國立虎尾科技大學 |

Accurate Hotspots Detection with Deep Convolutional Neural Network and Generative Adversarial Network Based Data Augmentation.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Accurate Hotspots Detection with Deep Convolutional Neural Network and Generative Adversarial Network Based Data Augmentation./
作者:	Cheng, Zeyuan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	59 p.
附註:	Source: Masters Abstracts International, Volume: 83-12.
Contained By:	Masters Abstracts International83-12.
標題:	Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28967543
ISBN:	9798834061397

Accurate Hotspots Detection with Deep Convolutional Neural Network and Generative Adversarial Network Based Data Augmentation.
Cheng, Zeyuan.

Accurate Hotspots Detection with Deep Convolutional Neural Network and Generative Adversarial Network Based Data Augmentation. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 59 p.

Source: Masters Abstracts International, Volume: 83-12.

Thesis (M.A.S.)--University of Toronto (Canada), 2022.

This item must not be sold to any third party vendors.

The increasing demand for computational power is pushing the photolithography technology to its physical limit. As one of its crucial technical bottlenecks, the design issue of layout hotspots requires innovative solutions to minimize the necessity of conducting expensive lithography simulation experiments but remain extremely accurate. In addition to the sustained development of conventional hotspots detection, the recent implementation of convolutional neural networks (CNN) has shown high effectiveness in flexible pattern recognitions. However, limited by the industrial data availability, technical issues such as imbalanced learning and rare/new hotspot detection need to be navigated. In this thesis, we introduced a deep learning hotspots detection method with an improved CNN classifier and enhanced the learning effectiveness by augmenting the dataset with the generative adversarial network (GAN) based synthetic pattern generation. Experiment results show competitive performance compared to the existing works, but further development is required to connect with the current industrial processes.

ISBN: 9798834061397Subjects--Topical Terms:

561152
Engineering.
Subjects--Index Terms:

Deep learning

Accurate Hotspots Detection with Deep Convolutional Neural Network and Generative Adversarial Network Based Data Augmentation.
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300 $a 59 p.
500 $a Source: Masters Abstracts International, Volume: 83-12.
500 $a Advisor: Behdinan, Kamran.
502 $a Thesis (M.A.S.)--University of Toronto (Canada), 2022.
506 $a This item must not be sold to any third party vendors.
520 $a The increasing demand for computational power is pushing the photolithography technology to its physical limit. As one of its crucial technical bottlenecks, the design issue of layout hotspots requires innovative solutions to minimize the necessity of conducting expensive lithography simulation experiments but remain extremely accurate. In addition to the sustained development of conventional hotspots detection, the recent implementation of convolutional neural networks (CNN) has shown high effectiveness in flexible pattern recognitions. However, limited by the industrial data availability, technical issues such as imbalanced learning and rare/new hotspot detection need to be navigated. In this thesis, we introduced a deep learning hotspots detection method with an improved CNN classifier and enhanced the learning effectiveness by augmenting the dataset with the generative adversarial network (GAN) based synthetic pattern generation. Experiment results show competitive performance compared to the existing works, but further development is required to connect with the current industrial processes.
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650 4 $a Mechanical engineering. $3 557493
653 $a Deep learning
653 $a Generative adversarial network
653 $a Hotspot detection
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710 2 $a University of Toronto (Canada). $b Mechanical and Industrial Engineering. $3 845502
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