Scanning Probe Microscopy Images Research Articles

Fourier transform-based post-processing drift compensation and calibration method for scanning probe microscopy

Scanning probe microscopy (SPM) is ubiquitous in nanoscale science allowing the observation of features in real space down to the angstrom resolution. The scanning nature of SPM, wherein a sharp tip rasters the surface during which a physical setpoint is maintained via a control feedback loop, often implies that the image is subject to drift effects, leading to distortion of the resulting image. While there are in-operando methods to compensate for the drift, correcting the residual linear drift in obtained images is often neglected. In this paper, we present a reciprocal space-based technique to compensate the linear drift in atomically-resolved scanning probe microscopy images without distinction of the fast and slow scanning directions; furthermore this method does not require the set of SPM images obtained for the different scanning directions. Instead, the compensation is made possible by the a priori knowledge of the lattice parameters. The method can also be used to characterize and calibrate the SPM instrument.

Step height measurement of monoatomic silicon crystal lattice steps with a commercial atomic force microscope

Abstract Precise control of advanced materials relies on accurate dimensional metrology at the sub-nanometre scale. At this scale, the accuracy of scanning probe microscopy (SPM) has been limited by the lack of traceable transfer standard artefacts with calibration structures of suitable dimensions. With the adoption in 2019 of the silicon crystal lattice spacing as a secondary realization of the metre in the International System of Units (SI), SPM users have direct access to a realization of the SI metre at the sub-nanometre level by means of the step height of self-assembled monatomic lattice steps that can form on the surface of silicon crystals. A key challenge of successfully adopting this pathway is establishing the need for established protocols to minimize measurement errors and artifacts in routine laboratory use.
In this study, step height measurements of monoatomic lattice steps in an ordinal/staircase structure on a Si(111) crystal surface have been derived from images acquired with a commercially available, research-level atomic force microscope (AFM). Measurement results derived from AFM images using three different SPM image processing and analysis software packages are compared. Significant sources of measurement uncertainty are identified; principally the contribution from the dependence on scan direction. The calibration of the
AFM derived from this measurment was used to traceably measure the sub-nanometre lattice steps on a silicon carbide crystal surface to demonstrate the viability of this calibration pathway.

Feature correlation method for image reconstruction evaluation in under-sampled scanning probe microscopy

We introduce an advanced feature-correlation approach for evaluating the accuracy of data completion in scanning probe microscopy (SPM). Our method utilizes characteristic patterns from conventional SPM images and their reconstructions via data interpolation. We develop a refined comparative evaluation algorithm based on correlation coefficients. This algorithm provides a precise assessment by effectively addressing SPM-specific distortions such as thermal drift, feedback error, and noise limitations often overlooked by traditional metrics such as peak signal-to-noise ratio and structural similarity index measure. The effectiveness of our approach is demonstrated through its application in high-resolution and extensive scanning tunneling microscopy assessments.

Development of a method for analyzing the positional correlation of local structures in scanning probe microscopy images using template-matching image-processing method

The functionalities of materials are governed by the atom type and arrangement, and perturbations caused by defects and adsorbate interactions often significantly alter the behavior of materials. Scanning probe microscopy (SPM) can capture complex interactions caused by the structures on surfaces. It is, however, difficult to analyze such interactions appearing there. In this paper, an image-processing technique that employs template matching to recognize local structures in SPM images and calculate positional correlations is reported. This approach opens new avenues for investigating intricate perturbations in the sciences and provides detailed insights into materials science.

Automated Generation and Analysis of Molecular Images Using Generative Artificial Intelligence Models.

The development of scanning probe microscopy (SPM) has enabled unprecedented scientific discoveries through high-resolution imaging. Simulations and theoretical analysis of SPM images are equally important as obtaining experimental images since their comparisons provide fruitful understandings of the structures and physical properties of the investigated systems. So far, SPM image simulations are conventionally based on quantum mechanical theories, which can take several days in tasks of large-scale systems. Here, we have developed a scanning tunneling microscopy (STM) molecular image simulation and analysis framework based on a generative adversarial model, CycleGAN. It allows efficient translations between STM data and molecular models. Our CycleGAN-based framework introduces an approach for high-fidelity STM image simulation, outperforming traditional quantum mechanical methods in efficiency and accuracy. We envision that the integration of generative networks and high-resolution molecular imaging opens avenues in materials discovery relying on SPM technologies.

