Proceedings - International Symposium for Testing and Failure Analysis · 2021 · 12 citations · 0 references
ComputingComputer ScienceElectrical EngineeringBiomedical ImagingDiagnostic ImagingMachine LearningComputed TomographyDeep LearningFault DetectionImage ReconstructionMedical ImagingX-ray Data AnalysisDeep Learning ReconstructionDeep Learning-based ReconstructionDevice Failure AnalysisFault Detection Using Deep LearningHigh-resolution X-ray ImagingLow-dose ReconstructionTomographyX-ray Computed Tomography
Abstract 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.