Managing dose-, damage- and data-rates in multi-frame spectrum-imaging

Ultra-fast Digital DPC Yielding High Spatio-temporal Resolution for Low-Dose Phase Characterization

In the scanning transmission electron microscope, both phase imaging of beam-sensitive materials and characterization of a material's functional properties using in situ experiments are becoming more widely available. As the practicable scan speed of 4D-STEM detectors improves, so too does the temporal resolution achievable for both differential phase contrast (DPC) and ptychography. However, the read-out burden of pixelated detectors, and the size of the gigabyte to terabyte sized data sets, remain a challenge for both temporal resolution and their practical adoption. In this work, we combine ultra-fast scan coils and detector signal digitization to show that a high-fidelity DPC phase reconstruction can be achieved from an annular segmented detector. Unlike conventional analog data phase reconstructions from digitized DPC-segment images yield reliable data, even at the fastest scan speeds. Finally, dose fractionation by fast scanning and multi-framing allows for postprocess binning of frame streams to balance signal-to-noise ratio and temporal resolution for low-dose phase imaging for in situ experiments.

Diffusion distribution model for damage mitigation in scanning transmission electron microscopy

Despite the widespread use of Scanning Transmission Electron Microscopy (STEM) for observing the structure of materials at the atomic scale, a detailed understanding of some relevant electron beam damage mechanisms is limited. Recent reports suggest that certain types of damage can be modelled as a diffusion process and that the accumulation effects of this process must be kept low in order to reduce damage. We therefore develop an explicit mathematical formulation of spatiotemporal diffusion processes in STEM that take into account both instrument and sample parameters. Furthermore, our framework can aid the design of Diffusion Controlled Sampling (DCS) strategies using optimally selected probe positions in STEM, that constrain the cumulative diffusion distribution. Numerical simulations highlight the variability of the cumulative diffusion distribution for different experimental STEM configurations. These analytical and numerical frameworks can subsequently be used for careful design of 2- and 4-dimensional STEM experiments where beam damage is minimised.

Event-responsive scanning transmission electron microscopy

An ever-present limitation of transmission electron microscopy is the damage caused by high-energy electrons interacting with any sample. By reconsidering the fundamentals of imaging, we demonstrate an event-responsive approach to electron microscopy that delivers more information about the sample for a given beam current. Measuring the time to achieve an electron count threshold rather than waiting a predefined constant time improves the information obtained per electron. The microscope was made to respond to these events by blanking the beam, thus reducing the overall dose required. This approach automatically apportions dose to achieve a given signal-to-noise ratio in each pixel, eliminating excess dose that is associated with diminishing returns of information. We demonstrate the wide applicability of our approach to beam-sensitive materials by imaging biological tissue and zeolite.

Operando Electron Microscopy of Catalysts: The Missing Cornerstone in Heterogeneous Catalysis Research?

Heterogeneous catalysis in thermal gas-phase and electrochemical liquid-phase chemical conversion plays an important role in our modern energy landscape. However, many of the structural features that drive efficient chemical energy conversion are still unknown. These features are, in general, highly distinct on the local scale and lack translational symmetry, and thus, they are difficult to capture without the required spatial and temporal resolution. Correlating these structures to their function will, conversely, allow us to disentangle irrelevant and relevant features, explore the entanglement of different local structures, and provide us with the necessary understanding to tailor novel catalyst systems with improved productivity. This critical review provides a summary of the still immature field of operando electron microscopy for thermal gas-phase and electrochemical liquid-phase reactions. It focuses on the complexity of investigating catalytic reactions and catalysts, progress in the field, and analysis. The forthcoming advances are discussed in view of correlative techniques, artificial intelligence in analysis, and novel reactor designs.

Different atomic contrasts in HAADF images and EELS maps of rutile TiO2

High-angle annular dark-field (HAADF) imaging and elemental mapping at the atomic scale by scanning transmission electron microscopy (STEM) combined with electron energy-loss spectroscopy (EELS) are widely used for material characterization, in which quantitative understanding of the contrast of the image is required. Here, we report an unexpected image contrast in the elemental mapping of rutile TiO2, where the Ti L2,3 map shows an anisotropic elliptical shape that extends along the long axis in the octahedral structure, while the atomic contrast of Ti columns in the HAADF image is almost circular. Multi-slice simulation reveals that unique electron channeling related to the rutile structure and the difference of the potentials between HAADF and EELS cause the different atomic contrasts in the two images.

