This lunch lecture will be opened by Dr. Yanja Dajsuren, director of the PDEng Software Technology (ST) program at TU/e. She will introduce briefly the PDEng programs at TU/e and then welcome Ani Megerdoumian as a speaker for the lunch lecture. Ani Megerdoumian is the winner of the PDEng ST Design Award 2020. She will talk about her PDEng graduation project at ASML with a focus on AI and Software Design. An abstract of her project can be found below. There will be a Q&A session at the end of the lunch lecture.
As the complexity of any system grows, the need for diagnostics becomes essential. In this sense, the data produced as an input for any complex system, like the high-tech machine of ASML is critical. During the wafer production cycle in TWINSCAN machines, the measurement, modeling, and applied corrections are all logged to a diagnostic file called MDL (Machine Diagnostic Log). MDL contains essential data about the behavior and performance of ASML’s machines, and this data helps designers (as well as support engineers) to understand the behavior of the machine during the production. Over the years, customers have started using informal MDL data as well. By design, MDL does not protect itself from incompatible changes. This project is initiated to analyze the possibility of converting all needed data to official XML-based files at low cost. As a roadmap, the current intention is to provide formal data to the customers. This, however, comes at a high cost if conversion is done manually. As a result of my PDEng graduation project, a support tool, with mappings of the log file content to XML-based file is delivered. Besides, an iterable pipeline and corresponding prototype for producing the mapping based on domain expert opinions is delivered. Finally, a prototype which provides an XML-based report for aiding the human resources in designing the XML-based formal reports is submitted. My PDEng thesis report presents the domain analysis done to model the major requirements needed to be transferred to the formal file format. In other words, transferring the meta information and place it in the XML-based reports to cover the needs of diagnostics architects and engineers. In the next phase of analysis and to decrease the costs of the transferring the data to the formal format, artificial intelligence methods are used to determine how many reports should be generated for each task in the machine and what information each report shall cover.
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