Abstract
Finding non-conformities, such as physical failures causing electrical malfunctioning of a device, in modern semiconductor devices is challenging. Highly qualified employees in a failure analysis (FA) lab typically use sophisticated and expensive tools like scanning electron microscopes to identify and locate such non-conformities. Given the increasing complexity of investigated devices and very limited resources, labs may struggle to deliver analysis results in time.
This paper proposes an approach to optimize the usage of FA lab resources by combining constraint programming with stream reasoning enabling situation-dependent monitoring of the lab’s conditions and schedule maintenance. Evaluation results indicate that our system can significantly improve the tardiness of real-world FA labs, and all its computational tasks can be finished in an average time of 3.6 s, with a maximum of 15.2 s, which is acceptable for the lab’s workflows.
PNRR project FAIR - Future AI Research (PE00000013), Spoke 9 - Green-aware AI, under the NRRP MUR program funded by the “NextGenerationEU”; PNRR project Tech4You “Technologies for climate change adaptation and quality of life improvement”, CUP H23C22000370006, under the NRRP MUR program funded by the “NextGenerationEU”; PRIN project PINPOINT - exPlaInable kNowledge-aware PrOcess INTelligence, CUP H23C22000280006; and Austrian Research Promotion Agency (FFG, Project No. 887931).
E. Mastria and D. Pagliaro—Equal contributions.
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Notes
- 1.
DP-sr works according to the event time notion [1] allowing to obtain deterministic, reproducible, and consistent results as computations are independent of both arrival time and order of events.
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Mastria, E., Pagliaro, D., Calimeri, F., Perri, S., Pleschberger, M., Schekotihin, K. (2025). Monitoring and Scheduling of Semiconductor Failure Analysis Labs. In: Dodaro, C., Gupta, G., Martinez, M.V. (eds) Logic Programming and Nonmonotonic Reasoning. LPNMR 2024. Lecture Notes in Computer Science(), vol 15245. Springer, Cham. https://doi.org/10.1007/978-3-031-74209-5_17
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