NIST only participates in the February and August reviews.
As Moore’s law, based on Dennard scaling, slows down, heterogeneous integration (HI) is becoming an important manufacturing paradigm enabled by advanced packaging techniques. HI is creating more complex chip structures, such as three-dimensionally (3D)-stacked chips, through-silicon vias, through-glass vias, and hybrid bonding. These features can have invisible buried defects and require non-destructive testing (NDT). These HI structures also challenge many conventional NDT methods, and X-ray-based imaging methods show promise. X-ray computed tomography (XCT) has been essential for failure analysis (FA), and the role of NDT is expanding from FA to near-line/in-line inspection. This type of inspection requires much higher throughput to be economically viable, which requires reduced acquisition time, advanced scanning strategies, and novel reconstruction and defect-detection algorithms based on artificial intelligence and machine learning (AI/ML).
Proposals are welcome to develop solutions to improve nondestructive detection metrology. For example, new physical artifacts/phantoms with programmed defects may be developed for NDT instrument performance evaluation, or new methods for developing reference datasets from industrially relevant samples or via realistic physics-based methods may be investigated. Novel reconstruction or image segmentation algorithms may be developed from the artifacts and datasets generated by the team. Our facility supports experimental work via a state-of-the-art X-ray computed laminography/tomography instrument, precision mechanical polisher, and various nanofabrication (e.g., lithography and etching) and characterization (e.g., focused ion beam and scanning electron microscope) instruments from NanoFab. The team has access to several GPU-accelerated workstations and commercial XCT simulation, reconstruction, and analysis software. Furthermore, NIST provides computational resources and an AI interest group that meets regularly, giving the successful applicant an opportunity to interact with a variety of NIST engineers and scientists.
[1] F.H. Kim, H. Yeung, and E.J. Garboczi (2021), “Characterizing the effects of laser control in laser powder bed fusion on near-surface pore formation via combined analysis of in-situ melt pool monitoring and X-ray computed tomography”, Additive Manufacturing, 48, 102372
[2] F.H. Kim, A.L. Pintar, J.H.J. Scott, E.J. Garboczi (2023), “Evaluation of Flaw Detection Algorithm Using Simulated X-Ray Computed Tomography of Ground Truth Data”, ASME J Nondestructive Evaluation. 6(4): 041005
[3] F.H. Kim, S.M. Robinson, N.N. Klimov, and J.-H.J. Scott (2025), “X-ray computed tomography flaw phantom development: stepper photolithography and deep reactive ion etching: Fabrication and reference measurements”, NIST Advanced Manufacturing Series 100-63, https://doi.org/10.6028/NIST.AMS.100-63
Nondestructive testing; X-ray computed tomography; X-ray computed laminography; phantom; image reconstruction; image segmentation; semiconductor; advanced packaging; heterogeneous integration; defect
level
Open to Postdoctoral applicants