Direct atomic fabrication and dopant positioning in Si using electron beams with active real-time image-based feedback

Direct Fabrication of Atomically Defined Pores in MXenes Using Feedback-Driven STEM

Controlled fabrication of nanopores in 2D materials offer the means to create robust membranes needed for ion transport and nanofiltration. Techniques for creating nanopores have relied upon either plasma etching or direct irradiation; however, aberration-corrected scanning transmission electron microscopy (STEM) offers the advantage of combining a sub-Å sized electron beam for atomic manipulation along with atomic resolution imaging. Here, a method for automated nanopore fabrication is utilized with real-time atomic visualization to enhance the mechanistic understanding of beam-induced transformations. Additionally, an electron beam simulation technique, Electron-Beam Simulator (E-BeamSim) is developed to observe the atomic movements and interactions resulting from electron beam irradiation. Using the MXene Ti3C2Tx, the influence of temperature on nanopore fabrication is explored by tracking atomic transformations and find that at room temperature the electron beam irradiation induces random displacement and results in titanium pileups at the nanopore edge, which is confirmed by E-BeamSim. At elevated temperatures, after removal of the surface functional groups and with the increased mobility of atoms results in atomic transformations that lead to the selective removal of atoms layer by layer. This work can lead to the development of defect engineering techniques within functionalized MXene layers and other 2D materials.

The Synthescope: A Vision for Combining Synthesis with Atomic Fabrication

The scanning transmission electron microscope, a workhorse instrument in materials characterization, is being transformed into an atomic-scale material-manipulation platform. With an eye on the trajectory of recent developments and the obstacles toward progress in this field, a vision for a path toward an expanded set of capabilities and applications is provided. The microscope is reconceptualized as an instrument for fabrication and synthesis with the capability to image and characterize atomic-scale structural formation as it occurs. Further development and refinement of this approach may have substantial impact on research in microelectronics, quantum information science, and catalysis, where precise control over atomic-scale structure and chemistry of a few "active sites" can have a dramatic impact on larger-scale functionality and where developing a better understanding of atomic-scale processes can help point the way to larger-scale synthesis approaches.

Applications of Transmission Electron Microscopy in Phase Engineering of Nanomaterials

Phase engineering of nanomaterials (PEN) is an emerging field that aims to tailor the physicochemical properties of nanomaterials by precisely manipulating their crystal phases. To advance PEN effectively, it is vital to possess the capability of characterizing the structures and compositions of nanomaterials with precision. Transmission electron microscopy (TEM) is a versatile tool that combines reciprocal-space diffraction, real-space imaging, and spectroscopic techniques, allowing for comprehensive characterization with exceptional resolution in the domains of time, space, momentum, and, increasingly, even energy. In this Review, we first introduce the fundamental mechanisms behind various TEM-related techniques, along with their respective application scopes and limitations. Subsequently, we review notable applications of TEM in PEN research, including applications in fields such as metallic nanostructures, carbon allotropes, low-dimensional materials, and nanoporous materials. Specifically, we underscore its efficacy in phase identification, composition and chemical state analysis, in situ observations of phase evolution, as well as the challenges encountered when dealing with beam-sensitive materials. Furthermore, we discuss the potential generation of artifacts during TEM imaging, particularly in scanning modes, and propose methods to minimize their occurrence. Finally, we offer our insights into the present state and future trends of this field, discussing emerging technologies including four-dimensional scanning TEM, three-dimensional atomic-resolution imaging, and electron microscopy automation while highlighting the significance and feasibility of these advancements.

Atomically Precise Detection and Manipulation of Nitrogen-Vacancy Centers in Nanodiamonds

Nitrogen-vacancy (NV) centers in nanodiamonds are a promising quantum communication system offering robust and discrete single photon emission, but a more thorough understanding of properties of the NV centers is critical for real world implementation in functional devices. The first step to understanding how factors such as surface, depth, and charge state affect NV center properties is to directly characterize these defects on the atomic scale. Here we use Angstrom-resolution scanning transmission electron microscopy (STEM) to identify a single NV center in a ∼4 nm natural nanodiamond through simultaneous acquisition of electron energy loss and energy dispersive X-ray spectra, which provide a characteristic NV center peak and a nitrogen peak, respectively. In addition, we identify NV centers in larger, ∼15 nm synthetic nanodiamonds, although without the single-defect resolution afforded by the lower background of the smaller natural nanodiamonds. We have further demonstrated the potential to directly position these technologically relevant defects at the atomic scale using the scanning electron beam to "herd" NV centers and nitrogen atoms across their host nanodiamonds.

