Depth estimation from a single CD-SEM image using domain adaptation with multimodal data

Tim Houben; Thomas Huisman; Maxim Pisarenco; Fons van der Sommen; Peter de With

doi:10.1117/12.2622635

4 March 2022 Depth estimation from a single CD-SEM image using domain adaptation with multimodal data

Tim Houben, Thomas Huisman, Maxim Pisarenco, Fons van der Sommen, Peter de With

Proceedings Volume 12084, Fourteenth International Conference on Machine Vision (ICMV 2021); 120841I (2022) https://doi.org/10.1117/12.2622635
Event: Fourteenth International Conference on Machine Vision (ICMV 2021), 2021, Rome, Italy

Abstract

There is a growing need for accurate depth measurements of on-chip structures, fueled by the ongoing size reduction of integrated circuits. However, current metrology methods do not offer a satisfactory solution. As Critical Dimension Scanning Electron Microscopes (CD-SEMs) are already being used for fast and local 2D imaging, it would be beneficial to leverage the 3D information hidden in these images. In this paper, we present a method that can predict depth maps from top-down CD-SEM images. We demonstrate that the proposed neural network architecture, together with a tailored training procedure, leads to accurate depth predictions on synthetic and real experimental data. Our training procedure includes a domain adaptation step, which utilizes data from a different modality (scatterometry), in the absence of ground truth data in the experimental CD-SEM domain. The mean relative error of the proposed method is smaller than 6.2% on a contact-hole dataset of synthetic CD-SEM images with realistic noise levels. Furthermore, we show that the method performs well in terms of important semiconductor metrics. To the extent of our knowledge, we are the first to achieve accurate depth estimation results on experimental data, by combining data from the aforementioned modalities. We achieve a mean relative error smaller than 1%.

Citation Download Citation

Tim Houben, Thomas Huisman, Maxim Pisarenco, Fons van der Sommen, and Peter de With "Depth estimation from a single CD-SEM image using domain adaptation with multimodal data", Proc. SPIE 12084, Fourteenth International Conference on Machine Vision (ICMV 2021), 120841I (4 March 2022); https://doi.org/10.1117/12.2622635

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KEYWORDS

Data modeling

3D modeling

Semiconducting wafers

Monte Carlo methods

Neural networks

Metrology

3D metrology

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