This paper presents a data-driven dynamics-based filtering approach to eliminate acoustic noise-caused distortion in images of atomic force microscope (AFM). AFM operations are sensitive to external noise as disturbances including acoustic noise as disturbances to the probe-sample interaction directly results in distortions in AFM imaging. Although conventional passive noise cancellation has been employed, limitation exists and residual noise still persists. Advanced online control techniques face difficulty in capturing the complex noise dynamic and limited system bandwidth imposed by the robustness requirement. Therefore, In this work, we propose a finite-impulse-response (FIR) based post-filtering approach to remove the noise-caused distortion in AFM images. Experimental implementation is presented and discussed to illustrate the proposed technique.

In this study, we propose a novel plate-like sensor which utilizes curvature-based stiffening effects for enhanced nanometrology. In the proposed concept, the stiffness and natural frequencies of the sensor can be arbitrarily adjusted by applying a transverse curvature via piezoelectric actuators, thereby enabling resonance amplification over a broad range of frequencies. The concept is validated using a macroscale experiment. Then, a microscale finite element analysis is used to study the effect of applied curvature on the microplate static stiffness and natural frequencies. We show that imposed transverse curvature is an effective way to tune the in-situ static stiffness and natural frequencies of the plate sensor system. These findings will form the basis of future curvature-based stiffening microscale studies for novel scenarios in atomic force microscopy.

Despite its proven success in a wide variety of applications, the atomic force microscope (AFM) remains limited by its slow imaging rate. One approach to overcome this challenge is to rely on algorithmic approaches that reduce the imaging time not by scanning faster but by scanning less. Such schemes are particularly useful on older instruments as they can provide significant gains despite the existing (slow) hardware. At the same time, algorithms for sub-sampling can yield even greater improvements in imaging rate when combined with advanced scanners that can be retrofitted into the system. In this work, we focus on the use of a dual-stage piezoelectric scanner coupled with a particular scanning algorithm known as Local Circular Scan (LCS). LCS drives the tip of the AFM along a circular trajectory while using feedback to center that circle on a sample edge and to move the circle along the feature, thus reducing imaging time by concentrating the samples to the region of interest. Dual-stage systems are well-suited to LCS as the algorithm is naturally described in terms of a high-frequency, short range path (the scanning circle) and a slower, long range path (the track along the sample). However, control of the scanner is not straightforward as the system is multi-input, single-output. Here we establish controllability and observability of the scanning stage, allowing us to develop individual controllers for the long-range and short-range actuators through the principle of separation. We then use an internal model controller for the short range actuator to track a sinusoidal input (to generate the circular motion) and a state-space set-point tracking controller for the long range actuator. The results are demonstrated through simulation.

We report a new non-raster scan method based on a rosette pattern for high-speed atomic force microscopy (AFM). In this method, the lateral axes of the scanner are driven by the sum of two sinusoids with identical amplitudes and different frequencies. We formulate the problem so as to generate the rosette pattern and calculate scan parameters and resolution. To achieve high performance tracking, a controller is designed based on the internal model principle. The controller includes the dynamic modes of the reference signals and higher harmonics to cope with the system nonlinearities. We conduct an experiment employing the proposed method and a two degree of freedom microelectromechanical system nanopositioner to scan a circular-shaped area with a diameter of 6μm in 0.2 sec. The steady state tracking error is less than 4.48nm, i.e. only 9% of the selected resolution. AFM scanning is performed in contact mode constant height and high quality images are obtained.

In this paper, the problem of rapid probe engagement and withdrawal in atomic force microscopy (AFM) is addressed. Probe engagement to and withdrawal from the sample, respectively, are fundamental steps in all AFM operations, ranging from imaging to nanomanipulation. However, due to the highly nonlinear force-distance relation and the rapid transition between the attractive and the repulsive force dominance, a quick “snap-in” of the probe and excessively large repulsive force during the engagement, and a large adhesive force during the withdrawal are induced, resulting in sample deformation and damage, and measurement errors. Such adverse effects become more severe when the engagement and withdrawal is at high speeds, and the sample is soft (such as the live biological samples). Rapid engagement and withdrawal is needed to achieve high-speed AFM operations, particularly, to capture and interrogate dynamic evolutions of the sample. We propose a learning-based online optimization technique to minimize the probe-sample interaction force in high-speed engagement and withdrawal. Specifically, the desired force and probe position trajectory profile is online designed by using the optimal trajectory design technique, and tracked by using iterative learning control technique. Then the designed force-trajectory profile is online optimized to minimize the engagement force and the adhesive force. The proposed rapid engagement and withdrawal technique is illustrated through experimental implementation on a Polydimethylsiloxane (PDMS) sample.

