Mr. Nicola Senin Profile

Nicola Senin

at Univ. of Nottingham

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Publications (2)

Proceedings Article | 20 June 2021 Presentation + Paper

Intelligent quality monitoring for additive manufactured surfaces by machine learning and light scattering

Mingyu Liu, Nicola Senin, Richard Leach

Proceedings Volume 11782, 1178206 (2021) https://doi.org/10.1117/12.2592554

KEYWORDS: Machine learning, Additive manufacturing, Data modeling, Light scattering, Data acquisition, Scattering, Neural networks, 3D modeling, Sensors, Metals

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Proceedings Article | 7 April 2020 Presentation + Paper

Cascaded machine learning model for reconstruction of surface topography from light scattering

Mingyu Liu, Nicola Senin, Rong Su, Richard Leach

Proceedings Volume 11352, 113520Q (2020) https://doi.org/10.1117/12.2555035

KEYWORDS: Machine learning, Light scattering, Scattering, Neural networks, Computer simulations

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Light scattering methods are promising for in-process surface measurement. Many researchers have investigated light scattering methods for evaluating surface texture. Researchers working on scatterometry have developed methods to derive surface texture parameters by solving the inverse scattering problem. However, most of the research has been focused only on texture measurement or determination of critical dimensions where feature sizes are less than the wavelength of the light source. In this paper, we propose a new light scattering method to reconstruct the surface topography of grating patterns, using a cascaded machine learning model. The experimental scattering signal can be fed into the machine learning model as the input and the surface topography can be determined as the output. The training dataset, i.e. scattering signals of different surfaces, are generated through a validated rigorous surface scattering model based on a boundary element method (BEM). In this way, the machine learning model can be trained using a big data approach including tens of thousands of datasets, which represent most of the scenarios in real cases. The cascaded machine learning model is designed as a combined top-down, two-layer model implemented using neural networks. The first layer consists of a classification model designed to determine which type of structured surface is being measured, amongst a set of predefined design variants. The second layer contains a regression model, designed to determine the values of the design parameters defining the specific type of structured surface which has been identified, for example the nominal pitch and height of its periodic features. We have developed a prototype system and conducted experiments to verify the proposed method. Structured surfaces containing grating patterns were considered, and different types of gratings were analysed. The results were validated by comparison with measurements performed with atomic force microscopy.

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