Improving data-driven OCD uncertainties from Gaussian process regression

Bryan M. Barnes; Mark-Alexander Henn

doi:10.1117/12.2584821

22 February 2021 Improving data-driven OCD uncertainties from Gaussian process regression

Proceedings Volume 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV; 116111K (2021) https://doi.org/10.1117/12.2584821
Event: SPIE Advanced Lithography, 2021, Online Only

Abstract

The continued extensibility of optical critical dimension (OCD) metrology relies not only upon experimental advances but also upon improved, rigorous data analysis. While often approached using traditional non-linear regression (NLR), the inverse problem inherent to OCD metrology can also be addressed using machine learning (ML) techniques. In our recent comparison between two ML approaches, Gaussian process regression (GPR) and NLR enhanced with radial basis function (RBF) interpolation, these methods could yield similar parametric values for a sufficient number of training points, but the GPR yielded much larger uncertainties. Here, we refine and explore further GPR through the addition and explanation of additional parameters, often better capturing crucial physical considerations. By identifying these key fitting attributes, reductions in both parametric uncertainties and in parametric bias are realized. Industrial applicability of GPR and similar ML approaches is discussed with respect to its computational costs.

Conference Presentation

Citation Download Citation

Bryan M. Barnes and Mark-Alexander Henn "Improving data-driven OCD uncertainties from Gaussian process regression", Proc. SPIE 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV, 116111K (22 February 2021); https://doi.org/10.1117/12.2584821

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