Determination of in-plane surface directions in scanning probe microscopy images.

We describe an approach to determine the in-plane crystallographic surface directions in scanning probe microscopy (SPM) images. This method is based on a one-time characterization of the SPM instrument with an appropriate test sample and is exemplified by the analysis of non-contact atomic force microscopy (NC-AFM) images on surfaces whose natural cleavage occurs along {111} planes. We introduce a two-dimensional rotation matrix relating the crystallographic surface directions known from an analysis of the macroscopic crystal to the directions in the NC-AFM images. The procedure takes into account rotations and mirror axes resulting from sample mounting, the SPM scanner rotation, the choice of scan direction, as well as data processing, storage, and display. We demonstrate the practicability of the approach by determining the [112̄] direction in topographic images of a CeO2(111) film grown on a Si(111) wafer and atomic resolution images of CaF2(111) with an instrument based on the beetle-type scanner.

UnDrift: A versatile software for fast offline SPM image drift correction.

Scanning probe microscopy (SPM) techniques are widely used to study the structure and properties of surfaces and interfaces across a variety of disciplines in chemistry and physics. One of the major artifacts in SPM is (thermal) drift, an unintended movement between sample and probe, which causes a distortion of the recorded SPM data. Literature holds a multitude of strategies to compensate for drift during the measurement (online drift correction) or afterwards (offline drift correction). With the currently available software tools, however, offline drift correction of SPM data is often a tedious and time-consuming task. This is particularly disadvantageous when analyzing long image series. Here, we present unDrift, an easy-to-use scientific software for fast and reliable drift correction of SPM images. unDrift provides three different algorithms to determine the drift velocity based on two consecutive SPM images. All algorithms can drift-correct the input data without any additional reference. The first semi-automatic drift correction algorithm analyzes the different distortion of periodic structures in two consecutive up and down (down and up) images, which enables unDrift to correct SPM images without stationary features or overlapping scan areas. The other two algorithms determine the drift velocity from the apparent movement of stationary features either by automatic evaluation of the cross-correlation image or based on positions identified manually by the user. We demonstrate the performance and reliability of unDrift using three challenging examples, namely images distorted by a very high drift velocity, only partly usable images, and images exhibiting an overall weak contrast. Moreover, we show that the semi-automatic analysis of periodic images can be applied to a long series containing hundreds of images measured at the calcite-water interface.

Microscopic studies on transparent AlSiN ternary hard coating

In this work AlSi nanocomposite coating were deposited by RF magnetron sputtering in Ar/N2 reactive atmosphere. AlSi alloy target of composition 86:14 wt % have been used to deposit the coating on float glass substrate by varying the deposition time: starting from 0.5 h to 2 h. The substrate temperature and target power were fixed at 250 °C and 100 W respectively. The effect of deposition time on microstructure, mechanical and optical property were studied by usingX-ray Diffraction (XRD), Stylus profiler, Field Emission Scanning Electron Microscope (FESEM), Energy Dispersive X-ray Spectroscopy (EDS), Transmission Electron Microscopy (TEM), UV–Visible Spectrometer and Nanoindentation techniques. The XRD shows the formation h-AlN phase for film deposited for 2 h which shows that with increase in deposition time, the film structure changes from amorphous to polycrystalline, the coating deposited for 2 h develops wurtzite-AlN nanocrystalline structure. The thickness of the coating increases with respect to the increasing deposition time which was confirmed from stylus profiler. The FESEM study shows the uneven and randomly oriented grains present in the coating. TEM study confirms the amorphous and polycrystalline nature of the films with low and high thickness respectively. The elemental composition was confirmed by EDS. UV–Visible Spectrometer delivers the optical transparency of the film which is >90% for all the coatings in the visible range spectra. The roughness of the samples was also measured from scanning probe microscope image, which showed average roughness (Rã) value below 2 nm. The maximum hardness obtained is 31 GPa for the coating deposited 2 h with 490 nm thickness, it has an elastic recovery of 92% which suggest its resistance to elasticity as well as plastic deformation.

