Mr. Raghunandan Arava Profile

Raghunandan Arava

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Publications (2)

Proceedings Article | 22 November 2023 Presentation

Predicting resist pattern collapse in EUVL using machine learning

Sean D'Silva, Raghunandan Arava, Andreas Erdmann, Thomas Muelders, Hans-Juergen Stock

Proceedings Volume PC12750, PC127500I (2023) https://doi.org/10.1117/12.2691987

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Proceedings Article | 5 October 2023 Paper

Predicting resist pattern collapse in EUVL using machine learning

Sean D'Silva, Raghunandan Arava, Andreas Erdmann, Thomas Mülders, Hans-Jürgen Stock

Proceedings Volume 12802, 1280206 (2023) https://doi.org/10.1117/12.2675700

KEYWORDS: Extreme ultraviolet lithography, Machine learning, Deformation, Liquids, Capillaries, Line width roughness, Image classification, Simulations, Point clouds

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Pattern collapse has become a problem area in lithographic manufacturing due to the added complexity as we move towards extreme ultraviolet (EUV) focused mass production of semiconductor devices. Collapse is influenced mainly by the geometry and mechanical properties of the pattern. Photoresist patterns with higher aspect ratios (ARs) or lower feature spacing (dense features) are prone to collapse. This, combined with a lower stiffness value (Young’s modulus), adds to the undesirable deformation of patterns at the wafer level as we move towards advanced technology nodes. Such a deformation in the resist is seen after the rinsing stage and is caused mainly due to the non-uniform drying of the rinse liquid after chemical development. The main causes of this are the unbalanced Laplace pressure ΔP difference across the liquid-air interface and the surface tension force (STF) along the three-phase line. In addition to that, the sidewall surface roughness leads to localized regions of higher aspect ratios which makes collapse modeling in EUV lithography more challenging. The irregular variation in the aspect ratios increases the risk of collapse and also requires additional model considerations. A machine learning (ML) based approach is introduced to predict deformation characteristics for rough cross-sections (XZ plane) which is then utilized for the computation of the overall deformation of an entire lines and spaces (L/S) pattern. The 3D profiles are converted into a 2D representation using modified Fisher vectors (FVs) and labeled based on the estimated deformation of the given rough profile. A convolutional neural network (CNN) is then trained with the data generated and used to predict collapse probabilities for a given data set. For the prediction of collapse in pillar patterns, a slightly different ML-based approach is used based on the cross sections in the XY plane. A finite element method (FEM) model is implemented to calculate the deformations δ for a given pillar arrangement which then serve as labels for the training data. The cross-sections are stacked together along the height/thickness and a 3D convolutional neural network is trained and used for collapse prediction.

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