I had the pleasure to talk about our work on an information-theoretic similarity measure for patterns on analog wafermaps. Analog wafermaps depict electrical measurement values of devices on a wafer, and patterns on these wafermaps may indicate process deviations. Detection and classifying these patterns, and reacting appropriately, can prevent further such deviations and, consequently, yield loss. Our work, a collaboration between Know-Center and K-AI within the SemI40 project, makes use of a feature extraction pipeline that was recently accepted for publication in the IEEE Transactions on Semiconductor Manufacturing. If you are interested in the slides, just click on the image below.
