  Statistical quality control in semiconductor manufacturing hinges on
effective diagnostics of wafer bin maps, wherein a key challenge is to detect
how defective chips tend to spatially cluster on a wafer--a problem known as
spatial pattern recognition. Recently, there has been a growing interest in
mixed-type spatial pattern recognition--when multiple defect patterns, of
different shapes, co-exist on the same wafer. Mixed-type spatial pattern
recognition entails two central tasks: (1) spatial filtering, to distinguish
systematic patterns from random noises; and (2) spatial clustering, to group
filtered patterns into distinct defect types. Observing that spatial filtering
is instrumental to high-quality mixed-type pattern recognition, we propose to
use a graph-theoretic method, called adjacency-clustering, which leverages
spatial dependence among adjacent defective chips to effectively filter the raw
wafer maps. Tested on real-world data and compared against a state-of the-art
approach, our proposed method achieves at least 46% gain in terms of internal
cluster validation quality (i.e., validation without external class labels),
and about ~5% gain in terms of Normalized Mutual Information--an external
cluster validation metric based on external class labels. Interestingly, the
margin of improvement appears to be a function of the pattern complexity, with
larger gains achieved for more complex-shaped patterns.