Automated real-space lattice extraction for atomic force microscopy images

Analyzing atomically resolved images is a time-consuming process requiring solid experience and substantial human intervention. In addition, the acquired images contain a large amount of information such as crystal structure, presence and distribution of defects, and formation of domains, which need to be resolved to understand a material’s surface structure. Therefore, machine learning techniques have been applied in scanning probe and electron microscopies during the last years, aiming for automatized and efficient image analysis. This work introduces a free and open source tool (AiSurf: Automated Identification of Surface Images) developed to inspect atomically resolved images via scale-invariant feature transform and clustering algorithms. AiSurf extracts primitive lattice vectors, unit cells, and structural distortions from the original image, with no pre-assumption on the lattice and minimal user intervention. The method is applied to various atomically resolved non-contact atomic force microscopy images of selected surfaces with different levels of complexity: anatase TiO2(101), oxygen deficient rutile TiO2(110) with and without CO adsorbates, SrTiO3(001) with Sr vacancies and graphene with C vacancies. The code delivers excellent results and is tested against atom misclassification and artifacts, thereby facilitating the interpretation of scanning probe microscopy images.

A nanoscale study of the structure and electrical response of Sepia eumelanin.

Eumelanin, the brown-black member of the melanin biopigment family, is a prototype material for sustainable (green) organic electronics. Sepia eumelanin (Sepia) is a type of biosourced eumelanin extracted from the ink sac of cuttlefish. Electron microscopy and scanning probe microscopy images of Sepia show distinguishable near spherical granules with diameters of about 150-200 nm. We have recently reported on predominant electronic transport in printed films of Sepia formulated inks including the (insulating) binder Polyvinyl-butyral (PVB). In that work, we proposed that inter-granular percolative transport, observed for micrometric interelectrode distances, is promoted by the confining action of the PVB binder on the Sepia granules. Considering that inter-granular transport implies intra-granular transport, in this work we proceeded to a nanoscale study of Sepia granules by High Resolution Atomic Force Microscopy (HR-AFM) and Conductive-AFM (c-AFM). We have observed protrusions on the surface of the Sepia granules, suggesting sub-granular structures compatible with the hierarchical development of Sepia, as proposed elsewhere. For films of Sepia formulated inks deposited on gold-coated substrates, c-AFM revealed, for the very first time, a nanoscale electrical response. Nanoscale studies provide the key to structure-property relationships in biosourced materials strategic for sustainable organic electronics.

A Deep‐Learning Framework for the Automated Recognition of Molecules in Scanning‐Probe‐Microscopy Images

AbstractComputer vision as a subcategory of deep learning tackles complex vision tasks by dealing with data of images. Molecular images with exceptionally high resolution have been achieved thanks to the development of techniques like scanning probe microscopy (SPM). However, extracting useful information from SPM image data requires careful analysis which heavily relies on human supervision. In this work, we develop a deep learning framework using an advanced computer vision algorithm, Mask R‐CNN, to address the challenge of molecule detection, classification and instance segmentation in binary molecular nanostructures. We employ the framework to determine two triangular‐shaped molecules of similar STM appearance. Our framework could accurately differentiate two molecules and label their positions. We foresee that the application of computer vision in SPM images will become an indispensable part in the field, accelerating data mining and the discovery of new materials.

A Deep-Learning Framework for the Automated Recognition of Molecules in Scanning-Probe-Microscopy Images.

Computer vision as a subcategory of deep learning tackles complex vision tasks by dealing with data of images. Molecular images with exceptionally high resolution have been achieved thanks to the development of techniques like scanning probe microscopy (SPM). However, extracting useful information from SPM image data requires careful analysis which heavily relies on human supervision. In this work, we develop a deep learning framework using an advanced computer vision algorithm, Mask R-CNN, to address the challenge of molecule detection, classification and instance segmentation in binary molecular nanostructures. We employ the framework to determine two triangular-shaped molecules of similar STM appearance. Our framework could accurately differentiate two molecules and label their positions. We foresee that the application of computer vision in SPM images will become an indispensable part in the field, accelerating data mining and the discovery of new materials.

Fatigue-Induced Surface Modification of Zr-Based Metallic Glass under Environmental Conditions.

