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Schrunner Stefan, Jenul Anna, Scheider Michael, Zernig Anja, Kaestner Andre, Kern Roman

A Health Factor for Process Patterns - Enhancing Semiconductor Manufacturing by Pattern Recognition in Analog Wafermaps

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy, 2019

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.

Santos Tiago, Schrunner Stefan, Geiger Bernhard, Pfeiler Olivia, Zernig Anja, Kaestner Andre, Kern Roman

Feature Extraction From Analog Wafermaps: A Comparison of Classical Image Processing and a Deep Generative Model

IEEE Transactions on Semiconductor Manufacturing, IEEE, 2019

Semiconductor manufacturing is a highly innovative branch of industry, where a high degree of automation has already been achieved. For example, devices tested to be outside of their specifications in electrical wafer test are automatically scrapped. In this paper, we go one step further and analyze test data of devices still within the limits of the specification, by exploiting the information contained in the analog wafermaps. To that end, we propose two feature extraction approaches with the aim to detect patterns in the wafer test dataset. Such patterns might indicate the onset of critical deviations in the production process. The studied approaches are: 1) classical image processing and restoration techniques in combination with sophisticated feature engineering and 2) a data-driven deep generative model. The two approaches are evaluated on both a synthetic and a real-world dataset. The synthetic dataset has been modeled based on real-world patterns and characteristics. We found both approaches to provide similar overall evaluation metrics. Our in-depth analysis helps to choose one approach over the other depending on data availability as a major aspect, as well as on available computing power and required interpretability of the results
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