How Fast is Your Detector? The Effect of Temporal Response on Image Quality

With increasing interest in high-speed imaging, there should be an increased interest in the response times of our scanning transmission electron microscope detectors. Previous works have highlighted and contrasted the performance of various detectors for quantitative compositional or structural studies, but here, we shift the focus to detector temporal response, and the effect this has on captured images. The rise and decay times of eight detectors' single-electron response are reported, as well as measurements of their flatness, roundness, smoothness, and ellipticity. We develop and apply a methodology for incorporating the temporal detector response into simulations, showing that a loss of resolution is apparent in both the images and their Fourier transforms. We conclude that the solid-state detector outperforms the photomultiplier tube-based detectors in all areas bar a slightly less elliptical central hole and is likely the best detector to use for the majority of applications. However, using the tools introduced here, we encourage users to effectively evaluate which detector is most suitable for their experimental needs.

Ultrathin GaN quantum wells in AlN nanowires for UV-C emission

Molecular beam epitaxy growth and optical properties of GaN quantum disks in AlN nanowires were investigated, with the purpose of controlling the emission wavelength of AlN nanowire-based light emitting diodes. Besides GaN quantum disks with a thickness ranging from 1 to 4 monolayers, a special attention was paid to incomplete GaN disks exhibiting lateral confinement. Their emission consists of sharp lines which extend down to 215 nm, in the vicinity of AlN band edge. The room temperature cathodoluminescence intensity of an ensemble of GaN quantum disks embedded in AlN nanowires is about 20% of the low temperature value, emphasizing the potential of ultrathin/incomplete GaN quantum disks for deep UV emission.

Fast generation of calculated ADF-EDX scattering cross-sections under channelling conditions

Advanced materials often consist of multiple elements which are arranged in a complicated structure. Quantitative scanning transmission electron microscopy is useful to determine the composition and thickness of nanostructures at the atomic scale. However, significant difficulties remain to quantify mixed columns by comparing the resulting atomic resolution images and spectroscopy data with multislice simulations where dynamic scattering needs to be taken into account. The combination of the computationally intensive nature of these simulations and the enormous amount of possible mixed column configurations for a given composition indeed severely hamper the quantification process. To overcome these challenges, we here report the development of an incoherent non-linear method for the fast prediction of ADF-EDX scattering cross-sections of mixed columns under channelling conditions. We first explain the origin of the ADF and EDX incoherence from scattering physics suggesting a linear dependence between those two signals in the case of a high-angle ADF detector. Taking EDX as a perfect incoherent reference mode, we quantitatively examine the ADF longitudinal incoherence under different microscope conditions using multislice simulations. Based on incoherent imaging, the atomic lensing model previously developed for ADF is now expanded to EDX, which yields ADF-EDX scattering cross-section predictions in good agreement with multislice simulations for mixed columns in a core-shell nanoparticle and a high entropy alloy. The fast and accurate prediction of ADF-EDX scattering cross-sections opens up new opportunities to explore the wide range of ordering possibilities of heterogeneous materials with multiple elements.

Direct measurement of single-molecule dynamics and reaction kinetics in confinement using time-resolved transmission electron microscopy

We report experimental methodologies utilising transmission electron microscopy (TEM) as an imaging tool for reaction kinetics at the single molecule level, in direct space and with spatiotemporal continuity. Using reactions of perchlorocoronene (PCC) in nanotubes of different diameters and at different temperatures, we found a period of molecular movement to precede the intermolecular addition of PCC, with a stronger dependence of the reaction rate on the nanotube diameter, controlling the local environments around molecules, than on the reaction temperature (-175, 23 or 400 °C). Once initiated, polymerisation of PCC follows zero-order reaction kinetics with the observed reaction cross section σ_obs of 1.13 × 10^-9 nm² (11.3 ± 0.6 barn), determined directly from time-resolved TEM image series acquired with a rate of 100 frames per second. Polymerisation was shown to proceed from a single point, with molecules reacting sequentially, as in a domino effect, due to the strict conformational requirement of the Diels-Alder cycloaddition creating the bottleneck for the reaction. The reaction mechanism was corroborated by correlating structures of reaction intermediates observed in TEM images, with molecular weights measured by using mass spectrometry (MS) when the same reaction was triggered by UV irradiation. The approaches developed in this study bring the imaging of chemical reactions at the single-molecule level closer to traditional concepts of chemistry.

