Advanced laser processing and its optimization with machine learning

A. Michalowski; A. Ilin; A. Kroschel; S. Karg; P. Stritt; A. Dais; S. Becker; G. Kunz; S. Sonntag; M. Lustfeld; P. Tighineanu; V. Onuseit; M. Haas; T. Graf; H. Ridderbusch

doi:10.1117/12.2653604

17 March 2023 Advanced laser processing and its optimization with machine learning

A. Michalowski, A. Ilin, A. Kroschel, S. Karg, P. Stritt, A. Dais, S. Becker, G. Kunz, S. Sonntag, M. Lustfeld, P. Tighineanu, V. Onuseit, M. Haas, T. Graf, H. Ridderbusch

Author Affiliations +

Proceedings Volume 12408, Laser Applications in Microelectronic and Optoelectronic Manufacturing (LAMOM) XXVIII; 1240803 (2023) https://doi.org/10.1117/12.2653604
Event: SPIE LASE, 2023, San Francisco, California, United States

Abstract

The flexibility of new laser sources and process-monitoring enables new possibilities in laser-based production technology, for instance the combination of different laser processes with many adjustable parameters. The fusion of domain knowledge and probabilistic models in the form of hybrid models allows an efficient optimization of these processes with machine learning. This can be a key technology to realize self-learning laser-based universal machines in the future. The article discusses some examples where algorithm-based optimization, partly supported by hybrid models, can already greatly reduce the effort required to find suitable process parameters.

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A. Michalowski, A. Ilin, A. Kroschel, S. Karg, P. Stritt, A. Dais, S. Becker, G. Kunz, S. Sonntag, M. Lustfeld, P. Tighineanu, V. Onuseit, M. Haas, T. Graf, and H. Ridderbusch "Advanced laser processing and its optimization with machine learning", Proc. SPIE 12408, Laser Applications in Microelectronic and Optoelectronic Manufacturing (LAMOM) XXVIII, 1240803 (17 March 2023); https://doi.org/10.1117/12.2653604

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