Bayesian Design of Metasurface Routers for CMOS Image Sensors via MetaRGBX-Net

Abstract

This letter presents a Bayesian optimization framework based on MetaRGBX-Net for tuning meta-atom diameters to achieve specific RGB sensitivity and interpixel crosstalk (XTALK) targets. MetaRGBX-Net—developed in prior work—is validated as an effective surrogate model within the optimization process and enables successful tuning of in-bound (IB) configurations, even near distribution boundaries. RGB sensitivity and XTALK errors were both maintained below 10% through balanced trade-offs. Experimental validation confirms these outcomes, emphasizing the impact of penalty weight adjustments—particularly for blue sensitivity, which was more responsive than red or green. In contrast, out-of-bound (OB) configurations resulted in notable performance degradation across all algorithms, with excessive XTALK and unmet RGB targets. These results underscore the framework’s potential and the importance of well-designed penalty functions and target selection for optimal performance.