Development of a new analysis algorithm based on the wavelet transforms for noisy Langmuir-probe traces

Summary form only given, as follows. Analysis algorithm of an electrical Langmuir probe data has been developed. Most algorithms requires the user specified input values for selecting the ion saturation, electron saturation, and intermediate regions to find the ion, electron density, plasma potential, and plasma temperature, respectively. However, the input values may cause a lack of reproducibility to analyze the probe data. When a noise is included into the probe signal, it is almost impossible to obtain the reliable plasma parameters from the probe data. In this work, a new algorithm is developed with the wavelet transforms to filter out the nose signal from the probe data and to determine the plasma parameters. A biorthogonal wavelet transform applies to determine the initial plasma potential and Daubechies wavelet is to obtain the electron and ion saturation currents. Electron energy distribution function (EEDF) or electron energy probability function (EEPF) can obtain clearly from the processed raw data by a biorthogonal wavelet transform. This algorithm is not necessary to require the user´s input values to analysis, thus it can be reduced an uncertainty from data analysis. With artificial data set including a random noise, the performance of this programs is evaluated and the result shows more than 95% accuracy.