  Identifying defect patterns in a wafer map during manufacturing is crucial to
find the root cause of the underlying issue and provides valuable insights on
improving yield in the foundry. Currently used methods use deep neural networks
to identify the defects. These methods are generally very huge and have
significant inference time. They also require GPU support to efficiently
operate. All these issues make these models not fit for on-line prediction in
the manufacturing foundry. In this paper, we propose an extremely simple yet
effective technique to extract features from wafer images. The proposed method
is extremely fast, intuitive, and non-parametric while being explainable. The
experiment results show that the proposed pipeline outperforms conventional
deep learning models. Our feature extraction requires no training or
fine-tuning while preserving the relative shape and location of data points as
revealed by our interpretability analysis.
