Autonomous hybrid optimization of a SiO2 plasma etching mechanism

Krüger, Florian; Zhang, Du; Luan, Pingshan; Park, Minjoon; Metz, Andrew; Kushner, Mark J.

doi:10.1116/6.0003554

Title: Autonomous hybrid optimization of a SiO₂ plasma etching mechanism

Journal Article · Fri Jun 28 00:00:00 UTC 2024 · Journal of Vacuum Science and Technology A

DOI: https://doi.org/10.1116/6.0003554 · OSTI ID:2572026

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University of Michigan, Ann Arbor, MI (United States)
TEL Technology Center, America, LLC, Albany, NY (United States)

Computational modeling of plasma etching processes at the feature scale relevant to the fabrication of nanometer semiconductor devices is critically dependent on the reaction mechanism representing the physical processes occurring between plasma produced reactant fluxes and the surface, reaction probabilities, yields, rate coefficients, and threshold energies that characterize these processes. The increasing complexity of the structures being fabricated, new materials, and novel gas mixtures increase the complexity of the reaction mechanism used in feature scale models and increase the difficulty in developing the fundamental data required for the mechanism. This challenge is further exacerbated by the fact that acquiring these fundamental data through more complex computational models or experiments is often limited by cost, technical complexity, or inadequate models. In this paper, we discuss a method to automate the selection of fundamental data in a reduced reaction mechanism for feature scale plasma etching of SiO₂ using a fluorocarbon gas mixture by matching predictions of etch profiles to experimental data using a gradient descent (GD)/Nelder–Mead (NM) method hybrid optimization scheme. These methods produce a reaction mechanism that replicates the experimental training data as well as experimental data using related but different etch processes.

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Research Organization:: University of Michigan, Ann Arbor, MI (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Fusion Energy Sciences (FES)

Grant/Contract Number:: SC0020232

OSTI ID:: 2572026

Journal Information:: Journal of Vacuum Science and Technology A, Journal Name: Journal of Vacuum Science and Technology A Journal Issue: 4 Vol. 42; ISSN 0734-2101; ISSN 1520-8559

Publisher:: American Vacuum Society / AIPCopyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
70 PLASMA PHYSICS AND FUSION TECHNOLOGY
computational models
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Title: Autonomous hybrid optimization of a SiO2 plasma etching mechanism

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Title: Autonomous hybrid optimization of a SiO₂ plasma etching mechanism