MetaRGBX-Net: RGB Sensitivity and Cross-Talk Prediction in CMOS Image Sensor

Abstract

The CMOS image sensor (CIS) underpins optical applications, enabling high-resolution imaging across the visible and near-infrared spectra. Advances in nanofabrication have enhanced pixel density, improving resolution, but as pixel dimensions approach the diffraction limit, maintaining optical sensitivity without performance trade-offs remains challenging. Nanophotonic solutions, like nanophotonic-based color routers, address these limitations. This study builds on MetaRGB-Net, a machine learning framework achieving 98% prediction accuracy in optimizing RGB sensitivity. We introduce MetaRGBX-Net, which employs separate neural networks to optimize both RGB sensitivity and cross-talk, achieving 95% and 98% prediction accuracy, respectively. This enables precise optimization of critical parameters and serves as a foundation for Bayesian optimization to refine metasurface designs, ensuring efficient light routing through RGB channels while minimizing cross-talk. MetaRGBX-Net streamlines metasurface design and provides a scalable foundation for next-generation CIS applications in IoT, biomedical imaging, and environmental monitoring.