-Bae Park;Jong-Hyeon Shin;Gon-Ho Kim","doi":"10.1109/TSM.2024.3447074","DOIUrl":"10.1109/TSM.2024.3447074","url":null,"abstract":"This study developed Plasma Information-based Virtual Metrology (PI-VM) to predict etching process results and analyze process phenomena. Using a dual-frequency capacitively coupled plasma (CCP) etcher with C4F8/Ar/O2 plasma, we etched low aspect ratio (AR) trench patterns in amorphous carbon layer (ACL) hard masks and \u0000<inline-formula> <tex-math>$rm SiO_{2}$ </tex-math></inline-formula>\u0000 molds, and developed the PI-VM statistically by integrating plasma information (PI) variables that reflect domain knowledge. The passivation effect of fluorocarbon plasma was analyzed by varying the gas ratios and the effect of ion energy was analyzed by changing the low frequency (LF) power. In the PI-VM results, the density ratios of the passivation precursor \u0000<inline-formula> <tex-math>$rm CF_{2}$ </tex-math></inline-formula>\u0000 to the etchant F and O were selected as key factors for predicting the process. The selection of radical density ratios as features confirmed the dominance of plasma chemistry in low AR etching. Demonstrating high predictive accuracy with minimal data, PI-VM offers significant potential to enhance the development of semiconductor process recipes.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"602-614"},"PeriodicalIF":2.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212170","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

Group-Exclusive Feature Group Lasso and Applications to Automatic Sensor Selection for Virtual Metrology in Semiconductor Manufacturing 组专属特征组套索及其在半导体制造虚拟计量自动传感器选择中的应用

IF 2.3 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2024-08-21 DOI: 10.1109/TSM.2024.3444720

Jeongsub Choi;Youngdoo Son;Jihoon Kang

引用次数: 0

Special Section Call for Papers: Bridging the Data Gap in Photovoltaics with Synthetic Data Generation 特别版块征稿：通过合成数据生成弥补光伏领域的数据差距

IF 2.3 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2024-08-14 DOI: 10.1109/TSM.2024.3442019

引用次数: 0

Direct Emissions Reduction in Plasma Dry Etching Using Alternate Chemistries: Opportunities, Challenges, and Need for Collaboration 使用替代化学剂直接减少等离子干蚀刻中的排放：机遇、挑战与合作需求

IF 2.3 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2024-08-14 DOI: 10.1109/TSM.2024.3444465

Gurpreet S. Lugani;Robert Skaggs;Bryan Morris;Tyler Tolman;Douglas Tervo;Stefan Uhlenbrock;Jon Hacker;Chye Seng Tan;James P. Nehlsen;Robert G. Ridgeway;Lois Wong Broadway;Francis P. Rudy

{"title":"Direct Emissions Reduction in Plasma Dry Etching Using Alternate Chemistries: Opportunities, Challenges, and Need for Collaboration","authors":"Gurpreet S. Lugani;Robert Skaggs;Bryan Morris;Tyler Tolman;Douglas Tervo;Stefan Uhlenbrock;Jon Hacker;Chye Seng Tan;James P. Nehlsen;Robert G. Ridgeway;Lois Wong Broadway;Francis P. Rudy","doi":"10.1109/TSM.2024.3444465","DOIUrl":"10.1109/TSM.2024.3444465","url":null,"abstract":"Plasma Dry-Etch (DE) is one of the key unit-operations in semiconductor manufacturing that use greenhouse gases (GHG) as feed gas (Donnelly and Kornblit, 2013). The exhaust GHG emission reduction or mitigation is one of the main focuses of scope 1 emission reduction at Micron Technology Inc. The reduction and mitigation approaches have been strategized in focus-tiers in order of proximity to the source of emissions. The focus-tiers upstream of exhaust are avoidance, replacement, reduction and downstream of exhaust are recovery/capture/recycle, abatement. This paper focuses on the replacement focus-tier that pertains to replacing high-emission feed gases (HE gas, feedgas that will produce relatively high kgCO2e through exhaust) with relatively low-emission feed gases (LE gas, feedgas that will produce relatively low kgCO2e through exhaust). The paper presents replacement opportunities and challenges through an evaluation study of Carbonyl Floride (COF2) as a replacement gas for NF3 or CF4 as a DE in-situ plasma chamber cleans gas. In conclusion, direct emissions from DE chamber cleans can be lowered by replacing NF3 and CF4 GHGs with COF2 by 90% or more. However, this replacement would require additional safety measures and abatement in operations due to increased toxicity and reactivity of COF2, along with cost roadmap to make its adoption economically feasible. Similar and possibly additional challenges would arise with other replacement options. To overcome challenges in replacement strategy focus-tier, it will require strong industry level collaboration between chemical suppliers, original equipment manufacturers (OEMs), device manufacturers, semiconductor research and collaboration centers and university research groups.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"445-452"},"PeriodicalIF":2.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637264","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212252","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

IEEE Transactions on Semiconductor Manufacturing Information for Authors IEEE Transactions on Semiconductor Manufacturing 为作者提供的信息

IF 2.3 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2024-08-14 DOI: 10.1109/TSM.2024.3434277

引用次数: 0

IEEE Transactions on Semiconductor Manufacturing Publication Information 电气和电子工程师学会半导体制造期刊》出版信息

IF 2.3 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2024-08-14 DOI: 10.1109/TSM.2024.3434275

引用次数: 0