![](bg8.png)
of other sources of heterogeneous data from the wafer
fabrication line, such as text, in order to further re-
duce the annotation cost by partially automating the
process. Also, we are interested in developing novel
criteria for querying the most informative instances in
the dataset that will lead to more robust and accurate
predictive models.
ACKNOWLEDGEMENTS
This work has been supported by Pro
2
Future (FFG
under contract No. 854184). Pro
2
Future is funded
within the Austrian COMET Program -Competence
Centers for Excellent Technologies- under the aus-
pices of the Austrian Federal Ministry of Trans-
port, Innovation and Technology, the Austrian Fed-
eral Ministry for Digital and Economic Affairs and of
the Provinces of Upper Austria and Styria. COMET
is managed by the Austrian Research Promotion
Agency FFG. Tiago Santos was a recipient of a DOC
Fellowship of the Austrian Academy of Sciences at
the Institute of Interactive Systems and Data Sci-
ence of the Graz University of Technology. Michael
Wiedemann was with TDK Electronics, Austria and
now with RF360 Europe GmbH, Germany. Stefan
Thalmann is with the University of Graz and Graz
University of Technology, Graz, Austria.
REFERENCES
Cheon, S., Lee, H., Kim, C. O., and Lee, S. H. (2019).
Convolutional neural network for wafer surface de-
fect classification and the detection of unknown defect
class. IEEE Transactions on Semiconductor Manufac-
turing, 32(2):163–170.
Chou, P. B., Rao, A. R., Sturzenbecker, M. C., Wu, F. Y.,
and Brecher, V. H. (1997). Automatic defect classi-
fication for semiconductor manufacturing. Machine
Vision and Applications, 9(4):201–214.
Huang, S.-H. and Pan, Y.-C. (2015). Automated visual
inspection in the semiconductor industry: A survey.
Computers in industry, 66:1–10.
Hwa, R. (2004). Sample selection for statistical parsing.
Computational linguistics, 30(3):253–276.
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K.,
Dally, W. J., and Keutzer, K. (2016). Squeezenet:
Alexnet-level accuracy with 50x fewer parameters and
< 0.5 mb model size. arXiv preprint.
Ioffe, S. and Szegedy, C. (2015). Batch normalization: Ac-
celerating deep network training by reducing internal
covariate shift. arXiv preprint.
Ketchen, D. J. and Shook, C. L. (1996). The application of
cluster analysis in strategic management research: an
analysis and critique. Strategic management journal,
17(6):441–458.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Im-
agenet classification with deep convolutional neural
networks. In Advances in neural information process-
ing systems, pages 1097–1105.
Kyeong, K. and Kim, H. (2018). Classification of mixed-
type defect patterns in wafer bin maps using convolu-
tional neural networks. IEEE Transactions on Semi-
conductor Manufacturing.
Nakazawa, T. and Kulkarni, D. V. (2018). Wafer map defect
pattern classification and image retrieval using convo-
lutional neural network. IEEE Transactions on Semi-
conductor Manufacturing, 31(2):309–314.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V.,
Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P.,
Weiss, R., Dubourg, V., et al. (2011). Scikit-learn:
Machine learning in python. the Journal of machine
Learning research, 12:2825–2830.
Ramachandran, P., Zoph, B., and Le, Q. V. (2017).
Searching for activation functions. arXiv preprint
arXiv:1710.05941.
Rawat, W. and Wang, Z. (2017). Deep convolutional neural
networks for image classification: A comprehensive
review. Neural computation, 29(9):2352–2449.
Scheffer, T., Decomain, C., and Wrobel, S. (2001). Active
hidden markov models for information extraction. In
International Symposium on Intelligent Data Analy-
sis, pages 309–318. Springer.
Schubert, E., Sander, J., Ester, M., Kriegel, H. P., and Xu,
X. (2017). Dbscan revisited, revisited: why and how
you should (still) use dbscan. ACM Transactions on
Database Systems (TODS), 42(3):1–21.
Settles, B. (2009). Active learning literature survey. Techni-
cal report, University of Wisconsin-Madison Depart-
ment of Computer Sciences.
Settles, B. and Craven, M. (2008). An analysis of active
learning strategies for sequence labeling tasks. In Pro-
ceedings of the 2008 Conference on Empirical Meth-
ods in Natural Language Processing, pages 1070–
1079.
Shim, J., Kang, S., and Cho, S. (2020). Active learn-
ing of convolutional neural network for cost-effective
wafer map pattern classification. IEEE Transactions
on Semiconductor Manufacturing, 33(2):258–266.
Wang, K., Zhang, D., Li, Y., Zhang, R., and Lin, L. (2016).
Cost-effective active learning for deep image classi-
fication. IEEE Transactions on Circuits and Systems
for Video Technology, 27(12):2591–2600.
KDIR 2020 - 12th International Conference on Knowledge Discovery and Information Retrieval
276