  Recently, semiconductors' demand has exploded in virtual reality,
smartphones, wearable devices, the internet of things, robotics, and
automobiles. Semiconductor manufacturers want to make semiconductors with high
yields. To do this, manufacturers conduct many quality assurance activities.
Wafer map pattern classification is a typical way of quality assurance. The
defect pattern on the wafer map can tell us which process has a problem. Most
of the existing wafer map classification methods are based on supervised
methods. The supervised methods tend to have high performance, but they require
extensive labor and expert knowledge to produce labeled datasets with a
balanced distribution in mind. In the semiconductor manufacturing process, it
is challenging to get defect data with balanced distribution. In this paper, we
propose a one-class classification method using an Adversarial Autoencoder
(AAE) with Deep Support Vector Data Description (DSVDD) prior, which generates
random vectors within the hypersphere of DSVDD. We use the WM-811k dataset,
which consists of a real-world wafer map. We compare the F1 score performance
of our model with DSVDD and AAE.
