Photomask Technology 2021最新文献

Panel Discussion: Mask readiness for 3nm and beyond: a mask supplier’s perspective 小组讨论:3nm及以上的掩膜准备情况:掩膜供应商的观点

Photomask Technology 2021 Pub Date : 2021-10-01 DOI: 10.1117/12.2618132

B. Kasprowicz, E. Gallagher, Andrew Wall, L. Melvin, Masashi Sunako, T. Heil

引用次数: 0

A general formula for deep learning success in semiconductor manufacturing 半导体制造业中深度学习成功的一般公式

Photomask Technology 2021 Pub Date : 2021-10-01 DOI: 10.1117/12.2603096

A. Fujimura, A. Baranwal

{"title":"A general formula for deep learning success in semiconductor manufacturing","authors":"A. Fujimura, A. Baranwal","doi":"10.1117/12.2603096","DOIUrl":"https://doi.org/10.1117/12.2603096","url":null,"abstract":"This is a management overview of our experience in how to apply deep learning for semiconductor manufacturing projects. \u0000 \u0000Deep learning is a transformative software technique, largely enabled by \"useful waste\" that is now possible with Peta-FLOPS level computing available with GPUs. DL has achieved many \"firsts\" that tens of years of effort by the best computer scientists could not achieve before. But production deployment of DL projects in semiconductor manufacturing including mask manufacturing has been difficult to attain. There has been a number of successes reported at this conference and elsewhere, but a common theme in deep learning papers in our field is the lack of availability of a large amount of data. Deep learning needs lots of data to train with because it is a pattern matching technique. It needs to see enough patterns to recognize all the situations a production mask might contain. In addition, deep learning programmers improve the network by adding data to the training data set to disambiguate where the network is confused. it is essential to be able to add any kind of data at will quickly to improve the success rate of a deep learning network. In semiconductor manufacturing, real data is hard to come by because of confidentiality requirements and also because of the expense of generating masks and wafers. A key ingredient to the general formula for deep learning success in semiconductor manufacturing is to use digital twins to generate data at will. It is time consuming, resource intensive, and expensive to set up. But it is necessary to create a deep learning capability that can be deployed in production. We will conclude with an overview of the other necessary conditions for deep learning success.","PeriodicalId":412383,"journal":{"name":"Photomask Technology 2021","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129085852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Redefining innovation during The Renaissance of Computing 在计算机复兴时期重新定义创新

Photomask Technology 2021 Pub Date : 2021-10-01 DOI: 10.1117/12.2605755

Christine Dunbar

引用次数: 2

EUV attenuated phase shift mask: development and characterization of mask properties EUV衰减相移掩模:掩模特性的发展和表征

Photomask Technology 2021 Pub Date : 2021-09-30 DOI: 10.1117/12.2606239

I. Fukasawa, Y. Ikebe, T. Aizawa, T. Shoki, T. Onoue

引用次数: 1

An SEM-based deep defect classification system for VSB mask writer that works with die-to-die and die-to-database inspection methods using multiple digital twins built with the state-of-the-art neural networks 一种基于sem的VSB掩模编码器深度缺陷分类系统，该系统使用最先进的神经网络构建的多个数字双胞胎，可以使用模对模和模对数据库的检测方法

Photomask Technology 2021 Pub Date : 2021-09-30 DOI: 10.1117/12.2601004

A. Baranwal, Suhas Pillai, T. Nguyen, J. Yashima, Jim Dewitt, N. Nakayamada, A. Fujimura

{"title":"An SEM-based deep defect classification system for VSB mask writer that works with die-to-die and die-to-database inspection methods using multiple digital twins built with the state-of-the-art neural networks","authors":"A. Baranwal, Suhas Pillai, T. Nguyen, J. Yashima, Jim Dewitt, N. Nakayamada, A. Fujimura","doi":"10.1117/12.2601004","DOIUrl":"https://doi.org/10.1117/12.2601004","url":null,"abstract":"The two standard reticle defect inspection methods are die-to-die and die-to-database. The die-to-die inspection method compares images from the two dice on the same reticle to identify any defect. However, the die-to-database inspection method compares images from the reticle with the design data (CAD). The previous year, we built an SEM-based VSB writer classification system for die-to-die inspection that used state-of-the-art deep learning models to identify errors such as shape, position, and dose [1]. Using the deep neural networks and DL-based SEM digital twins [2], we showed better accuracy than the average human expert in classifying SEM-based defects. However, a limitation remained that the DL model wasn’t aware of chrome and glass regions, just from the input SEM. This information is helpful to make better decisions in classifying some typical errors achieving higher accuracy. In the current paper, we improve the accuracy of the existing classifier by enhancing the underlying deep learning model and supplementing it with the recognition of chrome and glass (exposed and unexposed) regions further. We make it possible with yet another DL-based SEM2CAD digital twin to automatically identify exposed/unexposed areas from the SEM and augment manual input by the expert to it. We feed this new information into the SEM classifier that currently takes a reference and error SEM image for more accurate results. In addition, we also built an SEM-based defect classification system for the die-to-database inspection to categorize various types of VSB mask writer defects, which requires defect SEM images and the reference CAD. Using several deep neural network models and digital twins, in this paper, we provide a production-grade system for the VSB writer’s SEM-based defect classification that works for both die-to-die and die-to-database inspection methods.","PeriodicalId":412383,"journal":{"name":"Photomask Technology 2021","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133207096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A customized multifunctional actinic tool for EUV industry 为EUV行业定制的多功能光化工具

Photomask Technology 2021 Pub Date : 2021-09-30 DOI: 10.1117/12.2602005

B. Kim

引用次数: 0

SEM image quality assessment for mask quality control 用于掩模质量控制的SEM图像质量评价

Photomask Technology 2021 Pub Date : 2021-09-27 DOI: 10.1117/12.2601081

Nivea G. Schuch, Alexandre Moly, Charles Valade, Nassim A. Halli, M. Abaidi, Jordan Belissard, F. Robert, T. Figueiro

引用次数: 2