  Manufacturing wafers is an intricate task involving thousands of steps.
Defect Pattern Recognition (DPR) of wafer maps is crucial to find the root
cause of the issue and further improving the yield in the wafer foundry.
Mixed-type DPR is much more complicated compared to single-type DPR due to
varied spatial features, the uncertainty of defects, and the number of defects
present. To accurately predict the number of defects as well as the types of
defects, we propose a novel compact deformable convolutional transformer (DC
Transformer). Specifically, DC Transformer focuses on the global features
present in the wafer map by virtue of learnable deformable kernels and
multi-head attention to the global features. The proposed method succinctly
models the internal relationship between the wafer maps and the defects. DC
Transformer is evaluated on a real dataset containing 38 defect patterns.
Experimental results show that DC Transformer performs exceptionally well in
recognizing both single and mixed-type defects. The proposed method outperforms
the current state of the models by a considerable margin
