Assessing Electronics with Advanced 3D X-ray Imaging Techniques, Nanoscale Tomography, and Deep Learning

Abstract

This paper presents advanced workflows that combine 3D X-ray microscopy (XRM), nanoscale tomography, and deep learning (DL) to generate a detailed visualization of the interior of electronic devices and assemblies to enable the study of internal components for failure analysis (FA). Newly developed techniques, such as the integration of DL-based algorithms for 3D image reconstruction to improve scan quality through increased contrast and denoising, are also discussed in this article. In addition, a DL-based tool called DeepScout is presented. DeepScout uses 3D XRM scans in targeted regions of interest as training data for upscaling high-resolution in a low-resolution dataset, of a wider field of view, using a neural network model. Ultimately, these workflows can be run independently or complementary to other multiscale correlative microscopy evaluations, e.g., electron microscopy, and they will provide valuable insights into the inner workings of electronic packages and integrated circuits at multiple length scales, from macroscopic features on electronic devices (i.e., hundreds of mm) to microscopic details in electronic components (in the tens of nm). Understanding advanced electronic systems through X-ray imaging and machine learning—perhaps complemented with some additional correlative microscopy investigations—can speed development time, increase cost efficiency, and simplify FA and quality inspection of printed circuit boards (PCBs) and electronic devices assembled with new emerging technologies.