The Atomic Drill Bit: Precision Controlled Atomic Fabrication of 2D Materials

The ability to deterministically fabricate nanoscale architectures with atomic precision is the central goal of nanotechnology, whereby highly localized changes in the atomic structure can be exploited to control device properties at their fundamental physical limit. Here, an automated, feedback-controlled atomic fabrication method is reported and the formation of 1D-2D heterostructures in MoS₂ is demonstrated through selective transformations along specific crystallographic orientations. The atomic-scale probe of an aberration-corrected scanning transmission electron microscope (STEM) is used, and the shape and symmetry of the scan pathway relative to the sample orientation are controlled. The focused and shaped electron beam is used to reliably create Mo₆ S₆ nanowire (MoS-NW) terminated metallic-semiconductor 1D-2D edge structures within a pristine MoS₂ monolayer with atomic precision. From these results, it is found that a triangular beam path aligned along the zig-zag sulfur terminated (ZZS) direction forms stable MoS-NW edge structures with the highest degree of fidelity without resulting in disordering of the surrounding MoS₂ monolayer. Density functional theory (DFT) calculations and ab initio molecular dynamic simulations (AIMD) are used to calculate the energetic barriers for the most stable atomic edge structures and atomic transformation pathways. These discoveries provide an automated method to improve understanding of atomic-scale transformations while opening a pathway toward more precise atomic-scale engineering of materials.

Identifying and manipulating single atoms with scanning transmission electron microscopy

The manipulation of individual atoms has developed from visionary speculation into an established experimental science. Using focused electron irradiation in a scanning transmission electron microscope instead of a physical tip in a scanning probe microscope confers several benefits, including thermal stability of the manipulated structures, the ability to reach into bulk crystals, and the chemical identification of single atoms. However, energetic electron irradiation also presents unique challenges, with an inevitable possibility of irradiation damage. Understanding the underlying mechanisms will undoubtedly continue to play an important role to guide experiments. Great progress has been made in several materials including graphene, carbon nanotubes, and crystalline silicon in the eight years since the discovery of electron-beam manipulation, but the important challenges that remain will determine how far we can expect to progress in the near future.

Probing Electron Beam Induced Transformations on a Single-Defect Level via Automated Scanning Transmission Electron Microscopy

A robust approach for real-time analysis of the scanning transmission electron microscopy (STEM) data streams, based on ensemble learning and iterative training (ELIT) of deep convolutional neural networks, is implemented on an operational microscope, enabling the exploration of the dynamics of specific atomic configurations under electron beam irradiation via an automated experiment in STEM. Combined with beam control, this approach allows studying beam effects on selected atomic groups and chemical bonds in a fully automated mode. Here, we demonstrate atomically precise engineering of single vacancy lines in transition metal dichalcogenides and the creation and identification of topological defects in graphene. The ELIT-based approach facilitates direct on-the-fly analysis of the STEM data and engenders real-time feedback schemes for probing electron beam chemistry, atomic manipulation, and atom by atom assembly.

Mapping Conductance and Switching Behavior of Graphene Devices In Situ

Graphene is proposed for use in various nanodevice designs, many of which harness emergent quantum properties for device functionality. However, visualization, measurement, and manipulation become nontrivial at nanometer and atomic scales, representing a significant challenge for device fabrication, characterization, and optimization at length scales where quantum effects emerge. Here, proof of principle results at the crossroads between 2D nanoelectronic devices, e-beam-induced modulation, and imaging with secondary electron e-beam induced currents (SEEBIC) is presented. A device platform compatible with scanning transmission electron microscopy investigations is introduced. Then how the SEEBIC imaging technique can be used to visualize conductance and connectivity in single layer graphene nanodevices, even while supported on a thicker substrate (conditions under which conventional imaging fails) is shown. Finally, it is shown that the SEEBIC imaging technique can detect subtle differences in charge transport through time in nonohmic graphene nanoconstrictions indicating the potential to reveal dynamic electronic processes.

Towards automating structural discovery in scanning transmission electron microscopy *

Scanning transmission electron microscopy is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls, extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of ‘active learning’ methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO3 matrix, ferroelectric domains in BiFeO3, and topological defects in graphene. The code developed in this manuscript is open sourced and will be released at github.com/nccreang/AE_Workflows.

Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics to self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiments (AE) in imaging. Here, we aim to analyze the major pathways toward AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment and consider the latencies, biases, and prior knowledge of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities, and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of human operators (as is the case for autonomous driving), but rather automation of routine operations such as microscope tuning, etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning. Overall, we argue that ML/AI can dramatically alter the (S)TEM and SPM fields; however, this process is likely to be highly nontrivial and initiated by combined human-ML workflows and will bring challenges both from the microscope and ML/AI sides. At the same time, these methods will enable opportunities and paradigms for scientific discovery and nanostructure fabrication.

