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

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.