WaferCap: Open Classification of Wafer Map Patterns using Deep Capsule Network

In integrated circuit design, analysis of wafer map patterns is critical to enhance yield and detect manufacturing issues. With the emergence of novel wafer map patterns, there is increasing need for robust artificial intelligence models that can both accurately classify seen patterns and while also detecting ones not seen during training, a capability known as open world classification. We develop a novel solution to this problem: WaferCap, a Deep Capsule Network designed for wafer map pattern classification and equipped with a rejection mechanism. When evaluated using the WM-811k dataset, WaferCap significantly surpasses existing methods, achieving 99% accuracy for fully seen patterns while demonstrating robust performance in open-world settings by effectively detecting unseen wafer map patterns.