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It is a long-term goal to transfer biological processing principles as wellas the power of human recognition into machine vision and engineering systems.One of such principles is visual attention, a smart human concept which focusesprocessing on a part of a scene. In this contribution, we utilize attention toimprove the automatic detection of defect patterns for wafers within the domainof semiconductor manufacturing. Previous works in the domain have oftenutilized classical machine learning approaches such as KNNs, SVMs, or MLPs,while a few have already used modern approaches like deep neural networks(DNNs). However, one problem in the domain is that the faults are often verysmall and have to be detected within a larger size of the chip or even thewafer. Therefore, small structures in the size of pixels have to be detected ina vast amount of image data. One interesting principle of the human brain forsolving this problem is visual attention. Hence, we employ here a biologicallyplausible model of visual attention for automatic visual inspection. We proposea hybrid system of visual attention and a deep neural network. As demonstrated,our system achieves among other decisive advantages an improvement in accuracyfrom 81% to 92%, and an increase in accuracy for detecting faults from 67% to88%. Hence, the error rates are reduced from 19% to 8%, and notably from 33% to12% for detecting a fault in a chip. These results show that attention cangreatly improve the performance of visual inspection systems. Furthermore, weconduct a broad evaluation, identifying specific advantages of the biologicalattention model in this application, and benchmarks standard deep learningapproaches as an alternative with and without attention.  This work is an extended arXiv version of the original conference articlepublished in "IECON 2020", which has been extended regarding visual attention.

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