nach oben

Optical Memory and Neural Networks

Erschienen in:

01.10.2021

Using Machine Learning Methods to Predict the Magnitude and the Direction of Mask Fragments Displacement in Optical Proximity Correction (OPC)

verfasst von: P. E. Tryasoguzov, A. V. Kuzovkov, I. M. Karandashev, G. S. Teplov

Erschienen in: Optical Memory and Neural Networks | Ausgabe 4/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The paper studies the effectiveness of machine learning methods in computational photolithography. The first task is to determine the direction of displacement of the mask contour fragment. The second task is to determine the amount of displacement of the mask contour fragment. The machine learning models were trained on the data generated with Calibre WORKbench CAD in the form of radiation intensity vectors around the center of the segment. Comparisons were made between linear regression, random forest, gradient boosting, and feedforward convolutional neural network models. The most accurate results were demonstrated by the random forest model. With its help, it is possible to achieve an absolute error of 2 nm and an accuracy of displacement’s direction prediction of 97.9%.

Vorheriger Artikel Influence of Initial Guess on the Convergence Rate and the Accuracy of Wang–Landau Algorithm

Nächster Artikel High Sensibility Optical Water Sensor Using a One-Dimensional Defective Photonic Crystal

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Otto, O.W. et al., Automated optical proximity correction: a rules-based approach, Optical/Laser Microlithography VII, Int. Society for Optics and Photonics, 1994, vol. 2197, pp. 278–293.

Li, J. et al., Model-based optical proximity correction including effects of photoresist processes, Optical Microlithography X, Int. Society for Optics and Photonics, 1997, vol. 3051, pp. 643–651.

Kolobov, A.V., Ivanov, V.V., Kuzovkov, A.V., and Arilin R.A., OPC model development for 65 nm technology node, Electron. Eng., Ser. 3: Microelectronics, 2016, no. 4 (164), pp. 4–9.

Cummings, K.D., Frye, R.C., and Rietman, E.A., Using a neural network to proximity correct patterns written with a Cambridge electron beam microfabricator 10.5 lithography system, Appl. Phys. Lett., 1990, vol. 57, no. 14, pp. 1431–1433.CrossRef

Zach, F.X., Neural-network-based approach to resist modeling and opc, Optical Microlithography XVII, Int. Society for Optics and Photonics, 2004, vol. 5377, pp. 670–679.

Huang, W.C. et al., Intelligent model-based OPC, Optical Microlithography XIX, Int. Society for Optics and Photonics, 2006, vol. 6154, p. 615436.

Matsunawa, T. et al., A new lithography hotspot detection framework based on AdaBoost classifier and simplified feature extraction, Design-Process-Technology Co-optimization for Manufacturability IX, Int. Society for Optics and Photonics, 2015, vol. 9427, p. 94270S.

Yang, H. et al., Layout hotspot detection with feature tensor generation and deep biased learning, IEEE Trans. Computer-Aided Design of Integrated Circuits and Systems, 2018, vol. 38, no. 6, pp. 1175–1187.CrossRef

Yang, H. et al., Lithography hotspot detection: From shallow to deep learning, 2017 30th IEEE Int. System-on-Chip Conference (SOCC), IEEE, 2017, pp. 233–238.

10.

Xu, X. et al., A machine learning based framework for sub-resolution assist feature generation, Proc. of the 2016 on International Symposium on Physical Design, 2016, pp. 161–168.

11.

Xu, X. et al., Subresolution assist feature generation with supervised data learning, IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems, 2017, vol. 37, no. 6, pp. 1225–1236.CrossRef

12.

Shim, S., Choi, S., and Shin, Y., Machine learning (ML)-based lithography optimizations, 2016 IEEE Asia Pacific Conf. on Circuits and Systems (APCCAS), IEEE, 2016, pp. 530–533.

13.

Shim, S., Choi, S., and Shin, Y., Machine learning-based 3d resist model, Optical Microlithography XXX, Int. Society for Optics and Photonics, 2017, vol. 10147, pp. 101471D.

14.