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.

Comparison of detection limits of direct-counting CMOS and CCD cameras in EELS experiments

The noise performance and the detection limits of a direct-counting complementary metal-oxide semiconductor (CMOS) K2 camera and a charge-coupled device (CCD) camera in electron energy loss spectroscopy (EELS) experiments were evaluated. In the case of a single spectrum acquired at the shortest dwell times (2.5 ms for K2 and 1 μs for CCD), the detection limit, defined as three times the standard deviation of the spectral noise (3σ), was very low (1 e^-/channel) in the counting-mode spectrum acquired with the K2 camera compared with that acquired with the CCD camera (5 e^-/channel). By contrast, the spectral noise of the K2 camera changed depending on the dwell time because of the multiple read-outs related to its fixed frame rate (400 fps). The spectral noise of the K2 camera was greater than that of the CCD camera when the dwell time was longer than ∼30 ms. Thus, the CCD camera was found to still be useful when detecting a very small number of electrons using a long acquisition time. In the case of an accumulated spectrum obtained by acquiring 10,000 spectra after subtracting the ultra-high-quality dark reference signal, the detection limits per read-out were ∼0.016 and ∼0.025 e^-/channel/read-out for the K2 and CCD cameras, respectively. Because both cameras have advantages and disadvantages with respect to their detection limit, speed, and dynamic range, their proper use is important.

Reducing electron beam damage through alternative STEM scanning strategies, Part II: Attempt towards an empirical model describing the damage process

In this second part of a series we attempt to construct an empirical model that can mimick all experimental observations made regarding the role of an alternative interleaved scan pattern in STEM imaging on the beam damage in a specific zeolite sample. We make use of a 2D diffusion model that describes the dissipation of the deposited beam energy in the sequence of probe positions that are visited during the scan pattern. The diffusion process allows for the concept of trying to 'outrun' the beam damage by carefully tuning the dwell time and distance between consecutively visited probe positions. We add a non linear function to include a threshold effect and evaluate the accumulated damage in each part of the image as a function of scan pattern details. Together, these ingredients are able to describe qualitatively all aspects of the experimental data and provide us with a model that could guide a further optimisation towards even lower beam damage without lowering the applied electron dose. We deliberately remain vague on what is diffusing here which avoids introducing too many sample specific details. This provides hope that the model can be applied also in sample classes that were not yet studied in such great detail by adjusting higher level parameters: a sample dependent diffusion constant and damage threshold.

Improving Quantitative EDS Chemical Analysis of Alloy Nanoparticles by PCA Denoising: Part II. Uncertainty Intervals

Analytical studies of nanoparticles (NPs) are frequently based on huge datasets derived from hyperspectral images acquired using scanning transmission electron microscopy. These large datasets require machine learning computational tools to reduce dimensionality and extract relevant information. Principal component analysis (PCA) is a commonly used procedure to reconstruct information and generate a denoised dataset; however, several open questions remain regarding the accuracy and precision of reconstructions. Here, we use experiments and simulations to test the effect of PCA processing on data obtained from AuAg alloy NPs a few nanometers wide with different compositions. This study aims to address the reliability of chemical quantification after PCA processing. Our results show that the PCA treatment mitigates the contribution of Poisson noise and leads to better quantification, indicating that denoised results may be reliable from the point of view of both uncertainty and accuracy for properly planned experiments. However, the initial data need to be of sufficient quality: these results can only be obtained if the signal-to-noise ratio of input data exceeds a minimal value to avoid the occurrence of random noise bias in the PCA reconstructions.