Metallic glass (MG), an intrinsic heterogeneous structure at the atomic scale, is one of the promising engineering materials with intriguing physical properties. MG often suffers from the fatigue issue caused by the repetitive mechanical loading, but it is still elusive how the local heterogeneity evolves and affects the macroscale fatigue and deformation against bulky external stress. In this study, we investigate the fatigue effect in Zr-Cu-Al ribbon using a bending fatigue method. We used scanning probe microscopy (SPM) in parallel with X-ray diffraction and X-ray photoelectron spectroscopy to figure out the loading effect on the local heterogeneities. The spatially resolved SPM images show that there is a local fluctuation of mechanical and electrical properties on the fatigued side along with morphological deformation compared to the unloaded side. Approaching the broken edge where the fatigue failure occurs, the decaying tendency is not only more dominant but also accelerated by surface oxidation of the fatigued regions. Our study provides a useful guideline on how to monitor structural changes of MGs under fatigue conditions in service and will open a door toward commercialization of high-performance structural engineering materials.

Neural image enhancement and restoration for time-lapse SPM images

This paper presents methods for enhancing and restoring scanning probe microscopy (SPM) images. We focus on image super-resolution as enhancement and image denoising and deblurring as restoration. Assume that almost same time-lapse images are captured in the same area of each specimen. In contrast to a single image, our proposed methods using a recurrent neural network improve the enhancement and restoration of SPM images by merging the time-lapse images in order to acquire a single enhanced/restored image. However, subtle deformations between the time-lapse SPM images and degraded pixels such as noisy and blurred pixels in the SPM image disturb the network to successfully merge the images. For the successful merge, our methods spatially align the time-lapse images and detect degraded pixels based on the characteristic property of SPM images. Experimental results demonstrate that our methods can reconstruct sharp, super-resolved images, and clean noiseless images.

Objective Point Symmetry Classifications/Quantifications of an Electron Diffraction Spot Pattern with Pseudo-Hexagonal Lattice Metric

The recently developed information-theoretic approach to crystallographic symmetry classifications and quantifications in two dimensions (2D) from digital transmission electron and scanning probe microscope images is adapted for the analysis of an experimental electron diffraction spot pattern, for the first time. Digital input data are considered in this approach to consist of the pixel-wise sums of approximately Gaussian distributed noise and an unknown underlying signal that is strictly 2D periodic. Structural defects within the crystals or on the crystal surfaces, instrumental image recording noise, slight deviations from zero-crystal-tilt conditions in transmission electron microscopy, inhomogeneous staining in structural biology studies of intrinsic membrane protein complexes in lipid bilayers, and small inaccuracies in the algorithmic processing of the digital data all contribute to a single generalized noise term. The plane symmetry group and projected Laue class(or 2D Bravais lattice type) that is anchored to the least broken symmetries are identified as genuine in the presence of generalized noise. More severely broken symmetries that are not anchored to the least broken symmetries are identified as pseudo-symmetries. Our point symmetry quantification study of an electron diffraction spot pattern is highly topical because a new contrast mechanism for 4D scanning transmission electron microscopy was recently demonstrated by other authors. The usage of objective symmetry quantifications is bound to become the preeminent condition of the establishment of that contrast mode as an industry-wide standard.

Effect of Solvent on Optical Properties and Surface Topography of Nanocomposite Conjugated Polymer Thin Film

The addition of TiO2 nanoparticles (NPs) to a white-emission conjugated polymer (CP) blend of poly(9,9-dioctylfluorene-2,7-diyl) (PFO) and poly(2-methoxy-5(2-ethylhexyl)−1,4-phenylenevinylene (MEH-PPV) enhanced the optical properties. However, the agglomeration of the nanoparticles restricted enhancement. This drawback was successfully overcome in this study. The highly polar solvent chloroform was mixed with toluene and used to prepare thin films of the blends. Solution blending and spin-coating techniques were used to prepare thin films with different TiO2 nanoparticle contents. In addition, pure toluene and chloroform were investigated as solvents for the nanocomposite blend. These three cases were compared by studying the emission spectra, absorption spectra, Commission Internationale d’Eclairage coordinates (CIE), field emission scanning electron microscopy (FE-SEM) images and scanning probe microscope (SPM) images. The average roughness, quenching constant, and energy transfer probability were calculated. The optimum physical properties of the thin film were achieved using nanoparticles at 15wt% and applied to the binary blend with a mixture of equal amounts of toluene and chloroform. Although chloroform is better for nanoparticle distribution, toluene is mandatory for obtaining the highest yield of PFO.

