A New Kind of Atomic Force Microscopy Scan Control Enabled by Artificial Intelligence: Concept for Achieving Tip and Sample Safety Through Asymmetric Control.

This paper introduces a paradigm shift in atomic force microscope (AFM) scan control, leveraging an artificial intelligence (AI)-based controller. In contrast to conventional control methods, which either show a limited performance, such as proportional integral differential (PID) control, or which purely focus on mathematical optimality as classical optimal control approaches, our proposed AI approach redefines the objective of control for achieving practical optimality. This presented AI controller minimizes the root-mean-square control deviations in routine scans by a factor of about 4 compared to PID control in the presented setup and also showcases a distinctive asymmetric response in complex situations, prioritizing the safety of the AFM tip and sample instead of the lowest possible control deviations. The development and testing of the AI control concept are performed on simulated AFM scans, demonstrating its huge potential. Highlights: Artificial intelligence-based asymmetric controller for the highest tip and sample safety with AFM scans. Potential to significantly outperform proportional integral derivative control in classical scan situations. Dynamic change between classically optimal and asymmetric behavior depending on the scan situation. [ABSTRACT FROM AUTHOR]