Plasma heating characterization of the large area inductively coupled plasma etchers with the plasma information for managing the mass production

Author:

Park Seolhye1ORCID,Park Yoona1ORCID,Seong Jaegu1ORCID,Lee Haneul2ORCID,Bae Namjae2ORCID,Roh Ki-baek2,Seo Rabul1,Song Bongsub1,Kim Gon-Ho2ORCID

Affiliation:

1. Mobile Display Business, Samsung Display Co., Ltd 1 ., Asan-si, Chungcheongnam-do 31454, South Korea

2. Department of Nuclear Engineering, Seoul National University 2 , Seoul 08826, South Korea

Abstract

Meter-scale of the large area inductively coupled plasma etchers with the capacitive power coupling are widely applied for the mass production of OLED (organic light emitting diode) display panels. Because of the large area-to-volume ratio of the etcher, the balance between the power loss and absorption is easily located in the capacitive coupling mode rather than the ideal inductively coupled mode. Therefore, the process results are sensitively governed by the power absorption and plasma heating properties of the reactors. We have introduced a new PI (plasma information) parameter, the ratio of the stochastic heating to Ohmic heating of the plasmas, which is monitorable by using the optical emission spectroscopy data of the processing etchers. With the help of this plasma heating characteristic index, we could optimize the process recipes with the detailed control of the etched hole sidewall passivation and related species generation rate in the plasmas; thus, chamber-to-chamber matching in the huge mass production fab with the higher efficiency was possible. It was demonstrated that the introduced PI index with plasma heating mechanism characterization could be applicable to the VM (virtual metrology) modeling as one of the good information supplying core variables. This PI index has shown a very high correlation with the plasma sheath and ion flux governing phenomena for a large number of mass-produced OLED display glasses. From these results, the introduced plasma heating mechanism-based PI index is expected to be utilized as a good reference index for their performance analysis or PI-VM modelings.

Funder

the Brain Korea 21 FOUR Program

the National Research Council of Science & Technology (NST) grant by the Korea Government

Publisher

AIP Publishing

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