Detection and correlation of yield loss induced by color resist deposition deviation with a deep learning approach applied to optical acquisitions
Résumé
On imager devices, color resists are used as optical filters to produce RGB pixel arrays. These layers are deposited
through spin coating process towards the end of the fabrication process flow, where complex topography can induce
thickness inhomogeneity effect over the wafer surface causing a radial striations signature, predominant at the edge of
the wafer. This deviation can induce important yield loss but is hardly detectable with standard inline metrology or
defectivity approach. In this study, an interferometry-based metrology system and a reflectometry-based defectivity
system were used to gather raw optical responses on the full wafer surface. Individual die cartographies were created
from those and a deep learning algorithm was trained from both optical techniques. We then applied the deep learning
algorithm on a specific set of test wafers to determine the number of dies affected by striations. From there, we evaluated
the correlation of the outcome classification with the final electrical tests done on each die of those wafers.