PreCoPlad - Research project

Laser processing is a flexible and cost-effective tool that recently opened new perspectives of applications in industry. Implemented on plasmonic metasurfaces, it produces very singular colours that can be tuned independently in different modes of observation. Laser-induced plasmonic colours thus enable printing multiplexed images, which have great promise in security printing and data storage. However, the latter require very good accuracy in colour printing. And, laser induced colours strongly depend on the initial state of the material. Predicting the full gamut of colorus that can be observed in different modes of observation on plasmonic metasurfaces processed by a large set of laser processing parameters, when the initial state of these metasurfaces can vary from one batch to another, appears then as a crucial step for industrial implementation. As physical models are missing for such predictions, other approaches must be found.

Deep Learning represents one of the most powerful family of models in machine learning when one has to make some predictions from data having some local structure such as images or surfaces. In this thesis, the objective is to provide some appropriate architectures accompanied with relevant objective functions to correctly train these architectures for accurate prediction of the laser printed colours. A first challenge is to consider physical properties of the materials and the laser processing parameters in the model. Then, another goal is to improve the robustness of the model by adapting existing adversarial robustness methods existing in image classification to laser printed colours. Finally, the third aspect tackled in this project is to develop models for being able to automatically adapt the learned models to slightly different initial metasurfaces by means of transfer learning/domain adaptation strategies. This last objective intends to offer a certain tolerance to unwanted variations in the initial metasurface elaboration while maintaining a very good accuracy on the prediction of the laser printed colours.