AI improves 3D X-ray imaging for package failure analysis

August 31, 2020 //By Nick Flaherty
AI improves 3D X-ray imaging for package failure analysis
Iterative and deep learning reconstruction algorithms significantly enhance failure analysis throughput and image quality for the Zeiss Xradia Versa and Context microCT systems 

Zeiss has developed machine learning AI algorithms to improve its 3D X-ray imaging for failure analysis of semiconductor packages. 3D X-ray analysis is increasingly vital for 3D packaging of multiple chips in a single package with vias and interconnnect.

The Advanced Reconstruction Toolbox for the Zeiss Xradia Versa series of non-destructive 3D X-ray microscopes (XRM) and its Xradia Context 3D X-ray micro-computed tomography (microCT) systems is based around two modules: OptiRecon for iterative reconstruction, and DeepRecon, the first commercially available deep learning reconstruction technology for microscopy applications.

3D XRM is used for imaging defects to aid root cause investigation of package failures as it enables visualization of features that are not visible in 2D X-ray projection images. In package failure analysis, both fast results and high success rates are important. Consequently, decreasing imaging time while maintaining image quality is of very high value. Typically, Feldkamp-Davis-Kress (FDK) filtered back-projection algorithms are used to reconstruct the 3D dataset from many 2D projections acquired at different sample rotation angles. When image exposure times or numbers of projections are reduced in an effort to improve throughput, the FDK techniques often lead to degraded image quality.

The OptiRecon and DeepRecon engines enable higher scanning speeds while maintaining or even increasing image quality with improved contrast-to-noise ratios for semiconductor advanced packaging failure and structural analysis. In addition to electronics and semiconductor packaging, the Advanced Reconstruction Toolbox can be used for materials research, life sciences and advanced battery development.

OptiRecon is aimed at analysing a broad range of semiconductor packages and is suitable for both research and development applications, as well as FA. It uses iterative reconstruction, where differences are calculated between real and modeled projections through multiple iterations until convergence. This means fewer projections are needed with and less acquisition time compared to FDK. Scanning speeds as twice as fast with similar or better image quality can be achieved for semiconductor packages.

Next: Failure analysis benefits 


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