The atomic force microscope (AFM) is one of the most versatile tools in nanotechnology. For control engineers this instrument is particularly interesting, since its ability to image the surface of a sample is entirely dependent upon the use of a feedback loop. This paper will present a tutorial on the control of AFMs. We take the reader on a walk around the control loop and discuss each of the individual technology components. The major imaging modes are described from a controls perspective and recent advances geared at increasing the performance of these microscopes are highlighted.

We present an algebraic solution to the problem of localizing a single fluorescent particle with sub-diffraction-limit accuracy. The algorithm is derived and its performance studied experimentally. Isolated 20 nm fluorescent beads were imaged using a wide-field microscope at two different positions separated by 100 nm and at a range of signal-to-noise ratios (SNR). The data were analyzed using both the new algorithm and the standard approach of fitting the data to a Gaussian profile. Results indicate that the proposed approach is nearly as accurate as Gaussian fitting across a wide range of SNR while executing over 200 times faster. In addition, the new algorithm is able to localize at lower SNR than the fitting method.

We consider the problem of controlling the movement of multiple cooperating agents so as to minimize an uncertainty metric associated with a finite number of data sources. In a one-dimensional (1-D) mission space, we adopt an optimal control framework and show that the solution can be reduced to a simpler parametric optimization problem: Determining a sequence of locations where each agent may dwell for a finite amount of time and then switch direction. This amounts to a hybrid system which we analyze using the infinitesimal perturbation analysis (IPA) to obtain a complete online solution through an event-driven gradient-based algorithm which is also robust with respect to the uncertainty model used. The resulting controller depends on observing the events required to excite the gradient-based algorithm, which cannot be guaranteed. We solve this problem by introducing a new metric for the objective function which creates a potential field guaranteeing that gradient values are nonzero. This approach is compared to an alternative graph-based target-visit scheduling and dwell times optimization algorithm. The simulation examples are included to demonstrate the proposed methods.

A high-level feedback control approach for rapid imaging in atomic force microscopy is presented. The algorithms are designed for samples which are string-like, such as biopolymers, and for boundaries. Rather than the simple raster-scan pattern, data from the microscope are used in real-time to steer the tip along the sample, drastically reducing the area to be imaged. An order-of-magnitude reduction in the time to acquire an image is possible. The technique is illustrated through simulations and through physical experiments.

This work examines the dosimetric performance of two algorithms creating a corrected CBCT (corrCBCT) and a virtual CT (vCT) implemented in a commercial treatment planning system. 60 patients distributed across all patient groups treated with curative intent at Vejle Hospital (breast, lung, prostate and anal/rectal cancer) were selected for the present study. Clinical treatment plans were recalculated on corrCBCT and vCT, as well as a reference CT (refCT) acquired as close in time to the CBCT image as possible. Recalculated doses were compared using gamma analysis, as well as by comparing D_98%, D_50%, and D_2% for all delineated targets and organs at risk. High dosimetric accuracy is demonstrated on both the corrCBCT and vCT. Gamma 2%/2mm pass rates >98% were found for all patients except two outliers still having >93% pass rates. Equivalence of all evaluated dose metrics within ±1Gy was observed for all patient groups, while the pelvic patients additionally showed equivalence for all metrics within ±1% of the refCT dose. For the thoracic patients, equivalence within ±2.5% was established for all metrics except median dose to the ipsilateral lung, calculated on corrCBCT for the breast patient group. The corrCBCT and vCT images are shown in excellent dosimetric agreement with refCT images, and show high potential for future use for streamlined adaptive radiotherapy workflows.

Non-raster scanning and undersampling of atomic force microscopy (AFM) images is a technique for improving imaging rate and reducing the amount of tip–sample interaction needed to produce an image. Generation of the final image can be done using a variety of image processing techniques based on interpolation or optimization. The choice of reconstruction method has a large impact on the quality of the recovered image and the proper choice depends on the sample under study. In this work we compare interpolation through the use of inpainting algorithms with reconstruction based on optimization through the use of the basis pursuit algorithm commonly used for signal recovery in compressive sensing. Using four different sampling patterns found in non-raster AFM, namely row subsampling, spiral scanning, Lissajous scanning, and random scanning, we subsample data from existing images and compare reconstruction performance against the original image. The results illustrate that inpainting generally produces superior results when the image contains primarily low frequency content while basis pursuit is better when the images have mixed, but sparse, frequency content. Using support vector machines, we then classify images based on their frequency content and sparsity and, from this classification, develop a fast decision strategy to select a reconstruction algorithm to be used on subsampled data. The performance of the classification and decision test are demonstrated on test AFM images.