Understanding wafer patterns in semiconductor production with variational auto-encoders.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Original languageEnglish
Title of host publicationProc. 26th European Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Publication statusPublished - 2018

Cite this

Teixeira dos Santos, T., & Kern, R. (2018). Understanding wafer patterns in semiconductor production with variational auto-encoders. In Proc. 26th European Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

Understanding wafer patterns in semiconductor production with variational auto-encoders. / Teixeira dos Santos, Tiago; Kern, Roman.

Proc. 26th European Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Teixeira dos Santos, T & Kern, R 2018, Understanding wafer patterns in semiconductor production with variational auto-encoders. in Proc. 26th European Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN).
Teixeira dos Santos T, Kern R. Understanding wafer patterns in semiconductor production with variational auto-encoders. In Proc. 26th European Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 2018
Teixeira dos Santos, Tiago ; Kern, Roman. / Understanding wafer patterns in semiconductor production with variational auto-encoders. Proc. 26th European Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 2018.
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title = "Understanding wafer patterns in semiconductor production with variational auto-encoders.",
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year = "2018",
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booktitle = "Proc. 26th European Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)",

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M3 - Conference contribution

BT - Proc. 26th European Symp. on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

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