Multivariate Analysis of Wafer Process Data
| Autor: | Evans, R., Stegemann, E., Dinkel, T., Klöter, B., Stoye, P., Petter, K., Boreland, M. |
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| Jazyk: | angličtina |
| Rok vydání: | 2015 |
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| DOI: | 10.4229/eupvsec20152015-2bv.8.66 |
| Popis: | 31st European Photovoltaic Solar Energy Conference and Exhibition; 912-917 As PV manufacturers embrace common manufacturing quality protocols such as “six sigma”, understanding process variance becomes a crucial issue. Setting up a good system of quality management involves understanding and minimising variance, which in turn requires that all the steps in production need to work together to keep final variance as low as possible. Process control data has been used throughout the manufacturing sequence for some time, but mostly with the focus on individual processes or limited process interactions. The unique Tra.Q wafer tracking system at Hanwha Q CELLS’ facility in Bitterfeld-Wolfen, Germany can accurately track each wafer through the entire cell and module manufacturing sequence, joining all of this process control data together. This enables a deeper understanding of process interactions and the origins of production variance, as well as the development of sophisticated process control methods. For the analysis of datasets with large data dimensionality, multivariate statistical techniques can be used to identify and describe the most important relationships. |
| Databáze: | OpenAIRE |
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