DeepWafer: A Generative Wafermap Model with Deep Adversarial Networks

A certain amount of process deviations characterizes semiconductor manufacturing processes. Automated detection of these production issues followed by an automated root cause analysis has the potential to increase the effectiveness of semiconductor production. Manufacturing defects exhibit typical patterns in measured wafer test data, e.g., rings, spots, repetitive textures, or scratches. Recognizing these patterns is an essential step for finding the root cause of production issues. This paper demonstrates that combining Information Maximizing Generative Adversarial Network (InfoGAN) and Wasserstein GAN (WGAN) with a new loss function is suitable for extracting the most characteristic features from extensive real-world sensory wafer test data, which in various aspects outperforms traditional unsupervised techniques. These features are then used in subsequent clustering tasks to group wafers into clusters according to their exhibit patterns. The primary outcome of this work is a statistical generative model for recognizing spatial wafermaps patterns using deep adversarial neural networks. We experimentally evaluate the performance of the proposed approach over a real dataset.