  In semiconductor manufacturing, the wafer dicing process is central yet
vulnerable to defects that significantly impair yield - the proportion of
defect-free chips. Deep neural networks are the current state of the art in
(semi-)automated visual inspection. However, they are notoriously known to
require a particularly large amount of data for model training. To address
these challenges, we explore the application of generative adversarial networks
(GAN) for image data augmentation and classification of semiconductor wafer
dicing induced defects to enhance the variety and balance of training data for
visual inspection systems. With this approach, synthetic yet realistic images
are generated that mimic real-world dicing defects. We employ three different
GAN variants for high-resolution image synthesis: Deep Convolutional GAN
(DCGAN), CycleGAN, and StyleGAN3. Our work-in-progress results demonstrate that
improved classification accuracies can be obtained, showing an average
improvement of up to 23.1 % from 65.1 % (baseline experiment) to 88.2 % (DCGAN
experiment) in balanced accuracy, which may enable yield optimization in
production.
