Complexity visualization, dataset acquisition, and machine-learning perspectives for low-temperature plasma: a review

Abstract

Abstract Low-temperature plasma plays various roles in industrial material processing as well as provides a number of scientific targets, both from theoretical and experimental points of view. Such rich features in variety are based on its complexities, arising from diverse parameters in constituent gas-phase species, working gas pressure, input energy density, and spatial boundaries. When we consider causalities in these complexities, direct application of machine-learning methods is not always possible since levels of complexities are so high in comparison with other scientific research targets. To overcome this difficulty, progresses in plasma diagnostics and data acquisition systems are inevitable, and the handling of a large number of data elements is one of the key issues for this purpose. In this topical review, we summarize previous and current achievements of visualization, acquisition, and analysis methods for complex plasma datasets which may open a scientific and technological category mixed with rapid machine-learning advancements and their relevant outcomes. Although these research trends are ongoing, many reports published so far have already convinced us of various expanding aspects of low-temperature plasma leading to the potential for scientific progress as well as developments of intellectual design in industrial plasma processes.