Explosion in defectivity requires much faster determination of critical and non-critical defects.
The number of defects detected through inspection is exploding at each new process node. There are now millions of defects being identified on each wafer, but only a fraction of those can cause problems. Prasad Bachiraju, senior director of business development at Onto Innovation, talks about the different types of images being captured using different illumination modes at different touch points, how that data gets classified, and how AI and machine learning at the edge can be used to identify critical errors and separate out the nuisance errors that may be caused by process variation and neighborhood noise.
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