Helium line emission spectroscopy to measure plasma parameters using modeling and machine learning in low-temperature plasmas

Abstract

Abstract Line intensity ratios (LIRs) of helium (He) atoms are known to depend on electron density, n e , and temperature, T e , and thus are widely utilized to evaluate these parameters, known as the H e   I LIR method. In this conventional method, the measured LIRs are compared with theoretical values calculated using a collisional-radiative (CR) model to find the best possible n e and T e . Basic CR models have been improved to take into account several effects. For instance, radiation trapping can occur to a significant degree in weakly ionized plasmas, leading to major alterations of LIRs. This effect has been included with optical escape factors in CR models. A new approach to the evaluation of n e and T e from He I LIRs has recently been explored using machine learning (ML). In the ML-aided LIR method, a predictive model is developed with training data, which consists of an input (measured LIRs) and a desired/known output (measured n e or T e from other diagnostics). It has been demonstrated that this new method predicts n e and T e better than using the conventional method coupled with a CR model, not only for He but also for other species. This review focuses mainly on low-temperature plasmas with T e ⩽ 10 eV in linear plasma devices.