In this paper, we propose a finite-impulse-response (FIR)-based feedforward control approach to mitigate the acoustic-caused probe vibration during atomic force microscope (AFM) imaging. Compensation for the extraneous probe vibration is needed to avoid the adverse effects of environmental disturbances such as acoustic noise on AFM imaging, nanomechanical characterization, and nanomanipulation. Particularly, residual noise still exists even though conventional passive noise cancellation apparatus has been employed. The proposed technique exploits a data-driven approach to capture both the noise propagation dynamics and the noise cancellation dynamics in the controller design, and is illustrated through the experimental implementation in AFM imaging application.

Intracellular network deformation of the cell plays an important role in cellular shape formation. Recent studies suggest that cell reshaping and deformation due to external forces involves cellular volume, pore size, elasticity, and intracellular filaments polymerization rate changes. This behavior of live cells can be described by poroelastic models because of the porous structure of the cytoplasm. In this study, the poroelasticity of human mammary basel/claudin low carcinoma cell (MDA-MB-231) was investigated using indentation-based atomic force microscopy. The effects of cell deformation (i.e., indentation) rate on the poroelasticity of MDA-MB-231 cells were studied. Specifically, the cell poroelastic behavior (i.e., the diffusion coefficient) was quantified at different indenting velocities (0.2, 2, 10, 20, 100, 200 μm/s) by fitting the force-relaxation curves using a poroelastic model. It was found that the in general the MDA-MB-231 cells behaved poroelastic, and they were less poroelastic (i.e., with lower diffusion coefficient) at higher indenting velocities due to the local stiffening up caused by faster force loads. Poor poroelastic relaxation was observed when the indenting velocity was lower than 10 μm/s due to the intracelluar fluid redistribution during the slow indenting process to equilibrate the intracellular pressure. Moreover, the measurement results showed that the pore size reduction caused by local stiffening at faster indenting velocities is more dominant than the Young’s modulus in affecting the cell poroelasticity.

Micro- and millimeter-scale resonant mass sensors have received widespread research attention due to their robust and highly-sensitive performance in a wide range of detection applications. A key performance metric associated with such systems is the sensitivity of the resonant frequency of a given device to changes in mass, which needs to be calibrated for different sensor designs. This calibration is complicated by the fact that the position of any added mass on a sensor can have an effect on the measured sensitivity, and thus a spatial sensitivity mapping is needed. To date, most approaches for experimental sensitivity characterization are based upon the controlled addition of small masses. These approaches include the direct attachment of microbeads via atomic force microscopy or the selective microelectrodeposition of material, both of which are time consuming and require specialized equipment. This work proposes a method of experimental spatial sensitivity measurement that uses an inkjet system and standard sensor readout methodology to map the spatially-dependent sensitivity of a resonant mass sensor — a significantly easier experimental approach. The methodology is described and demonstrated on a quartz resonator and used to inform practical sensor development.

Automated methods for deriving dynamic models from frequency response data of high-order dynamics systems are the default choice of most engineers. However, these methods themselves often require manual tuning of weighting parameters, a priori selection of system order, and even by hand removal of extraneous dynamics. On the other hand, manually matching complicated features in the Bode plot of the frequency response of high-order system is difficult with conventional first and second order numerators and denominators. In this papers we present a manual technique for systematically creating a dynamic model from Bode plots of frequency response data with complicated features. We apply the method to identifying dynamics of a piezoelectric stage holding the sample of an atomic force microscope (AFM). We show the manual method works better than the tfest command of Matlab ™ for this example system.

