  For over 40 years lithographic silicon scaling has driven circuit integration
and performance improvement in the semiconductor industry. As silicon scaling
slows down, the industry is increasingly dependent on IC package technologies
to contribute to further circuit integration and performance improvements. This
is a paradigm shift and requires the IC package industry to reduce the size and
increase the density of internal interconnects on a scale which has never been
done before. Traditional package characterization and process optimization
relies on destructive techniques such as physical cross-sections and delayering
to extract data from internal package features. These destructive techniques
are not practical with today's advanced packages. In this paper we will
demonstrate how data acquired non-destructively with a 3D X-ray microscope can
be enhanced and optimized using machine learning, and can then be used to
measure, characterize and optimize the design and production of buried
interconnects in advanced IC packages. Test vehicles replicating 2.5D and HBM
construction were designed and fabricated, and digital data was extracted from
these test vehicles using 3D X-ray and machine learning techniques. The
extracted digital data was used to characterize and optimize the design and
production of the interconnects and demonstrates a superior alternative to
destructive physical analysis. We report an mAP of 0.96 for 3D object
detection, a dice score of 0.92 for 3D segmentation, and an average of 2.1um
error for 3D metrology on the test dataset. This paper is the first part of a
multi-part report.
