Boundary effect on mode transformation in an electron cyclotron resonance etching reactor

Tamura, Hitoshi; Tetsuka, T.; Sekine, Toshimori; Shinohara, Shunjiro

doi:10.35848/1347-4065/ac56fb

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2023

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“…1 is used in this study. 22,23) Process gas is introduced from a shower plate which is set on the top of a chamber. Microwave power with a frequency of 2.45 GHz is introduced into the chamber.…”

Section: Etching Apparatusmentioning

confidence: 99%

Effect of initial-learning dataset on etching profile optimization using machine learning in plasma etching

Dobashi

Kobayashi

Okuyama

et al. 2023

Jpn. J. Appl. Phys.

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Machine learning was applied to optimize etching profile for line-and-space pattern sample in plasma etching. To investigate effect of difference of initial-learning dataset on optimization of etching profile, high-, medium-, and low-quality datasets were prepared. The high-quality dataset was composed of etching results relatively close to a target etching profile. The low-quality dataset was composed of etching results relatively far from the target etching profile. The medium-quality dataset was intermediate between the high- and low-quality dataset. For the machine learning, Kernel ridge regression method was used. After six learning cycles, better etching results were obtained by the medium- and low-quality datasets than to that in the whole initial-learning dataset. However, the etching results by the high-quality dataset did not exceed that in the whole initial-learning dataset. These results indicate that initial-learning dataset having etching results far from the target profile can be useful to optimize etching profile.

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Section: Etching Apparatusmentioning

confidence: 99%