Atomic force microscopes use a probe to interface with matter at the nanoscale through a variety of imaging or manipulation methods. A dual-probe atomic force microscope (DP-AFM) has been proposed for simultaneous imaging and manipulation. One challenge of DP-AFM is probe-to-probe contact, which may occur intentionally such as when locating one probe with the other. This work studies the stability for such interactions where the 1 st probe is in the tapping mode (typically used for imaging) and 2 nd probe is in the contact mode (typically used for manipulation). A state dependent switched model is proposed for DP-AFM. A theorem is proposed for uniformly ultimately bounded (UUB) stability of switched systems under a sequence nonincreasing condition and applied to the DP-AFM problem.

Mechanical characterization of thin samples is now routine due to the prominence of the Atomic Force Microscope. Advances in amplitude modulation techniques have allowed for accurate measurement of a sample’s elastic properties by interpreting the changes in the vibration of a cantilevered beam in intermittent contact. However, the nonlinearities associated with contact complicate attempts to find an accurate time-history for the beam. Furthermore, the inclusion of viscous effects, common to soft samples, puts an explicit solution even farther from reach. A numerical method is proposed that analyzes the time-history and frequency response of a microcantilever beam with a viscoelastic end-condition. The mathematics can be simplified by incorporating the viscoelastic end-condition into the equation of motion directly by modeling it as a distributed load. A forcing function can then be derived from the Standard Linear Solid model of viscoelasticity and implemented in the non-conservative work term of Hamilton’s principle. The Galerkin method can separate the resulting nonlinear equation of motion into time and space components. Performing a numerical analysis of the time factor equation provide the beam’s response over time. The results demonstrate the distinctive effects of viscoelasticity and periodic contact on the beam’s motion and provide the framework for the determination of viscous properties using dynamic techniques.

This paper presents a new silicon-on-insulator-based MEMS nanopositioner that is designed for high-speed on-chip atomic force microscopy (AFM). The device features four electrostatic actuators in a 2-DOF configuration that allows bidirectional actuation of a central stage along two orthogonal axes with displacements greater than ±10 μ m . The x- and y-axis resonant modes of the stage are located at 1274Hz and 1286Hz , respectively. Integrated electrothermal sensors are used to control the system in closed loop, with a damping controller and an internal model controller being implemented for each axis. The performance of the closed-loop system is demonstrated by performing a 20 μ m×20 μ m contact-mode AFM scan via a Lissajous scan trajectory with a 410Hz sinusoidal reference.

An adaptive multi-loop mode (AMLM) imaging of atomic force microscope (AFM) is proposed. Due to its superior image quality and less sample disturbances, tapping mode (TM) imaging is currently the de facto most widely used imaging technique. However, the speed of TM-imaging is substantially limited, and becoming the major bottleneck of this technique. The proposed AMLM-imaging overcomes the limits of TM-imaging by utilizing control techniques to substantially increase the speed of TM-imaging while preserving the advantages of TM-imaging. The AMLM-imaging is tested and demonstrated through imaging a PtBA sample in experiments, and the experiment results demonstrated that the image quality over large-size imaging (50 μm by 25 μm) achieved at the scan rate of 25 Hz is at the same level of that when using TM-imaging at 1 Hz, while the probe-sample interaction force is smaller than that of the TM-imaging at 2.5 Hz.

The tapping mode (TM) is a popularly used imaging mode in atomic force microscopy (AFM). A feedback loop regulates the amplitude of the tapping cantilever by adjusting the offset between the probe and sample; the image is generated from the control action. This paper explores the role of the trajectory of the tapping cantilever in the accuracy of the acquired image. This paper demonstrates that reshaping the cantilever trajectory alters the amplitude response to changes in surface topography, effectively altering the mechanical sensitivity of the instrument. Trajectory dynamics are analyzed to determine the effect on mechanical sensitivity and analysis of the feedback loop is used to determine the effect on image accuracy. Experimental results validate the analysis, demonstrating better than 30% improvement in mechanical sensitivity using certain trajectories. Images obtained using these trajectories exhibit improved sharpness and surface tracking, especially at high scan speeds.

