Multi-Illumination Feature Enhancement for Robust BGA Defect Detection and Explainable Attention Alignment

Abstract Ball Grid Array (BGA) inspection is critical for ensuring the reliability of semiconductor assemblies. Conventional RGB imaging under a single illumination often yields high classification metrics with pretrained CNNs, yet explainability analyses reveal that models may attend to spurious artifacts rather than solder ball defects.This paper introduces a png4ch multi-illumination dataset by stacking grayscale captures under four illumination intensities into a multi-channel representation.A 1×1 convolutional projection maps the four channels into three, enabling compatibility with pretrained CNN backbones. Comparative experiments with imgShiny (RGB single-illumination) and png4ch show that while RGB yields more consistent high F1 scores, multi-illumination significantly improves attention alignment with defect regions (Grad-CAM), underscoring the importance of dataset design for trustworthy industrial deployment.