Bayesian machine learning for efficient minimization of defects in ALD 
passivation layers

Dogan, G.; Demir, S. O.; Gutzler, R.; Gruhn, H.; Dayan, C. B.; Sanli, U. T.; Silber, C.; Culha, U.; Sitti, M.; Schütz, G.; Grévent, C.; Keskinbora, K.

doi:10.1021/acsami.1c14586

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Bayesian machine learning for efficient minimization of defects in ALD passivation layers

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Dogan, G.
Dept. Modern Magnetic Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;
Robert Bosch GmbH, Automotive Electronics;

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Demir, S. O.
Dept. Physical Intelligence, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Dayan, C. B.
Dept. Physical Intelligence, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Sanli, U. T.
Dept. Modern Magnetic Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Culha, U.
Dept. Physical Intelligence, Max Planck Institute for Intelligent Systems, Max Planck Society;
Present Address: Munich School of Robotics and Machine Intelligence, Technical University of Munich;

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Sitti, M.
Dept. Physical Intelligence, Max Planck Institute for Intelligent Systems, Max Planck Society;
Dept. of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA;

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Schütz, G.
Dept. Modern Magnetic Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Keskinbora, K.
Dept. Modern Magnetic Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;
Present Address: Massachusetts Institute of Technology;

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Citation

Dogan, G., Demir, S. O., Gutzler, R., Gruhn, H., Dayan, C. B., Sanli, U. T., et al. (2021). Bayesian machine learning for efficient minimization of defects in ALD passivation layers. ACS Applied Materials and Interfaces, 13(45), 54503-54515. doi:10.1021/acsami.1c14586.

Cite as: https://hdl.handle.net/21.11116/0000-0009-9AFE-C

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