JEOS RP ISSN03 | Page 449

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J. Eur. Opt. Society-Rapid Publ. 22, 45( 2026)

Figure 5. Gamut size evolution for BO, GA and random,( a) for magnetron sputtering sample in scattering reflection mode,( b) for mesoporous film in backside reflection mode.

Table 1. Comparison of GA and BO gamut improvement for different intermediate dielectric layer thicknesses of the reflective samples elaborated by magnetron sputtering. Orange boxes indicate the best sample type identified with each method, the red underlining indicates the best method for a given sample.

3.2 Real time optimization

A“ live” experiment was also performed to complement the postmortem analysis. Contrary to the post-mortem method, this allows GA and BO to actually improve the gamut of inscribed colors by suggesting never-inscribedbefore parameters that will be marked experimentally on the sample. The volume improvement on the gamut was tracked for both methods: the same initial random selection of 40 laser parameters combinations was used for both methods. This experiment was done on several variations of the reflective sample elaborated by magnetron sputtering, all elaborated with different thicknesses of the intermediate dielectric layer. Measurements were done here in diffuse reflection observation mode.

The optimization results are compiled in Table 1. They show that, irrespective of the sample, the BO optimization leads to the largest gamut volume after the inscriptions of 400 colors. It is also the most effective optimization method after reaching the exit criterion, with the exception of one sample( 120 nm) where it is marginally surpassed by GA. The study also demonstrates that the optimal thickness to achieve the widest color gamut is 140 nm. However, if we were to rely solely on the GA optimization, the conclusion would have identified the 120 nm thickness as the most effective one to achieve a broader color palette. For the sake of illustration of the gain brought by the optimization process, Figure 6 compares images simulated by using either the color gamut obtained by considering randomly chosen laser parameter sets( pre-optimization), or the gamut optimized with the GA or BO. The corresponding gamuts can be found in Appendix E. This figure clearly illustrates the gain brought by the optimization processes. The first row corresponds to the gamut in transmission of a semi-transparent sample elaborated by sol-gel process. The second row corresponds to a gamut in specular reflection on a reflective sample elaborated by magnetron sputtering with a thickness of 140 nm. The third row corresponds to the same sample as in the second row, but for a gamut observed in diffuse reflection. In order to assess the precision of the reproductions, a metric designed for highly distorted and restricted gamut was employed, as defined in [ 46 ]. The score of this metric is indicated on the bottom right side of each simulated image. The better the reproduction, the lower the metric score. The proposed image-quality score is not calibrated as an identity metric. It is a linear model fitted