The semiconductor industry is no longer driven purely by performance. Miniaturization, increased functionality, low latency and high bandwidth requirements are becoming more important. Furthermore, as Moore’s law scaling becomes more difficult and costly, innovations in packaging technologies through heterogeneous integration are being adopted rapidly to meet these demands. This paper discusses how defects in InFO (Integrated Fan-Out) wafer level multi-die semiconductor packages can be successfully root caused and describes the challenges faced when doing failure analysis of such packages.

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.

Lock-in thermography (LIT) has been successfully applied in different excitation and analysis modes including classical LIT, analysis of the time-resolved temperature response (TRTR) upon square wave excitation and TRTR analysis in combination with arbitrary waveform stimulation. The results obtained by both classical square wave- and arbitrary waveform stimulation showed excellent agreement. Phase and amplitudes values extracted by classical LIT analysis and by Fourier analysis of the time resolved temperature response also coincided, as expected from the underlying system theory. In addition to a conceptual test vehicle represented by a point-shaped thermal source, two semiconductor packages with actual defects were studied and the obtained results are presented herein. The benefit of multi-parametric imaging for identification of a defect’s lateral position in the presence of multiple hot spots was also demonstrated. For axial localization, the phase shift values have been extracted as a function of frequency [4]. For comparative validation, LIT analyses were conducted in both square wave and arbitrary waveform excitation using custom designed and sample-specific stimulation signals. In both cases result verification was performed employing X-ray, scanning electron microscopy (SEM) and energy dispersive x-ray (EDX) as complementary techniques.