In this work, an optimization algorithm was proposed for plasma diagnostic modeling based on a statistical analysis of reduced population density distribution. The algorithm generates a diagnostic equation, whose input parameters are the radiant flux of the multi-optical emission lines, and output parameters are electron temperature T e, electron density N e, and electron energy distribution function (EEDF), based on the dependence of reduced population density onT e, N e, and EEDF. The argon collisional-radiative model and the generalized EEDF were applied for the analysis of the excitation-kinetics. In this study, the diagnostic error for the low-pressure inductively coupled plasma, the low-pressure microwave discharge surface wave plasma, and the atmospheric dielectric barrier discharge plasma were simulated. The simulated diagnostic errors were smaller than those of the previously reported model.

Previously, we reported an optimization algorithm based on a plasma diagnostic model for optical emission spectroscopic (OES) measurements. In this study, we improved the algorithm. In the improved algorithm, the dependence of unimodality of reduced population density on electron temperature, electron density, and electron energy distribution function (EEDF) was discussed. Furthermore, electron temperature, electron density, and EEDF of 10 Pa argon inductively coupled plasma were diagnosed to experimentally evaluate the proposed algorithm. The diagnosis results were compared with those of the line-pair method and excitation temperature conversion method. The reduced population density distribution, which was calculated under the condition of the result of the proposed method, was in good agreement with the experimental result by OES measurement, thereby demonstrating the reliability of our proposed method.

In the field of photometry, an inverse square law is often used in which the illuminance value is inversely proportional to the square of the photometric distance. It is well known that this is a rule that assumes that the light source is a point light source. In this research, it is shown by model simulation that the inverse square law cannot be applied with high accuracy depending on the distance and the size of the light-receiving area even in the case of a point light source. Also, when checked experimentally, the experimental results agree well with the simulation.

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