Adaptive deep learning for the prediction of laser-induced colours on evolving printable substrates

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description Context: . Laser-induced colouring has become a key technology for producing visual security features on high-value documents and products. By locally and precisely tuning the optical properties of a substrate through carefully controlled laser parameters, it is possible to generate a wide range of colours in a contactless, flexible and cost-efficient manner, and even to multiplex several images that can be revealed independently under different observation conditions (e.g. transmission versus reflection). Scaling up the technology requires to guarantee perfect reproducibility over time. However, colour produced by a given set of laser parameters depends strongly on the initial optical state of the printable substrate. There is currently no physical model able to predict, quickly and reliably, the colours obtainable on a given sample, and rigorous electromagnetic simulations remain far too costly to be generalised at the scale of millimetre-large, quasi-random metasurfaces. Topics: . This 18-month post-doctoral position, funded by Toppan Security and carried out at Laboratoire Hubert Curien, aims to improve the reliability of the laser colouring process using deep learning. The work targets a printable-substrate-plus-laser-process technology recently brought to market by Toppan Security, for which batch-to-batch reproducibility is not perfectly guaranteed by the fabrication process.