AI-based image enhancement improves detection of white bump defects

Summary:

Traditional failure analysis (FA) methods face growing challenges due to the increasing miniaturization and complexity of modern semiconductor devices. In particular, the detection of cracks in the extreme low K layers of flip-chips, utilized in advanced packaging architectures at the back end of line (BEOL), is challenging. To improve detectability of these defects, we propose an AI-based image enhancement workflow, specifically tailored to improve scanning acoustic microscopy (SAM) images. By applying a Deep CNN with Skip Connections and Network in Network (DCSCN) model to low-resolution C-scan SAM images, we are able to improve perceptual quality of images. The approach boosts resolution-dependent analysis like AI-based object detection and classification as well as decreases significantly SAM scanning time. Our work demonstrates that AI-based image enhancement can significantly improve FA workflows, offering a robust and efficient solution for modern semiconductor analysis.