This article addresses output-boundary regulation for high-scan-frequency Atomic Force Microscope (AFM) imaging of soft samples. The main contribution of this article is to use the causal inverse for nonminimum phase systems to rapidly transition an output away from a specified boundary whenever the output approaches the boundary due to unknown disturbances. The proposed feedforward-based control technique overcomes both: (i) lack of preview information of the disturbances; and (ii) performance limitations of feedback-based control methods for non-minimum phase systems. Simulation results for an example AFM are presented to illustrate the approach.

We present the design and fabrication of a Micro-Electro-Mechanical Systems based piezoresistive cantilever force sensor as a potential candidate for micro/nano indentation of biological specimens such as cells and tissues. The fabricated force sensor consists of a silicon cantilever beam with a p-type piezoresistor and a cylindrical probing tip made from SU-8 polymer. One of the key features of the sensor is that a standard silicon wafer is used to make silicon-on-insulator (SOI), thereby reducing the cost of fabrication. To make SOI from standard silicon wafer the silicon film was sputtered on an oxidized silicon wafer and annealed at 1050 °C so as to obtain polycrystalline silicon. The sputtered silicon layer was used to fabricate the cantilever beam. The as-deposited and annealed silicon films were experimentally characterized using X-ray diffraction (XRD) and Atomic Force Microscopy (AFM). The annealed silicon film was polycrystalline with a low surface roughness of 3.134 nm (RMS value).

This paper exemplifies methods to estimate sample properties, including topographical properties, from a high bandwidth estimate of tip-sample interaction forces between the probe tip and the sample surface in an atomic force microscope. The tip-sample interaction force is the most fundamental quantity that can be detected by the probe tip. The fact that sample features as well as physical properties of the sample are a function of tip-sample interaction model chosen is exploited, and the property estimates are obtained by fitting appropriate physical models to the force estimate data. The underlying idea is to treat the non-linear tip-sample interactions as a disturbance to the cantilever subsystem and design a feedback controller that ensures the cantilever deflection tracks a desired trajectory. This tracking allows scanning speeds as high as 1/10 th of the cantilever resonance frequency compared to typical scanning modes that regulate derivatives of the probe deflection such as amplitude or phase, providing much lower scan speeds. The high bandwidth disturbance rejection and consequent estimation provides estimates of the tip-sample interaction force.

In this paper, we present significant improvements to a scanning probe microscope (SPM) modeling technique that uses the SPM’s probe-surface interaction signal to model the lateral dynamics of the SPM. The fundamental idea behind this modeling method is to use the topography signal resulting from a sinusoidal scan of a known calibration surface to develop a transfer function model of the AFM dynamics. This method is useful in situations where sensors are either unavailable, insufficient, or require independent calibration. The method is experimentally implemented to model a commercial atomic force microscope system (AFM).

An application and experimental verification of the online structure from motion (SFM) method is presented to estimate the position of a moving object using a moving camera. An unknown input observer is implemented for the position estimation of a moving object attached to a two-link robot observed by a moving camera attached to a PUMA robot. The velocity of the object is considered as an unknown input to the perspective dynamical system. Series of experiments are performed with different camera and object motions. The method is used to estimate the position of the static object as well as the moving object. The position estimates are compared with ground-truth data computed using forward kinematics of the PUMA and the two-link robot. The observer gain design problem is formulated as a convex optimization problem to obtain an optimal observer gain.

The mechanical properties of biomaterials have long been a subject of interest for researchers due to their potential in predicting biologically relevant questions, like proliferation of cancer in tissue. A popular technique of estimating material properties of biomaterials is the AFM, which consists of a probe that indents the material of interest. However, region localization for AFM indentation is challenging, especially when probing large sections of the tissue. Furthermore, identifying the point of contact between AFM tip and the specimen on the force-displacement curve involves uncertainties that are difficult to predict. In this work, we try to address these two issues. We use a vision-guided positioning system to achieve region localization, and we use a resistance based-electrical circuit to identify the point of contact between AFM tip and the specimen.