知识蒸馏跨领域扩散模型:缺陷模式分割的生成式人工智能方法

IF 2.3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Yuanfu Yang;Min Sun
{"title":"知识蒸馏跨领域扩散模型:缺陷模式分割的生成式人工智能方法","authors":"Yuanfu Yang;Min Sun","doi":"10.1109/TSM.2024.3472611","DOIUrl":null,"url":null,"abstract":"In semiconductor manufacturing, defect detection is pivotal for enhancing productivity and yield. This paper introduces a novel weakly supervised method, the Implicit Cross Domain Diffusion Model (ICDDM), designed to tackle defect pattern segmentation challenges in the absence of detailed pixel-wise annotations. ICDDM employs a generative model to estimate the joint distribution of images depicting defect patterns and background circuits, formulating this estimation as a Markov Chain and optimizing it through denoising score matching. Building on this, we propose the Cross Domain Latent Diffusion Model (CDLDM), inspired by the Latent Diffusion Model, which simplifies the diffusion process into a lower-dimensional latent space to boost detection efficiency. Further enhancing our model, we introduce the Knowledge Distillation Cross Domain Diffusion Model (KDCDDM), which utilizes CDLDM as a teacher model and a Generative Adversarial Network (GAN) as a student model. This approach significantly accelerates the diffusion process by reducing the number of necessary denoising iterations while maintaining robust model performance. This suite of techniques offers a comprehensive solution for efficient and effective defect detection in semiconductor production environments.","PeriodicalId":451,"journal":{"name":"IEEE Transactions on Semiconductor Manufacturing","volume":"37 4","pages":"634-642"},"PeriodicalIF":2.3000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Knowledge Distillation Cross Domain Diffusion Model: A Generative AI Approach for Defect Pattern Segmentation\",\"authors\":\"Yuanfu Yang;Min Sun\",\"doi\":\"10.1109/TSM.2024.3472611\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In semiconductor manufacturing, defect detection is pivotal for enhancing productivity and yield. This paper introduces a novel weakly supervised method, the Implicit Cross Domain Diffusion Model (ICDDM), designed to tackle defect pattern segmentation challenges in the absence of detailed pixel-wise annotations. ICDDM employs a generative model to estimate the joint distribution of images depicting defect patterns and background circuits, formulating this estimation as a Markov Chain and optimizing it through denoising score matching. Building on this, we propose the Cross Domain Latent Diffusion Model (CDLDM), inspired by the Latent Diffusion Model, which simplifies the diffusion process into a lower-dimensional latent space to boost detection efficiency. Further enhancing our model, we introduce the Knowledge Distillation Cross Domain Diffusion Model (KDCDDM), which utilizes CDLDM as a teacher model and a Generative Adversarial Network (GAN) as a student model. This approach significantly accelerates the diffusion process by reducing the number of necessary denoising iterations while maintaining robust model performance. This suite of techniques offers a comprehensive solution for efficient and effective defect detection in semiconductor production environments.\",\"PeriodicalId\":451,\"journal\":{\"name\":\"IEEE Transactions on Semiconductor Manufacturing\",\"volume\":\"37 4\",\"pages\":\"634-642\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Semiconductor Manufacturing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10702557/\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Semiconductor Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10702557/","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

在半导体制造领域,缺陷检测对于提高生产率和成品率至关重要。本文介绍了一种新颖的弱监督方法--隐式跨域扩散模型(ICDDM),旨在解决缺乏详细像素注释的缺陷模式分割难题。ICDDM 采用生成模型来估计描述缺陷模式和背景电路的图像的联合分布,将这种估计形成马尔可夫链,并通过去噪分数匹配对其进行优化。在此基础上,我们受潜在扩散模型(Latent Diffusion Model)的启发,提出了跨域潜在扩散模型(Cross Domain Latent Diffusion Model,CDLDM),将扩散过程简化为低维潜在空间,以提高检测效率。为了进一步增强我们的模型,我们引入了知识蒸馏跨域扩散模型(KDCDDM),它利用 CDLDM 作为教师模型,利用生成对抗网络(GAN)作为学生模型。这种方法通过减少必要的去噪迭代次数,大大加快了扩散过程,同时保持了模型的稳健性能。这套技术为半导体生产环境中的高效缺陷检测提供了全面的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Knowledge Distillation Cross Domain Diffusion Model: A Generative AI Approach for Defect Pattern Segmentation
In semiconductor manufacturing, defect detection is pivotal for enhancing productivity and yield. This paper introduces a novel weakly supervised method, the Implicit Cross Domain Diffusion Model (ICDDM), designed to tackle defect pattern segmentation challenges in the absence of detailed pixel-wise annotations. ICDDM employs a generative model to estimate the joint distribution of images depicting defect patterns and background circuits, formulating this estimation as a Markov Chain and optimizing it through denoising score matching. Building on this, we propose the Cross Domain Latent Diffusion Model (CDLDM), inspired by the Latent Diffusion Model, which simplifies the diffusion process into a lower-dimensional latent space to boost detection efficiency. Further enhancing our model, we introduce the Knowledge Distillation Cross Domain Diffusion Model (KDCDDM), which utilizes CDLDM as a teacher model and a Generative Adversarial Network (GAN) as a student model. This approach significantly accelerates the diffusion process by reducing the number of necessary denoising iterations while maintaining robust model performance. This suite of techniques offers a comprehensive solution for efficient and effective defect detection in semiconductor production environments.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IEEE Transactions on Semiconductor Manufacturing
IEEE Transactions on Semiconductor Manufacturing 工程技术-工程:电子与电气
CiteScore
5.20
自引率
11.10%
发文量
101
审稿时长
3.3 months
期刊介绍: The IEEE Transactions on Semiconductor Manufacturing addresses the challenging problems of manufacturing complex microelectronic components, especially very large scale integrated circuits (VLSI). Manufacturing these products requires precision micropatterning, precise control of materials properties, ultraclean work environments, and complex interactions of chemical, physical, electrical and mechanical processes.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信