Data Processing Techniques for Ion and Electron Energy Distribution Functions

TL;DR

This paper reviews techniques for processing ion and electron energy distribution data from plasma diagnostics, focusing on filtering and differentiation methods to handle noisy signals and improve data accuracy.

Contribution

It provides a comprehensive comparison of analog and numerical filtering techniques applied to experimental plasma data, highlighting their effects on energy distribution functions.

Findings

Different filtering methods impact data accuracy and resolution

Trade-offs exist between signal distortion and noise reduction

Guidelines for selecting appropriate techniques for plasma diagnostics

Abstract

Retarding field energy analyzers and Langmuir probes are routinely used to obtain ion and electron energy distribution functions (IEDF, EEDF). These typically require knowledge of the first and second derivatives of the I-V characteristics, both of which can be obtained in various ways. This poses challenges inherent to differentiating noisy signals, a frequent problem with electric-probe plasma diagnostics. A brief review of commonly used analog and numerical filtering and differentiation techniques is presented, together with their application on experimental data collected in a radio-frequency plasma. The application of each method is detailed with regards to the obtained IEDF and EEDF, the deduced plasma parameters, dynamic range, energy resolution and signal distortion.