Poster + Paper
21 November 2023 Optimized test pattern selection with machine learning method
Author Affiliations +
Conference Poster
Abstract
The technology node shrinks years after years. To guarantee the functionality and yield of IC production, the resolution enhancement technology becomes more and more important. Both optical proximity correction and inverse lithography technique need a precisely calibrated lithographic model. A mask of test patterns needs to be prepared and the lithographic experiment has to be done with it to obtain the CD SEM data for the model fitting. It is beneficial to select the test pattern efficiently. Fewer number of test patterns should be selected without compromising their coverage capability and the accuracy of the lithographic model. We present a machine learning method based on the convolutional autoencoder and core set selection method to achieve above goal. We optimize the existing test pattern mask by selecting parts of gauges out. The OPC models calibrated with the selected data are compared with the models calibrated with original test patterns to evaluate our method.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Peng Xu, Juan Wei, Jingkang Qin, Jinlai Liu, Guangyu Sun, Song Sun, Cuixiang Wang, Qingchen Cao, and Jiangliu Shi "Optimized test pattern selection with machine learning method", Proc. SPIE 12751, Photomask Technology 2023, 1275113 (21 November 2023); https://doi.org/10.1117/12.2685435
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KEYWORDS
Data modeling

Matrices

Machine learning

Optical proximity correction

Lithography

Active learning

Advanced patterning

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