МЕТОДИ ПІДВИЩЕННЯ ТОЧНОСТІ ВИМІРЮВАННЯ СКАНУЮЧИМ ЗОНДОВИМ МІКРОСКОПОМ В ЗАЛЕЖНОСТІ ВІД ГЕОМЕТРІЇ ЗОНДУ

The article analyzes the influence of different types of probe geometry of a scanning probe microscope (SPM) on the features of measuring the surface topography of a nano-object. Based on the analysis, it was determined that the main drawback inherent in all modes of scanning probe microscopy is the finite size of the measuring probe tip, which cannot reach certain areas of the measuring surface during scanning, which is caused by the geometric characteristics of the probe shape. This leads to a significant deterioration of the spatial resolution and significant distortions in the SPM images when scanning surfaces with large unevenness of the ratio between the typical vertical and horizontal dimensions. Based on the conducted research, the methods of restoration of SPM images are proposed, which are built on mathematical and computer processing of SPM data, which takes into account the specific shape of the probe tip and allows improving the metrological characteristics of the measurement information. It is proved that the SPM image and the experimentally obtained shape of the tip are two-dimensional arrays of discrete values, for which the derivative is an incorrectly determined value. It is recommended that instead of deriving discrete functions during the numerical deconvolution of SPM images, during scanning with a constant average height, use the requirement for the minimum distance of the tip to the surface. It has been proven that the most effective way of digital correction of the surface topography is numerical deconvolution using the tip image obtained experimentally by scanning test structures with a well-known topography and subsequent computer processing of the data. A method of partial restoration of the topography is proposed, which is characterized by flexibility to the set metrological tasks and is aimed at increasing the speed of computing operations while ensuring the necessary accuracy of measurement information.

Machine Vision Automated Chiral Molecule Detection and Classification in Molecular Imaging.

Scanning probe microscopy (SPM) is recognized as an essential characterization tool in a broad range of applications, allowing for real-space atomic imaging of solid surfaces, nanomaterials, and molecular systems. Recently, the imaging of chiral molecular nanostructures via SPM has become a matter of increased scientific and technological interest due to their imminent use as functional platforms in a wide scope of applications, including nonlinear chiroptics, enantioselective catalysis, and enantiospecific sensing. Due to the time-consuming and error-prone image analysis process, a highly efficient analytic framework capable of identifying complex chiral patterns in SPM images is needed. Here, we adopted a state-of-the-art machine vision algorithm to develop a one-image-one-system deep learning framework for the analysis of SPM images. To demonstrate its accuracy and versatility, we employed it to determine the chirality of the molecules comprising two supramolecular self-assemblies with two distinct chiral organization patterns. Our framework accurately detected the position and labeled the chirality of each molecule. This framework underpins the tremendous potential of machine learning algorithms for the automated recognition of complex SPM image patterns in a wide range of research disciplines.

Measurement of the Length of Objects on Scanning Probe Microscope Images Using Curvature Detectors

Measurement of the Length of Objects on Scanning Probe Microscope Images Using Curvature Detectors

Removing background and estimating a unit height of atomic steps from a scanning probe microscopy image using a statistical model.

We present a statistical method to remove background and estimate a unit height of atomic steps of an image obtained using a scanning probe microscope. We adopt a mixture model consisting of multiple statistical distributions to describe an image. This statistical approach provides a comprehensive way to subtract a background surface even in the presence of atomic steps as well as to evaluate terrace heights in a single framework. Moreover, it also enables us to extract further quantitative information by introducing additional prior knowledge about the image. An example of this extension is estimating a unit height of atomic steps together with the terrace heights. We demonstrate the capability of our method for a topographic image of a Cu(111) surface taken using a scanning tunneling microscope. The background subtraction corrects all terraces to be parallel to a horizontal plane, and the precision of the estimated unit height reaches the order of a picometer. An open-source implementation of our method is available on the web.