AI-Enhanced Fault Detection Using Multi-Structured Data in Semiconductor Manufacturing

Linus Kohl (), Theresa Madreiter and Fazel Ansari
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Linus Kohl: Research Group of Production and Maintenance Management, TU Wien
Theresa Madreiter: Research Group of Production and Maintenance Management, TU Wien
Fazel Ansari: Research Group of Production and Maintenance Management, TU Wien

A chapter in Multimodal and Tensor Data Analytics for Industrial Systems Improvement, 2024, pp 297-312 from Springer

Abstract: Abstract The semiconductor industry is growing rapidly due to its key drivers, an increased chip demand for newly emerging technologies, as well as the existing ubiquity in industry and consumer goods. The equipment necessary for the manufacturing process is at the same time extremely expensive, and production processes are highly complex. To stay competitive and prevent yield loss, manufacturers are permanently trying to optimize current fault diagnostic and classification processes based on physical sensors within the production process. To enhance current approaches, fault detection must not be limited to structured sensor data; it should also include multi-structured data sources. This chapter outlines how current fault detection processes in semiconductor manufacturing can be improved by not only analyzing structured sensor data but also by including unstructured textual data, leading to an increased uptime. A multi-step algorithm is proposed, able to improve fault detection based on extracted problem statements and solutions from historical maintenance reports for an occurred failure. The performance of the introduced approach is evaluated in a simulation-based use case in the semiconductor industry, leading to an increase of 0.5% in uptime and an improvement of 12.03% in the mean time between failure, resulting in an improved overall equipment efficiency of 2.1%.

Keywords: Decision support systems; Fault detection; Maintenance; Neural networks; Text mining (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-53092-0_14

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