High-speed atomic force microscopy through local raster scanning

• Main Author: Chang, Peter I.
• Language: en_US
• Published: Boston University 2018
• Online Access: https://hdl.handle.net/2144/31522

Thesis (Ph.D.)--Boston University === PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at [e-mail]. Thank you. === Recent advances in high-speed atomic force microscopy (AFM), including new actuator design, advanced controller architecture and overall system optimization have made possible near video-rate imaging of biological samples. Despite this grand achievement, current state-of-the-art high-speed AFM systems still have their limitations, including a small image size and a limited choice of imaging modes. In addition, there are many interesting and important bio-related systems that have dynamics whose time scale is significantly faster than video rate. Thus there is a need to push imaging rates even higher for high-speed AFM systems. In this thesis, we discuss a novel tracking and imaging scheme known as the local raster scan (LRS) method, that can yield an order-of-magnitude improvement or more when imaging a particular class of samples. Local raster scanning utilizes data measured by the AFM system in real-time and steers the tip of the AFM to remain near the sample of interest at all times. LRS is designed for imaging biopolymers or other string-like samples and reduces the overall imaging time, not by increasing the speed of the instrument but by reducing the total sampling area. LRS is achieved by closing a high-level feedback loop around the AFM system. This thesis develops the LRS algorithm, beginning from design of the tip trajectory and moving through the detection schemes that measure the position of the sample in the scan. In addition, this thesis also provide an analysis of the errors of LRS that arise from the noisy measurements of AFM systems and discusses their influence on the performance of LRS. This framework provides a selection guide for choosing LRS parameters so as to maximize the tracking ability of the method. Both simulation and experimental results are included to demonstrate how the LRS performs in practice. These experiments are performed using a combined commercial AFM system together with a modified two-axis nano-positioning stage. The results illustrate improvements of up to an order of magnitude reduction in scan time, compared to the traditional raster scanning scheme with the same resolution and equivalent line rate. === 2031-01-01