TY - GEN
T1 - Laser-based Hair Crack Detection on Wafers
AU - Fuchs, Alexander
AU - Priewald, Robin
AU - Pernkopf, Franz
PY - 2020/8
Y1 - 2020/8
N2 - The detection of hair cracks is one of the key challenges to improve wafer-processing stability. Contrary to other defects on the wafer-edge, hair cracks have a very small geometric footprint, making them hard to detect for measurement systems. This raises the demand for a powerful data analysis tool, which can extract the relevant information even in low signal-To-noise ratio scenarios. In this paper, we investigate an approach for hair crack detection using a laser-based wafer edge inspection device and deep neural networks to analyze and classify the measured data. We propose different pre-processing methods for the raw measurement data, to improve the learning behavior of the networks. The results show that a substantial improvement, in both detection rate and false positive rate, can be achieved by appropriate pre-processing of the measured data.
AB - The detection of hair cracks is one of the key challenges to improve wafer-processing stability. Contrary to other defects on the wafer-edge, hair cracks have a very small geometric footprint, making them hard to detect for measurement systems. This raises the demand for a powerful data analysis tool, which can extract the relevant information even in low signal-To-noise ratio scenarios. In this paper, we investigate an approach for hair crack detection using a laser-based wafer edge inspection device and deep neural networks to analyze and classify the measured data. We propose different pre-processing methods for the raw measurement data, to improve the learning behavior of the networks. The results show that a substantial improvement, in both detection rate and false positive rate, can be achieved by appropriate pre-processing of the measured data.
UR - http://www.scopus.com/inward/record.url?scp=85091397903&partnerID=8YFLogxK
U2 - 10.1109/ASMC49169.2020.9185278
DO - 10.1109/ASMC49169.2020.9185278
M3 - Conference paper
T3 - ASMC (Advanced Semiconductor Manufacturing Conference) Proceedings
BT - 2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference, ASMC 2020
T2 - 2020 31st Annual SEMI Advanced Semiconductor Manufacturing Conference
Y2 - 24 August 2019 through 26 August 2019
ER -