Void region segmentation in ball grid array using u-net approach and synthetic data

TL;DR

This paper presents a U-Net based method for void region segmentation in ball grid array solder joints, utilizing synthetic data generation to overcome dataset annotation challenges for high-quality, automated inspection.

Contribution

The paper introduces a synthetic data generation approach combined with U-Net for accurate void segmentation in electronic solder joints, addressing annotation difficulties and variability in real-world conditions.

Findings

U-Net effectively segments void regions in solder balls.

Synthetic data improves model training and robustness.

Method scales to various electronic products.

Abstract

The quality inspection of solder balls by detecting and measuring the void is important to improve the board yield issues in electronic circuits. In general, the inspection is carried out manually, based on 2D or 3D X-ray images. For high quality inspection, it is difficult to detect and measure voids accurately with high repeatability through the manual inspection and the process is time consuming. In need of high quality and fast inspection, various approaches were proposed, but, due to the various challenges like vias, reflections from the plating or vias, inconsistent lighting, noise and void-like artifacts makes these approaches difficult to work in all these challenging conditions. In recent times, deep learning approaches are providing the outstanding accuracy in various computer vision tasks. Considering the need of high quality and fast inspection, in this paper, we applied U-Net to segment the void regions in soldering balls. As it is difficult to get the annotated dataset covering all the variations of void, we proposed an approach to generated the synthetic dataset. The proposed approach is able to segment the voids and can be easily scaled to various electronic products.