Watanabe, Y. et al., Accurate lithography simulation model based on convolutional neural networks, Photomask Japan 2017: XXIV Symp. on Photomask and Next-Generation Lithography Mask Technology, Int. Society for Optics and Photonics, 2017, vol. 10454, pp. 104540I.

15.

Lin, Y. et al., Data efficient lithography modeling with residual neural networks and transfer learning, Proc. of the 2018 Int. Symp. on Physical Design, 2018, pp. 82–89.

16.

Yu, B. et al., Machine learning and pattern matching in physical design, 20th Asia and South Pacific Design Automation Conf., IEEE, 2015, pp. 286–293.

17.

Jia, N. and Lam, E.Y., Machine learning for inverse lithography: using stochastic gradient descent for robust photomask synthesis, J. Opt., 2010, vol. 12, no. 4, pp. 045601.CrossRef

18.

Luo, K. et al., SVM based layout retargeting for fast and regularized inverse lithography, J. Zhejiang Univ., Sci., C, 2014, vol. 15, no. 5, pp. 390–400.

19.

Yang, H. et al., GAN-OPC: Mask optimization with lithography-guided generative adversarial nets, Proc. of the 55th Annual Design Automation Conf., 2018, pp. 1–6.

20.

Ma, X. et al., Fast pixel-based optical proximity correction based on nonparametric kernel regression, J. Micro/Nanolithogr., MEMS, MOEMS, 2014, vol. 13, no. 4, p. 043 007.CrossRef

21.

Gu, A. and Zakhor, A., Optical proximity correction with linear regression, IEEE Trans. Semicond. Manuf. 2008, vol. 21, no. 2, pp. 263–271.CrossRef

22.

Li, Y., Yu, S.M., and Li, Y.L., Intelligent optical proximity correction using genetic algorithm with model-and rule-based approaches, Comput. Mater. Sci., 2009, vol. 45, no. 1, pp. 65–76.CrossRef

23.

Luo, R., Optical proximity correction using a multilayer perceptron neural network, J. Opt., 2013, vol. 15, no. 7, p. 075708.CrossRef

24.

Choi, S., Shim, S., and Shin, Y., Machine learning (ML)-guided OPC using basis functions of polar Fourier transform, Optical Microlithography XXIX, Int. Society for Optics and Photonics, 2016, vol. 9780, p. 97800H.

25.

Matsunawa, T., Yu, B., and Pan, D.Z., Optical proximity correction with hierarchical bayes model, Optical Microlithography XXVIII, Int. Society for Optics and Photonics, 2015, vol. 9426, p. 94260X.

26.

Cho, J., Cho, G., and Shin, Y., Optimization of machine learning guided optical proximity correction, 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS), IEEE, 2018, pp. 921–924.

27.

Kwon, Y., Song, Y., and Shin, Y., Optical proximity correction using bidirectional recurrent neural network (BRNN), Design-Process-Technology Co-optimization for Manufacturability XIII, Int. Society for Optics and Photonics, 2019, vol. 10962, p. 109620D.

Titel: Using Machine Learning Methods to Predict the Magnitude and the Direction of Mask Fragments Displacement in Optical Proximity Correction (OPC)
verfasst von: P. E. Tryasoguzov
A. V. Kuzovkov
I. M. Karandashev
G. S. Teplov
Publikationsdatum: 01.10.2021
Verlag: Pleiades Publishing
Erschienen in: Optical Memory and Neural Networks / Ausgabe 4/2021
Print ISSN: 1060-992X
Elektronische ISSN: 1934-7898
DOI: https://doi.org/10.3103/S1060992X21040056

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2021

Investigation of the Use of Neuron-Like Procedures for Processing Coherent Location Signals

Influence of Initial Guess on the Convergence Rate and the Accuracy of Wang–Landau Algorithm

High Sensibility Optical Water Sensor Using a One-Dimensional Defective Photonic Crystal

Formation Mechanisms of the Averaged Poynting Vector of a Polychromatic Wave

State of the Stratospheric Aerosol Layer over Tomsk in 2017 Using Data from Sensing at Siberian Lidar Station in Tomsk

Multimodal Convolutional Neural Networks for Detection of Covid-19 Using Chest X-Ray and CT Images

Premium Partner