IEEE Transactions on Semiconductor Manufacturing最新文献_第8页

Machine Learning on Multiplexed Optical Metrology Pattern Shift Response Targets to Predict Electrical Properties 利用多路复用光学计量模式偏移响应目标的机器学习来预测电气特性

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-12-05 DOI: 10.1109/TSM.2023.3339330

Thomas J. Ashby;Vincent Truffert;Dorin Cerbu;Kit Ausschnitt;Anne-Laure Charley;Wilfried Verachtert;Roel Wuyts

{"title":"Machine Learning on Multiplexed Optical Metrology Pattern Shift Response Targets to Predict Electrical Properties","authors":"Thomas J. Ashby;Vincent Truffert;Dorin Cerbu;Kit Ausschnitt;Anne-Laure Charley;Wilfried Verachtert;Roel Wuyts","doi":"10.1109/TSM.2023.3339330","DOIUrl":"https://doi.org/10.1109/TSM.2023.3339330","url":null,"abstract":"Doing high throughput high accuracy metrology in small geometries is challenging. One approach is to build easily measurable proxy targets onto dies and make a predictive model based on those signals. We use optical Pattern Shift Response (PSR) proxy targets to build predictive models of the electrical characteristics of devices in the Back End Of Line (BEOL). Given the wide choice of PSR targets, we explore how to select combinations of them to maximise the utility of the features for building an accurate Machine Learning (ML) model; we call this approach Multiplexed Optical Metrology. We also explore the trade-off between chip area dedicated to targets and achievable accuracy. We run ML experiments using different selections of targets measured at different stages of BEOL processing: post-lithography and post-Chemical-Mechanical-Planarisation (CMP). Our results show that a) reasonable predictive performance can be achieved for a reasonable area budget; b) ML model performance across target families varies significantly, thus justifying the need for careful selection of targets; c) longitudinal measurements of targets increases accuracy for no extra area penalty; d) increasing the number of targets gives some improvement in accuracy for a dataset of this size, but relatively small compared to the increase in area budget needed. Ultimately we aim to do die-level yield prediction using these techniques. We discuss how collecting a larger dataset with appropriate yield information is the logical next step to achieving this.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"46-58"},"PeriodicalIF":2.7,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10342754","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139695064","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

Coherent Fourier Scatterometry for Detection of Killer Defects on Silicon Carbide Samples 相干傅立叶散射测量法检测碳化硅样品上的杀手缺陷

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-11-29 DOI: 10.1109/TSM.2023.3337720

Jila Rafighdoost;Dmytro Kolenov;Silvania F. Pereira

{"title":"Coherent Fourier Scatterometry for Detection of Killer Defects on Silicon Carbide Samples","authors":"Jila Rafighdoost;Dmytro Kolenov;Silvania F. Pereira","doi":"10.1109/TSM.2023.3337720","DOIUrl":"10.1109/TSM.2023.3337720","url":null,"abstract":"It has been a widely growing interest in using silicon carbide (SiC) in high-power electronic devices. Yet, SiC wafers may contain killer defects that could reduce fabrication yield and make the device fall into unexpected failures. To prevent these failures from happening, it is very important to develop inspection tools that can detect, characterize and locate these defects in a non-invasive way. Current inspection techniques such as Dark Field or Bright field microscopy are effectively able to visualize most such defects; however, there are some scenarios where the inspection becomes problematic or almost impossible, such as when the defects are too small or have low contrast or if the defects lie deep into the substrate. Thus, an alternative method is needed to face these challenges. In this paper, we demonstrate the application of coherent Fourier scatterometry (CFS) as a complementary tool in addition to the conventional techniques to overcome different and problematic scenarios of killer defects inspection on SiC samples. Scanning electron microscopy (SEM) has been used to assess the same defects to validate the findings of CFS. Great consistency has been demonstrated in the comparison between the results obtained with CFS and SEM.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 1","pages":"124-128"},"PeriodicalIF":2.7,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10335636","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139256281","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

Single-Mask Fabrication of Sharp SiOx Nanocones 单掩模制造尖锐氧化硅纳米锥体

IF 2.7 3区工程技术

IEEE Transactions on Semiconductor Manufacturing Pub Date : 2023-11-28 DOI: 10.1109/TSM.2023.3336169

Eric Herrmann;Xi Wang

引用次数: 0

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}

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

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

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

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

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

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

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

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

引用次数: 0

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

引用次数: 0