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3 May 2019 Estimating extremely low probability of stochastic defect in extreme ultraviolet lithography from critical dimension distribution measurement
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Abstract
Projection lithography using extreme ultraviolet (EUV) light at 13.5-nm wavelength will be applied to the production of integrated circuits below 7-nm design rules. In pursuit of further miniaturization, however, stochastic pattern defect problems have arisen, and monitoring such defect generation probabilities in extremely low range (<10  −  10) is indispensable. We discuss a method for predicting stochastic defect probabilities from a histogram of feature sizes for patterns several orders of magnitude fewer than the number of features to inspect. Based on our previously introduced probabilistic model of stochastic pattern defect, the defect probability is expressed as the product sum of the probability for edge position and the probability that film defect covers the area between edges, and we describe the latter as a function of edge position. The defect probabilities in the order between 10  −  7 and 10  −  5 were predicted from 105 measurement data for real EUV-exposed wafers, suggesting the effectiveness of the model and its potential for defect inspection.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Hiroshi Fukuda, Yoshinori Momonoi, and Kei Sakai "Estimating extremely low probability of stochastic defect in extreme ultraviolet lithography from critical dimension distribution measurement," Journal of Micro/Nanolithography, MEMS, and MOEMS 18(2), 024002 (3 May 2019). https://doi.org/10.1117/1.JMM.18.2.024002
Received: 29 January 2019; Accepted: 12 April 2019; Published: 3 May 2019
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CITATIONS
Cited by 4 scholarly publications.
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KEYWORDS
Stochastic processes

Inspection

Cadmium

Extreme ultraviolet lithography

Semiconducting wafers

Extreme ultraviolet

Statistical analysis

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