Improving Automated Visual Fault Detection by Combining a Biologically Plausible Model of Visual Attention with Deep Learning

no code implementations • 13 Feb 2021 • Frederik Beuth, Tobias Schlosser, Michael Friedrich, Danny Kowerko

However, one problem in the domain is that the faults are often very small and have to be detected within a larger size of the chip or even the wafer.

Fault Detection

A Novel Visual Fault Detection and Classification System for Semiconductor Manufacturing Using Stacked Hybrid Convolutional Neural Networks

no code implementations • 25 Nov 2019 • Tobias Schlosser, Frederik Beuth, Michael Friedrich, Danny Kowerko

Consisting of stacked hybrid convolutional neural networks (SH-CNN) and inspired by current approaches of visual attention, the realized system draws the focus over the level of detail from its structures to more task-relevant areas of interest.

Fault Detection General Classification

Hexagonal Image Processing in the Context of Machine Learning: Conception of a Biologically Inspired Hexagonal Deep Learning Framework

no code implementations • 25 Nov 2019 • Tobias Schlosser, Michael Friedrich, Danny Kowerko

Inspired by the human visual perception system, hexagonal image processing in the context of machine learning deals with the development of image processing systems that combine the advantages of evolutionary motivated structures based on biological models.