A semianalytic radio frequency sheath model integrated into a two-dimensional hybrid model for plasma processing reactors

Abstract We demonstrate that a passive non-contact diagnostic technique, radio emission spectroscopy (RES), provides a sensitive monitor of currents in a low pressure radio frequency (RF) plasma. A near field magnetic loop antenna was used to capture RF emissions from the plasma without perturbing it. The analysis was implemented for a capacitively coupled RF plasma with an RF supply at a frequency of 13.56 MHz. Real-time measurements are captured in scenarios relevant to contemporary challenges faced during semiconductor fabrication (e.g. window coating and wall disturbance). Exploration of the technique for key equipment parameters including applied RF power, chamber pressure, RF bias frequencies and chamber wall cleanliness shows sensitive and repeatable function. In particular, the induced RES signal was found to vary sensitively to pressure changes and we were able to detect pressure and power variations as low as ~2.5 %/mtorr and ~3.5 %/watt, respectively, during the plasma processing during a trial generic plasma process. Finally, we explored the ability of RES to monitor the operation of a multiple frequency low-pressure RF plasma system (f1 = 2 MHz, f2 = 162 MHz) and intermixing products which suggests strongly that the plasma sheaths are the primary source of this non-linear diode mixing effect.

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Hydrogen/oxygen radio-frequency plasma processing of LiNbO3

10.1117/12.246800 ◽

1996 ◽

Cited By ~ 1

Author(s):

Hana Turcicova ◽

Jiri Vacik ◽

Jarmila Cervena ◽

Vladimir Zelezny

Keyword(s):

Radio Frequency ◽

Plasma Processing ◽

Radio Frequency Plasma ◽

Frequency Plasma

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Two-dimensional fluid modelling of charged particle transport in radio-frequency capacitively coupled discharges

Plasma Sources Science and Technology ◽

10.1088/0963-0252/11/4/312 ◽

2002 ◽

Vol 11 (4) ◽

pp. 448-465 ◽

Cited By ~ 71

Author(s):

A Salabas ◽

G Gousset ◽

L L Alves

Keyword(s):

Charged Particle ◽

Radio Frequency ◽

Particle Transport ◽

Two Dimensional ◽

Capacitively Coupled ◽

Charged Particle Transport

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Continuously tracking of moving object by a combination of ultra-high frequency radio-frequency identification and laser range finder

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719860990 ◽

2019 ◽

Vol 15 (7) ◽

pp. 155014771986099 ◽

Cited By ~ 1

Author(s):

Yulu Fu ◽

Ran Liu ◽

Hua Zhang ◽

Gaoli Liang ◽

Shafiq ur Rehman ◽

...

Keyword(s):

Radio Frequency ◽

Radio Frequency Identification ◽

Spatial Clustering ◽

Laser Ranging ◽

Laser Range Finder ◽

Two Dimensional ◽

Range Finder ◽

Laser Range ◽

Frequency Identification ◽

Velocity Matching

Due to the unique and contactless way of identification, radio-frequency identification is becoming an emerging technology for objects tracking. As radio-frequency identification does not provide any distance or bearing information, positioning using radio-frequency identification sensor itself is challenging. Two-dimensional laser range finders can provide the distance to the objects but require complicated recognition algorithms to acquire the identity of object. This article proposes an innovative method to track the locations of dynamic objects by combining radio-frequency identification and laser ranging information. We first segment the laser ranging data into clusters using density-based spatial clustering of applications with noise (DBSCAN). Velocity matching–based approach is used to track the location of object when the object is in the radio-frequency identification reading range. Since the radio-frequency identification reading range is smaller than a two-dimensional laser range finder, velocity matching–based approach fails to track location of the object when the radio-frequency identification reading is not available. In this case, our approach uses the clustering results from density-based spatial clustering of applications with noise to continuously track the moving object. Finally, we verified our approach on a Scitos robot in an indoor environment, and our results show that the proposed approach reaches a positioning accuracy of 0.43 m, which is an improvement of 67.6% and 84.1% as compared to laser-based and velocity matching–based approaches, respectively.

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