Application of Various Neural Networks Architectures for Mask Calculation in Problem of Inverse Photolithography
Abstract
In this work, we solve the inverse problem of computational photolithography. The mask topology was calculated using deep neural networks. The study was aimed at comparing the effectiveness of the neural network architectures U-Net, Erf-Net and Deep Lab v3, as well as the built-in Calibre Workbench algorithms in solving the problem of inverse photolithography. Training of artificial neural networks was performed on a specially generated and labeled data set. Random shapes were generated using the Calibre Workbench CAD mask for the 90nm transistor gate mask. The comparison was made in terms of accuracy and speed. Edge placement error (EPE) and intersection over union (IOU) were used as metrics in the work. The use of neural networks made it possible to speed up the calculation of the mask by 100 times while maintaining an accuracy of 92% on the IOU metric.
About the Authors
Ya. M. Karandashev
ФГУ ФНЦ НИИСИ РАН;
Российский университет дружбы народов
Russian Federation
Russian Federation
G. S. Teplov
АО «НИИМЭ»;
Московский физико-технический институт
Russian Federation
Russian Federation
V. V. Keremet
Московский государственный университет им. М.В.Ломоносова
Russian Federation
Russian Federation
A. A. Karmanov
Московский физико-технический институт
Russian Federation
Russian Federation
A. V. Kuzovkov
АО «НИИМЭ»
Russian Federation
Russian Federation
M. Yu. Malsagov
ФГУ ФНЦ НИИСИ РАН
Russian Federation
Russian Federation
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Review
For citations:
Karandashev Ya.M., Teplov G.S., Keremet V.V., Karmanov A.A., Kuzovkov A.V., Malsagov M.Yu. Application of Various Neural Networks Architectures for Mask Calculation in Problem of Inverse Photolithography. МАТЕМАТИЧЕСКОЕ И КОМПЬЮТЕРНОЕ МОДЕЛИРОВАНИЕ СЛОЖНЫХ СИСТЕМ: ТЕОРЕТИЧЕСКИЕ И ПРИКЛАДНЫЕ АСПЕКТЫ. 2022;12(4):73-80. (In Russ.)
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