A learning-control system for advanced atomic-force-microscopy scanning mode
适用于原子力显微镜先进扫描模式的学习控制系统

Atomic-force-microscopy(AFM) is an important instrument for nanoscale measurement and manipulation. This paper proposes a learning-control advanced scanning mode for an AFM system. Specifically, a learning-control scheme is designed for the AFM system, which consists of an optimal inverse compensator for the AFM scanner dynamics and a learning algorithm dealing with the surface profile of the detected sample. Based on the observation of the offset among neighboring scanning lines, the aforementioned learning-control scheme combined with a conventional proportional-integral(PI) controller realizes the advanced AFM scanning mode. The designed scanning mode is then utilized for periodic samples to test its performance. As demonstrated by simulation and experimental results, this scanning mode can greatly increase the measurement speed and precision, and simultaneously keep the distance between the cantilever tip and the detected sample within a reasonable range to avoid possible harm to them. Therefore, the proposed advanced scanning mode can be employed for online inspection of fast biologic processes, and it can also be utilized to implement such nanomanipulation as repetitive writing.