| Zugriffsnummer | 44472 |
| Dokumenttyp | Zeitschriftenartikel |
| Peer Review | mit Peer Review |
| Sprache | Englisch |
| Titel | Efficient Bayesian inversion for shape reconstruction of lithography masks |
| Autor(in); Institution |
Farchmin, Nando; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Hammerschmidt, Martin; JCMwave GmbH, Berlin, GERMANY; Konrad-Zuse-Zentrum für Informationstechnik, Berlin, GERMANY
Schneider, Philipp-Immanuel; JCMwave GmbH, Berlin, GERMANY; Konrad-Zuse-Zentrum für Informationstechnik, Berlin, GERMANY
Wurm, Matthias; 4.2, Bild- und Wellenoptik, PTB-Braunschweig; 7.2, Röntgenmesstechnik mit Synchrotronstrahlung, PTB-Berlin
Bodermann, Bernd; 4.2, Bild- und Wellenoptik, PTB-Braunschweig
Bär, Markus; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
Heidenreich, Sebastian; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin
|
| Quelle/Jahr | Journal of Micro/Nanolithography, MEMS and MOEMS: 19 (2020), 2, 024001-1 - 024001-12 |
| ISSN | 1932-5150 (PRINT) ; 1932-5134 (ONLINE) |
| DOI | |
| Verlag | Bellingham, Wash.: SPIE |
| Freie Schlagworte | uncertainty quantification ; polynomial chaos ; inverse problem ; parameter reconstruction ; scatterometry |
| Zusammenfassung | Background: Scatterometry is a fast, indirect, and nondestructive optical method for quality control in the production of lithography masks. To solve the inverse problem in compliance with the upcoming need for improved accuracy, a computationally expensive forward model that maps geometry parameters to diffracted light intensities has to be defined. Aim: To quantify the uncertainties in the reconstruction of the geometry parameters, a fast-to-evaluate surrogate for the forward model has to be introduced. Approach: We use a nonintrusive polynomial chaos-based approximation of the forward model, which increases speed and thus enables the exploration of the posterior through direct Bayesian inference. In addition, this surrogate allows for a global sensitivity analysis at no additional computational overhead. Results: This approach yields information about the complete distribution of the geometry parameters of a silicon line grating, which in return allows for quantifying the reconstruction uncertainties in the form of means, variances, and higher order moments of the parameters. Conclusions: The use of a polynomial chaos surrogate allows for quantifying both parameter influences and reconstruction uncertainties. This approach is easy to use since no adaptation of the expensive forward model is required. |
| Themenbereich der Metrologie | Mathematik und metrologische Informationstechnik |
| Förderinformationen (1) |
Förderername: German Central Innovation Program (ZIM)
Förderungsnummer: ZF4014017RR7 |
Zitierung
Farchmin, N., Hammerschmidt, M., Schneider, P.-I., Wurm, M., Bodermann, B., Bär, M., & Heidenreich, S. (2020). Efficient Bayesian inversion for shape reconstruction of lithography masks. Journal of Micro/Nanolithography, MEMS and MOEMS, 19(2), 024001-1–024001-12. https://doi.org/10.1117/1.jmm.19.2.024001