Making the Most of your Electrons: Challenges and Opportunities in Characterizing Hybrid Interfaces with STEM

Inspired by the unique architectures composed of hard and soft materials in natural and biological systems, synthetic hybrid structures and associated soft-hard interfaces have recently evoked significant interest. Soft matter is typically dominated by fluctuations even at room temperature, while hard matter (which often serves as the substrate or anchor for the soft component) is governed by rigid mechanical behavior. This dichotomy offers considerable opportunities to leverage the disparate properties offered by these components across a wide spectrum spanning from basic science to engineering insights with significant technological overtones. Such hybrid structures, which include polymer nanocomposites, DNA functionalized nanoparticle superlattices and metal organic frameworks to name a few, have delivered promising insights into the areas of catalysis, environmental remediation, optoelectronics, medicine, and beyond. The interfacial structure between these hard and soft phases exists across a variety of length scales and often strongly influence the functionality of hybrid systems. While scanning/transmission electron microscopy (S/TEM) has proven to be a valuable tool for acquiring intricate molecular and nanoscale details of these interfaces, the unusual nature of hybrid composites presents a suite of challenges that make assessing or establishing the classical structure-property relationships especially difficult. These include challenges associated with preparing electron-transparent samples and obtaining sufficient contrast to resolve the interface between dissimilar materials given the dose sensitivity of soft materials. We discuss each of these challenges and supplement a review of recent developments in the field with additional experimental investigations and simulations to present solutions for attaining a nano or atomic-level understanding of these interfaces. These solutions present a host of opportunities for investigating and understanding the role interfaces play in this unique class of functional materials.

Reducing electron beam damage through alternative STEM scanning strategies, Part I: Experimental findings

The highly energetic electrons in a transmission electron microscope (TEM) can alter or even completely destroy the structure of samples before sufficient information can be obtained. This is especially problematic in the case of zeolites, organic and biological materials. As this effect depends on both the electron beam and the sample and can involve multiple damage pathways, its study remained difficult and is plagued with irreproducibility issues, circumstantial evidence, rumors, and a general lack of solid data. Here we take on the experimental challenge to investigate the role of the STEM scan pattern on the damage behavior of a commercially available zeolite sample with the clear aim to make our observations as reproducible as possible. We make use of a freely programmable scan engine that gives full control over the tempospatial distribution of the electron probe on the sample and we use its flexibility to obtain multiple repeated experiments under identical conditions comparing the difference in beam damage between a conventional raster scan pattern and a newly proposed interleaved scan pattern that provides exactly the same dose and dose rate and visits exactly the same scan points. We observe a significant difference in beam damage for both patterns with up to 11 % reduction in damage (measured from mass loss). These observations demonstrate without doubt that electron dose, dose rate and acceleration voltage are not the only parameters affecting beam damage in (S)TEM experiments and invite the community to rethink beam damage as an unavoidable consequence of applied electron dose.

Extraction of physically meaningful endmembers from STEM spectrum-images combining geometrical and statistical approaches

This article addresses extraction of physically meaningful information from STEM EELS and EDX spectrum-images using methods of Multivariate Statistical Analysis. The problem is interpreted in terms of data distribution in a multi-dimensional factor space, which allows for a straightforward and intuitively clear comparison of various approaches. A new computationally efficient and robust method for finding physically meaningful endmembers in spectrum-image datasets is presented. The method combines the geometrical approach of Vertex Component Analysis with the statistical approach of Bayesian inference. The algorithm is described in detail at an example of EELS spectrum-imaging of a multi-compound CMOS transistor.

Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

Optimizing Nonrigid Registration for Scanning Transmission Electron Microscopy Image Series

Achieving sub-picometer precision measurements of atomic column positions in high-resolution scanning transmission electron microscope images using nonrigid registration (NRR) and averaging of image series requires careful optimization of experimental conditions and the parameters of the registration algorithm. On experimental data from SrTiO3 [100], sub-pm precision requires alignment of the sample to the zone axis to within 1 mrad tilt and sample drift of less than 1 nm/min. At fixed total electron dose for the series, precision in the fast scan direction improves with shorter pixel dwell time to the limit of our microscope hardware, but the best precision along the slow scan direction occurs at 6 μs/px dwell time. Within the NRR algorithm, the “smoothness factor” that penalizes large estimated shifts is the most important parameter for sub-pm precision, but in general, the precision of NRR images is robust over a wide range of parameters.

