Monitoring and Scheduling of Semiconductor Failure Analysis Labs
E Mastria, D Pagliaro, F Calimeri, S Perri… - … Conference on Logic …, 2024 - Springer
International Conference on Logic Programming and Nonmonotonic Reasoning, 2024•Springer
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 …
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 …
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.
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