原子力显微镜(AFM)是进行纳米测量和操作的一种重要工具.针对原子力显微镜系统,论文提出了一种基于学习控制的先进扫描模式.具体而言,首先构造了一种适用于AFM的学习控制系统,它由对于扫描管动态特性的最优逆补偿环节和对于样品表面特性的学习算法两部分组成.然后,针对测量过程中扫描线之间的偏移,通过将常见的比例-积分控制算法与这种学习控制相结合,实现了一种基于学习算法的先进扫描模式.论文将这种模式应用于周期性样品来测试其性能,仿真和实验结果表明它可以显著提高测量的速度和精度,同时将样品与探针针尖的距离控制在一个合理的范围之内,以避免损坏样品或探针.这种先进扫描模式可以应用于对快速生物过程的实时监测,同时也可以用来完成重复刻写等纳米操作.
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.