Mechanism of Electron-Beam Manipulation of Single-Dopant Atoms in Silicon

The precise positioning of dopant atoms within bulk crystal lattices could enable novel applications in areas including solid-state sensing and quantum computation. Established scanning probe techniques are capable tools for the manipulation of surface atoms, but at a disadvantage due to their need to bring a physical tip into contact with the sample. This has prompted interest in electron-beam techniques, followed by the first proof-of-principle experiment of bismuth dopant manipulation in crystalline silicon. Here, we use first-principles modeling to discover a novel indirect exchange mechanism that allows electron impacts to non-destructively move dopants with atomic precision within the silicon lattice. However, this mechanism only works for the two heaviest group V donors with split-vacancy configurations, Bi and Sb. We verify our model by directly imaging these configurations for Bi and by demonstrating that the promising nuclear spin qubit Sb can be manipulated using a focused electron beam.

Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO₃, with results showing that, with just 20% of the area sampled, most high-response clusters were captured. This approach can allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability. Improvements to the framework to enable the incorporation of more prior information and improve efficiency further are modeled and discussed.

Exploring order parameters and dynamic processes in disordered systems via variational autoencoders

We suggest and implement an approach for the bottom-up description of systems undergoing large-scale structural changes and chemical transformations from dynamic atomically resolved imaging data, where only partial or uncertain data on atomic positions are available. This approach is predicated on the synergy of two concepts, the parsimony of physical descriptors and general rotational invariance of noncrystalline solids, and is implemented using a rotationally invariant extension of the variational autoencoder applied to semantically segmented atom-resolved data seeking the most effective reduced representation for the system that still contains the maximum amount of original information. This approach allowed us to explore the dynamic evolution of electron beam-induced processes in a silicon-doped graphene system, but it can be also applied for a much broader range of atomic scale and mesoscopic phenomena to introduce the bottom-up order parameters and explore their dynamics with time and in response to external stimuli.

Imaging Secondary Electron Emission from a Single Atomic Layer

Graphene-based devices hold promise for a wide range of technological applications. Yet characterizing the structure and the electrical properties of a material that is only one atomic layer thick still poses technical challenges. Recent investigations indicate that secondary-electron electron-beam-induced current (SE-EBIC) imaging can reveal subtle details regarding electrical conductivity and electron transport with high spatial resolution. Here, it is shown that the SEEBIC imaging mode can be used to detect suspended single layers of graphene and distinguish between different numbers of layers. Pristine and contaminated areas of graphene are also compared to show that pristine graphene exhibits a substantially lower SE yield than contaminated regions. This SEEBIC imaging mode may provide valuable information for the engineering of surface coatings where SE yield is a priority.

Real-time dose control for electron-beam lithography

Shot-to-shot, or pixel-to-pixel, dose variation during electron-beam lithography is a significant practical and fundamental problem. Dose variations associated with charging, electron source instability, optical system drift, and ultimately shot noise in the e-beam itself conspire to critical dimension variability, line width/edge roughness, and limited throughput. It would be an important improvement to e-beam based patterning technology if real-time feedback control of electron-dose were provided so that pattern quality and throughput would be improved beyond the shot noise limit. In this paper, we demonstrate control of e-beam dose based on the measurement of electron arrival at the sample where patterns are written, rather than from the source or another point in the electron optical column. Our results serve as the first steps towards real-time dose control and eventually overcoming the shot noise.

Electron beam triggered single-atom dynamics in two-dimensional materials

Controlling atomic structure and dynamics with single-atom precision is the ultimate goal in nanoscience and nanotechnology. Despite great successes being achieved by scanning tunneling microscopy (STM) over the past a few decades, fundamental limitations, such as ultralow temperature, and low throughput, significantly hinder the fabrication of a large array of atomically defined structures by STM. The advent of aberration correction in scanning transmission electron microscopy (STEM) revolutionized the field of nanomaterials characterization pushing the detection limit down to single-atom sensitivity. The sub-angstrom focused electron beam (e-beam) of STEM is capable of interacting with an individual atom, thereby it is the ideal platform to direct and control matter at the level of a single atom or a small cluster. In this article, we discuss the transfer of energy and momentum from the incident e-beam to atoms and their subsequent potential dynamics under different e-beam conditions in 2D materials, particularly transition metal dichalcogenides (TMDs). Next, we systematically discuss the e-beam triggered structural evolutions of atomic defects, line defects, grain boundaries, and stacking faults in a few representative 2D materials. Their formation mechanisms, kinetic paths, and practical applications are comprehensively discussed. We show that desired structural evolution or atom-by-atom assembly can be precisely manipulated by e-beam irradiation which could introduce intriguing functionalities to 2D materials. In particular, we highlight the recent progress on controlling single Si atom migration in real-time on monolayer graphene along an extended path with high throughput in automated STEM. These results unprecedentedly demonstrate that single-atom dynamics can be realized by an atomically focused e-beam. With the burgeoning of artificial intelligence and big data, we can expect that fully automated microscopes with real-time data analysis and feedback could readily design and fabricate large scale nanostructures with unique functionalities in the near future.

