Electrical measurement data at the end of semi- conductor frontend production, so-called wafer test data, pro- vide deep insight into the preceding manufacturing process. Patterns in these datasets, such as spatial regularities on the wafer, frequently indicate that deviations occurred during production, potentially leading to failures in the produced devices. As such patterns of interest differ w.r.t. their shapes and equally important their intensities, pattern recognition is challenging, but crucial as a prerequisite for production environments in Industry 4.0. In this work, we propose an indicator for the presence and development of process patterns, a so-called ”Health Factor for Process Patterns”, embedded in a framework of statistical decision theory. We provide adequate machine learning components, focusing on the recognition and assessment of known patterns in analog wafer test data. Finally, we conduct experiments using simulated as well as real-world datasets to demonstrate that our method yields competitive results and can be extended to a decision support system for industrial usage.
|Publication status||Published - 2019|
|Event||IEEE International Conference on Systems, Man and Cybernetics - Bari, Italy|
Duration: 6 Oct 2019 → 9 Oct 2019
|Conference||IEEE International Conference on Systems, Man and Cybernetics|
|Period||6/10/19 → 9/10/19|