Repair simulation with image processing and artificial intelligence

Yo-Han Choi; Moon-Kyu Sung; Sang-Hyun Lee; Ji-Hyung Lee; Jin-Hong Park; Ji-Hyun Choi; Seong-Yong Moon; Sung-Woon Choi; Woo-Sung Han

doi:10.1117/12.599369

10 May 2005 Repair simulation with image processing and artificial intelligence

Yo-Han Choi, Moon-Kyu Sung, Sang-Hyun Lee, Ji-Hyung Lee, Jin-Hong Park, Ji-Hyun Choi, Seong-Yong Moon, Sung-Woon Choi, Woo-Sung Han

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Proceedings Volume 5752, Metrology, Inspection, and Process Control for Microlithography XIX; (2005) https://doi.org/10.1117/12.599369
Event: Microlithography 2005, 2005, San Jose, California, United States

Abstract

Defect-free mask is a dream of mask makers. Repair technology [1] that removes defects on Att. PSM is getting more attentions than ever. Therefore the fast and precise verification of repaired results is highly required. Most confirmation methods are carried out by using the inspection system because it is faster than AIMS to verify the repaired results. However, the accuracy of the verification using the inspection system cannot be compared to it with AIMS in the view of printability. In this paper, the results of optical simulation using top-down repair image are compared with those of AIMS for rapid confirmation of repaired results with competitive accuracy. Also, neural network which can compute the complex non-linear relationships easily are used to increase the accuracy of repair simulation.

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Yo-Han Choi, Moon-Kyu Sung, Sang-Hyun Lee, Ji-Hyung Lee, Jin-Hong Park, Ji-Hyun Choi, Seong-Yong Moon, Sung-Woon Choi, and Woo-Sung Han "Repair simulation with image processing and artificial intelligence", Proc. SPIE 5752, Metrology, Inspection, and Process Control for Microlithography XIX, (10 May 2005); https://doi.org/10.1117/12.599369

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KEYWORDS

Neural networks

Neurons

Optical simulations

Image segmentation

Edge detection

Quartz

Artificial intelligence

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