3D X-ray microscopy (XRM) is an effective highresolution and non-destructive tool for semiconductor package level failure analysis. One limitation with XRM is the ability to achieve high-resolution 3D images over large fields of view (FOVs) within acceptable scan times. As modern semiconductor packages become more complex, there are increasing demands for 3D X-ray instruments to image encapsulated structures and failures with high productivity and efficiency. With the challenge to precisely localize fault regions, it may require high-resolution imaging with a FOV of tens of millimeters. This may take over hundreds of hours of scans if many high-resolution but small-volume scans are performed and followed with the conventional 3D registration and stitches. In this work, a novel deep learning reconstruction method and workflow to address the issue of achieving highresolution imaging over a large FOV is reported. The AI powered technique and workflow can be used to restore the resolution over the large FOV scan with only a high-resolution and a large FOV scan. Additionally, the 3D registration and stitch workflow are automated to achieve the large FOV images with a recovered resolution comparable to the actual high-resolution scan.

3D X-ray tomography plays a critical role in electronic device failure analysis, but it can take several hours to overnight to get sufficient resolution in fault regions to detect and identify defects. In this paper, we propose a machine learning based reconstruction technique that can speed up data acquisition by a factor of four or more, while maintaining image quality. The method, which uses neural networks, extracts signals from low-dose data more efficiently than the conventional Feldkamp-Davis-Kress (FDK) approach, which is sensitive to noise and prone to aliasing errors. Several semiconductor packages and a commercial smartwatch battery module are analyzed using the new technique and the results compared with those obtained using conventional methods. The neural network can be trained on as little as one tomography image and the only requirement for the training data is that the sample or region of interest is well represented with all characteristic features in the field-of-view.

In wafer fabrication, Fluorine (F) contamination may cause fluorine-induced corrosion and defects on microchip Aluminum (Al) bondpads, resulting in bondpad discoloration or non-stick on pads (NSOP). Auger Electron Spectroscopy (AES) is employed for measurements of the fluorine level on the Al bondpads. From a Process control limit and a specification limit perspective, it is necessary to establish a control limit to enable process monitor reasons. Control limits are typically lower than the specification limits which are related to bondpad quality. The bondpad quality affects the die bondability. This paper proposes a simulation method to determine the specification limit of Fluorine and a Shelf Lifetime Accelerated Test (SLAT) for process monitoring. Wafers with different F levels were selected to perform SLAT with high temperature and high relative humidity tests for a fixed duration to simulate a one year wafer storage condition. The results of these simulation results agree with published values. If the F level on bondpad surfaces was less than 6.0 atomic percent (at%), then no F induced corrosion on the bond pads was observed by AES. Similarly, if the F level on bond pad surfaces was higher than 6.0 atomic per cent (at%) then AES measured F induced corrosion was observed.