How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic-precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft-surface materials. In this paper, we focus on the potential for supporting SPM-based measurements, with an emphasis on the application of AI-based algorithms, especially Machine Learning-based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure-property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI-based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI-QC-based approach were also discussed. Finally, we outline a research path for improving AI-QC-powered SPM. RESEARCH HIGHLIGHTS: Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai-qc-powered scanning probe microscopy.

Formation and Characterization of Hole Nanopattern on Photoresist Layer by Scanning Near-Field Optical Microscope.

Patterning of lines of holes on a layer of positive photoresist SX AR-P 3500/6 (Allresist GmbH, Strausberg, Germany) spin-coated on a quartz substrate is carried out by using scanning near-field optical lithography. A green 532 nm-wavelength laser, focused on a backside of a nanoprobe of 90 nm diameter, is used as a light source. As a result, after optimization of parameters like laser power, exposure time, or sleep time, it is confirmed that it is possible to obtain a uniform nanopattern structure in the photoresist layer. In addition, the lines of holes are characterized by a uniform depth (71-87 nm) and relatively high aspect ratio ranging from 0.22 to 0.26. Numerical modelling performed with a rigorous method shows that such a structure can be potentially used as a phase zone plate.