Tracking atomic structure evolution during directed electron beam induced Si-atom motion in graphene via deep machine learning

Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was explored, providing information on changes of the Si atom neighborhood during atomic motion and providing statistical information of possible defect configurations. The combination of a Gaussian mixture model and principal component analysis applied to the deep learning-processed experimental data allowed disentangling of the atomic distortions for two different graphene sublattices. This approach demonstrates the potential of e-beam manipulation to create defect libraries of multiple realizations of the same defect and explore the potential of symmetry breaking physics. The rapid image analytics enabled via a deep learning network further empowers instrumentation for e-beam controlled atom-by-atom fabrication. The analysis described in the paper can be reproduced via an interactive Jupyter notebook at https://git.io/JJ3Bx.

Toward Electrochemical Studies on the Nanometer and Atomic Scales: Progress, Challenges, and Opportunities

Electrochemical reactions and ionic transport underpin the operation of a broad range of devices and applications, from energy storage and conversion to information technologies, as well as biochemical processes, artificial muscles, and soft actuators. Understanding the mechanisms governing function of these applications requires probing local electrochemical phenomena on the relevant time and length scales. Here, we discuss the challenges and opportunities for extending electrochemical characterization probes to the nanometer and ultimately atomic scales, including challenges in down-scaling classical methods, the emergence of novel probes enabled by nanotechnology and based on emergent physics and chemistry of nanoscale systems, and the integration of local data into macroscopic models. Scanning probe microscopy (SPM) methods based on strain detection, potential detection, and hysteretic current measurements are discussed. We further compare SPM to electron beam probes and discuss the applicability of electron beam methods to probe local electrochemical behavior on the mesoscopic and atomic levels. Similar to a SPM tip, the electron beam can be used both for observing behavior and as an active electrode to induce reactions. We briefly discuss new challenges and opportunities for conducting fundamental scientific studies, matter patterning, and atomic manipulation arising in this context.

Influence of temperature on the displacement threshold energy in graphene

The atomic structure of nanomaterials is often studied using transmission electron microscopy. In addition to image formation, the energetic electrons impinging on the sample may also cause damage. In a good conductor such as graphene, the damage is limited to the knock-on process caused by elastic electron-nucleus scattering. This process is determined by the kinetic energy an atom needs to be sputtered, i.e. its displacement threshold energy E_d. This is typically assumed to have a fixed value for all electron impacts on equivalent atoms within a crystal. Here we show using density functional tight-binding simulations that the displacement threshold energy is affected by thermal perturbations of atoms from their equilibrium positions. This effect can be accounted for in the estimation of the displacement cross section by replacing the constant threshold energy value with a distribution. Our refined model better describes previous precision measurements of graphene knock-on damage, and should be considered also for other low-dimensional materials.

Compressed Sensing of Scanning Transmission Electron Microscopy (STEM) With Nonrectangular Scans

Scanning transmission electron microscopy (STEM) has become the main stay for materials characterization on atomic level, with applications ranging from visualization of localized and extended defects to mapping order parameter fields. In recent years, attention has focused on the potential of STEM to explore beam induced chemical processes and especially manipulating atomic motion, enabling atom-by-atom fabrication. These applications, as well as traditional imaging of beam sensitive materials, necessitate increasing the dynamic range of STEM in imaging and manipulation modes, and increasing the absolute scanning speed which can be achieved by combining sparse sensing methods with nonrectangular scanning trajectories. Here we have developed a general method for real-time reconstruction of sparsely sampled images from high-speed, noninvasive and diverse scanning pathways, including spiral scan and Lissajous scan. This approach is demonstrated on both the synthetic data and experimental STEM data on the beam sensitive material graphene. This work opens the door for comprehensive investigation and optimal design of dose efficient scanning strategies and real-time adaptive inference and control of e-beam induced atomic fabrication.

The Atomic Circus: Small Electron Beams Spotlight Advanced Materials Down to the Atomic Scale

Defects in crystalline materials have a tremendous impact on their functional behavior. Controlling and tuning of these imperfections can lead to marked improvements in their physical, electrical, magnetic, and optical properties. Thanks to the development of aberration-corrected (scanning) transmission electron microscopy (STEM/TEM), direct visualization of defects at multiple length scales has now become possible, including those critically important defects at the atomic scale. Thorough understanding of the nature and dynamics of these defects is the key to unraveling the fundamental origins of structure-property relationships. Such insight can therefore allow the creation of new materials with desired properties through appropriate defect engineering. Herein, several examples of new insights obtained from representative functional materials are shown, including piezoelectrics/ferroelectrics, oxide interfaces, thermoelectrics, electrocatalysts, and 2D materials.