As semiconductor processing has developed and become increasingly complex over time, the amount of data and sources of data have also increased. This trend complicates the data analysis tasks performed by many fab engineers throughout the industry, requiring the need to gather data from multiple sources and reach conclusions quickly and efficiently. In the case of root cause analysis for a deviation or excursion, it is especially important for analysis solutions to pinpoint possible root causes quickly and allow the engineer to contain and resolve the issue before it becomes a major quality incident. Today, there are effective solutions to address some of these cases. However, more advanced and automated analysis solutions are needed to unlock interactivity across the full data continuum not only within the fab, but across the life cycle of the device. One approach, which is used by the new Fab.da solution provided by Synopsys, organizes the individual data components into a data lakehouse, demonstrates performance improvements when compared to older technologies, and unlocks new use cases in the areas of decision support and joining with data from the design and mask optimization space.
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Barar et al. (2024) studied this question.
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