Development of a Practicable Digital Pulse Read-Out for Dark-Field STEM

When characterizing beam-sensitive materials in the scanning transmission electron microscope (STEM), low-dose techniques are essential for the reliable observation of samples in their true state. A simple route to minimize both the total electron-dose and the dose-rate is to reduce the electron beam-current and/or raster the probe at higher speeds. At the limit of these settings, and with current detectors, the resulting images suffer from unacceptable artifacts, including signal-streaking, detector-afterglow, and poor signal-to-noise ratios (SNRs). In this article, we present an alternative approach to capture dark-field STEM images by pulse-counting individual electrons as they are scattered to the annular dark-field (ADF) detector. Digital images formed in this way are immune from analog artifacts of streaking or afterglow and allow clean, high-SNR images to be obtained even at low beam-currents. We present results from both a ThermoFisher FEI Titan G2 operated at 300 kV and a Nion UltraSTEM200 operated at 200 kV, and compare the images to conventional analog recordings. ADF data are compared with analog counterparts for each instrument, a digital detector-response scan is performed on the Titan, and the overall rastering efficiency is evaluated for various scanning parameters.

Enhancing hyperspectral EELS analysis of complex plasmonic nanostructures with pan-sharpening

Nanoscale hyperspectral techniques-such as electron energy loss spectroscopy (EELS)-are critical to understand the optical response in plasmonic nanostructures, but as systems become increasingly complex, the required sampling density and acquisition times become prohibitive for instrumental and specimen stability. As a result, there has been a recent push for new experimental methodologies that can provide comprehensive information about a complex system, while significantly reducing the duration of the experiment. Here, we present a pan-sharpening approach to hyperspectral EELS analysis, where we acquire two datasets from the same region (one with high spatial resolution and one with high spectral fidelity) and combine them to achieve a single dataset with the beneficial properties of both. This work outlines a straightforward, reproducible pathway to reduced experiment times and higher signal-to-noise ratios, while retaining the relevant physical parameters of the plasmonic response, and is generally applicable to a wide range of spectroscopy modalities.

Natural hybrid silica/protein superstructure at atomic resolution

Formation of highly symmetric skeletal elements in demosponges, called spicules, follows a unique biomineralization mechanism in which polycondensation of an inherently disordered amorphous silica is guided by a highly ordered proteinaceous scaffold, the axial filament. The enzymatically active proteins, silicateins, are assembled into a slender hybrid silica/protein crystalline superstructure that directs the morphogenesis of the spicules. Furthermore, silicateins are known to catalyze the formation of a large variety of other technologically relevant organic and inorganic materials. However, despite the biological and biotechnological importance of this macromolecule, its tertiary structure was never determined. Here we report the atomic structure of silicatein and the entire mineral/organic hybrid assembly with a resolution of 2.4 Å. In this work, the serial X-ray crystallography method was successfully adopted to probe the 2-µm-thick filaments in situ, being embedded inside the skeletal elements. In combination with imaging and chemical analysis using high-resolution transmission electron microscopy, we provide detailed information on the enzymatic activity of silicatein, its crystallization, and the emergence of a functional three-dimensional silica/protein superstructure in vivo. Ultimately, we describe a naturally occurring mineral/protein crystalline assembly at atomic resolution.

Partial Scanning Transmission Electron Microscopy with Deep Learning

Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam exposure with minimal information loss. Following successful applications of deep learning to compressed sensing, we have developed a two-stage multiscale generative adversarial neural network to complete realistic 512 × 512 scanning transmission electron micrographs from spiral, jittered gridlike, and other partial scans. For spiral scans and mean squared error based pre-training, this enables electron beam coverage to be decreased by 17.9× with a 3.8% test set root mean squared intensity error, and by 87.0× with a 6.2% error. Our generator networks are trained on partial scans created from a new dataset of 16227 scanning transmission electron micrographs. High performance is achieved with adaptive learning rate clipping of loss spikes and an auxiliary trainer network. Our source code, new dataset, and pre-trained models are publicly available.

