Similarity-based scaling networks for capacitive radio frequency discharge plasmas
Source abstract
Abstract
We demonstrate similarity-based scaling networks for capacitive radio frequency (RF) plasmas, which extensively correlate discharge characteristics under varied conditions, incorporating the transition from original to similarity states. Based on fully kinetic particle-in-cell simulations, similar RF discharges in argon are demonstrated with three external control parameters (gas pressure, gap distance, and driving frequency) simultaneously tuned. A complete set of scaling pathways regarding fundamental discharge parameters is obtained, from which each plasma state finds its neighboring node with only one control parameter tuned. The results from this study provide a promising strategy for plasma multi-parameter mapping, enabling effective cross-comparisons, prediction, and manipulation of RF discharge plasmas.
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Research context
OpenAlex topics: Plasma Diagnostics and Applications · Electrostatic Discharge in Electronics · Electrohydrodynamics and Fluid Dynamics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Chemical Engineering, Tsinghua University 1 , Beijing 100084, China
Department of Electrical Engineering, Tsinghua University 2 , Beijing 100084, China
School of Physics, Beijing Institute of Technology 3 , Beijing 100081, China
Department of Electrical and Computer Engineering, Michigan State University 4 , East Lansing, Michigan 48824, USA
Department of Computational Mathematics, Science and Engineering, Michigan State University 5 , East Lansing, Michigan 48824, USA
StateKey Laboratory of Power System Operation and Control, Department of Electrical Engineering, Tsinghua University 6 , Beijing 100084, China
Funding
National Natural Science Foundation of China · Air Force Office of Scientific Research · U.S. Department of Energy · State Key Laboratory of Power System Operation and Control
References (49)
Show source-provided references
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