Near-field optics is a blooming field of physics with multiple techniques that contribute to help understand novel and complex optical phenomena. Among them, scattering-type scanning near-field optical microscopy (s-SNOM) is a well-established method, which was largely enabled by the advent of high-brightness tunable or broadband infrared sources. Thus, today, besides imaging, spectroscopy, and hyperspectral measurements are easily available at the nanoscale. Such instrumental capabilities and the commercialization of s-SNOM instruments made experiments on low-energy phenomena accessible to a large number of laboratories. However, an important outstanding issue is the lack of data analysis tools. Underlying phenomena are complex and require flexible, experiment-specific workflows, numerical modeling, or the use of artificial intelligence. While expert research groups develop specific, in-house processing codes, adapting, exchanging, and reproducing data analysis workflows even within those laboratories can be challenging. Therefore, the need for software that allows easy data processing and interpretation is clear. Here, we introduce the first open-source software suite based on visual programming that democratizes s-SNOM data analysis, unlocks the potential of machine learning for newcomers to the field, and empowers experts to develop and collaborate more efficiently, benefiting the whole near-field community.
Németh et al. (Wed,) studied this question.