A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks. - Tobias Schlosser; Frederik Beuth; Michael Friedrich 005; Danny Kowerko (ETFA, 2019)

Hexagonal Image Processing in the Context of Machine Learning: Conception of a Biologically Inspired Hexagonal Deep Learning Framework. - Tobias Schlosser; Michael Friedrich 005; Danny Kowerko (ICMLA, 2019)

Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning. - Frederik Beuth; Tobias Schlosser; Michael Friedrich 005; Danny Kowerko (IECON, 2020)

Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks. - Tobias Schlosser; Michael Friedrich 005; Frederik Beuth; Danny Kowerko (J. Intell. Manuf., 2022)

Utilizing Generative Adversarial Networks for Image Data Augmentation and Classification of Semiconductor Wafer Dicing Induced Defects. - Zhining Hu; Tobias Schlosser; Michael Friedrich 005; André Luiz Buarque Vieira e Silva; Frederik Beuth; Danny Kowerko (ETFA, 2024)

Hexagonal Image Processing for Computer Vision With Hexnet: A Hexagonal Image Processing Data Set and Generator. - Tobias Schlosser; Michael Friedrich 005; Trixy Meyer; Maximilian Eibl; Danny Kowerko (IEEE Access, 2024)