Imaging Beam-Sensitive Materials by Electron Microscopy

Electron microscopy allows the extraction of multidimensional spatiotemporally correlated structural information of diverse materials down to atomic resolution, which is essential for figuring out their structure-property relationships. Unfortunately, the high-energy electrons that carry this important information can cause damage by modulating the structures of the materials. This has become a significant problem concerning the recent boost in materials science applications of a wide range of beam-sensitive materials, including metal-organic frameworks, covalent-organic frameworks, organic-inorganic hybrid materials, 2D materials, and zeolites. To this end, developing electron microscopy techniques that minimize the electron beam damage for the extraction of intrinsic structural information turns out to be a compelling but challenging need. This article provides a comprehensive review on the revolutionary strategies toward the electron microscopic imaging of beam-sensitive materials and associated materials science discoveries, based on the principles of electron-matter interaction and mechanisms of electron beam damage. Finally, perspectives and future trends in this field are put forward.

Extremely low count detection for EELS spectrum imaging by reducing CCD read-out noise

Extremely low count detection for EELS spectrum imaging is required to overcome problems with electron irradiation and widen the range of available applications. We have made a systematic statistical study of the reduction of CCD noise for EELS. We propose a calculation method to estimate the properties of noise and a procedure to reduce it. Since the dominant noise is a practically random component, it can be reduced by subtracting the population mean of the dark reference and a summation over an appropriate number of spectra, depending on the standard deviation of the noise. A gain-averaging method can further improve the signal-to-noise (SN) ratio. It is thereby demonstrated that a high-SN spectrum can be obtained even for a single-count core-loss signal. The present method would be useful for measuring low signal spectrum such as monochromated spectra and for radiation sensitive materials.

Optimal principal component analysis of STEM XEDS spectrum images

STEM XEDS spectrum images can be drastically denoised by application of the principal component analysis (PCA). This paper looks inside the PCA workflow step by step on an example of a complex semiconductor structure consisting of a number of different phases. Typical problems distorting the principal components decomposition are highlighted and solutions for the successful PCA are described. Particular attention is paid to the optimal truncation of principal components in the course of reconstructing denoised data. A novel accurate and robust method, which overperforms the existing truncation methods is suggested for the first time and described in details.

The effects of encapsulation on damage to molecules by electron radiation

Encapsulation of materials imaged by high resolution transmission electron microscopy presents a promising route to the reduction of sample degradation, both independently and in combination with other traditional solutions to controlling radiation damage. In bulk crystals, the main effect of encapsulation (or coating) is the elimination of diffusion routes of beam-induced radical species, enhancing recombination rates and acting to limit overall damage. Moving from bulk to low dimensional materials has significant effects on the nature of damage under the electron beam. We consider the major changes in mechanisms of damage of low dimensional materials by separating the effects of dimensional reduction from the effects of encapsulation. An effect of confinement is discussed using a model example of coronene molecules encapsulated inside single walled carbon nanotubes as determined from molecular dynamics simulations calculating the threshold energy required for hydrogen atom dissociation. The same model system is used to estimate the rate at which the nanotube can dissipate excess thermal energy above room temperature by acting as a thermal sink.

Joint non-rigid image registration and reconstruction for quantitative atomic resolution scanning transmission electron microscopy

Aberration corrected scanning transmission electron microscopes (STEM) enable to determine local strain fields, composition and bonding states at atomic resolution. The precision to locate atomic columns is often obstructed by scan artifacts limiting the quantitative interpretation of STEM datasets. Here, a novel bias-corrected non-rigid registration approach is presented that compensates for fast and slow scan artifacts in STEM image series. The bias-correction is responsible for the correction of the slow scan artifacts and based on a explicit coupling of the deformations of the individual images in a series via a minimization of the average deformation. This allows to reduce fast scan noise in an image series and slow scan distortions simultaneously. The novel approach is tested on synthetic and experimental images and its implication on atomic resolution strain and elemental mapping is discussed.