A new algorithm to analyze the digitized Langmuir probe (LP) data is developed with the wavelet transforms, providing the electron energy distribution function (EEDF) for bi-Maxwellian plasmas. Because most of algorithms to analyze the probe data have been developed with the Druyvesteyn formula which is based on the second derivation of current with respect to probe voltage, the accuracy of analysis is very sensitive to the noise level during the derivation of probe data. Especially the amount of hot electrons in the bi-Maxwellian plasma is small enough to compare the noise level and the noise filtering method is a kernel in the development of the algorithm to analyze the bi-Maxwellian EEDF. Here, the bi-orthogonal wavelet and continuous wavelet transforms are chosen for the de-noising and the differentiation processes respectively. It has an advantage to provide the filtered data with minimum loss of important information. The artificial LP data sets composed of electrons were generated with various bulk and hot temperatures and the developed algorithm was evaluated for the various white noise levels. For the case that the noise levels are 10 times of ion saturation current, the plasma parameters such as the population of hot electrons and the temperatures of hot and bulk electron were accurately analyzed within only a few percent deviations from the input values for the generation of artificial probe data. As the demonstration of the algorithm, the analysis of probe data was carried out for the DC glow and the inductively coupled plasmas (ICP) with various input powers and operating pressures, respectively. From the EEDF analyses of those plasmas, the variation of hot electrons in the DC plasma and the Ramsauer effect of low pressure ICP could be observed clearly.

 10.3938/jkps.55.1825