Search for: All records

Abstract Next-generation semiconductor devices are adopting three-dimensional (3D) architectures with feature sizes in the few-nanometer regime, creating a need for atomic-scale metrology to identify and resolve performance-limiting fabrication challenges. X-ray methods provide 3D information but lack atomic resolution, while conventional electron microscopy offers limited depth sensitivity. Here we show how multislice electron ptychography, a computational microscopy technique with sub-Ångström lateral and nanometer-scale depth resolution, enables 3D imaging of buried device structures. We image prototype gate-all-around transistors and directly quantify roughness, strain, and defects at the interface of the 3D gate oxide wrapped around the channel. We find that silicon in the 5-nm-thick channel relaxes away from the interfaces, leaving only ~60% of atoms in a bulk-like structure. From a single dataset, ptychography provides quantitative metrology of atomic-scale interface roughness in 3D, previously accessible only through indirect inference, along with strain and other structural parameters needed for device modeling and process development. 

When hydrogen atoms occupy interstitial sites in metal lattices, they form metal hydrides (MHx), whose structural and electronic properties can differ significantly from those of the host metals. Determining where the hydrogen is located within the MHx is crucial for predicting and understanding the resultant unique physical and electronic properties of the hydride. Yet, directly imaging hydrogen within a host material remains a major challenge due to its weak signal in conventional X-ray and electron imaging techniques. Here, we employ electron ptychography, a scanning transmission electron microscopy (STEM) technique, to image the three-dimensional (3D) distribution of H atoms in palladium hydride (PdHx) nanocubes, one of the most studied and industrially relevant MHx materials. We observe an unexpected one-dimensional superlattice ordering of hydrogen within the PdHx nanocubes and 3D hydrogen clustering in localized regions within the PdHx nanocubes, revealing spatial heterogeneity in metal hydride nanoparticles previously inaccessible by other methods. 

The development of large-wafer single-crystal AlN substrates has expanded their role beyond UV photonics to enable next-generation integrated electronics. In this study, we investigated δ-doped AlN/GaN/AlN heterostructures, where an n-type δ-doping layer is introduced to suppress the undesired two-dimensional hole gas at the bottom GaN/AlN interface, thereby enhancing the conductivity of the two-dimensional electron gas at the top AlN/GaN interface. We began by systematically identifying epitaxial growth conditions to achieve high crystalline quality, as confirmed by cross-sectional transmission electron microscopy images. To understand the impact of δ-doping density on transport properties, we combined theoretical modeling with experimental measurements, revealing that an optimal δ-doping density of ∼5×1013cm−2 minimizes interface roughness scattering and enhances mobility. Finally, we demonstrated scalability by extending the growth to large-area wafers, supported by structural and transport characterization. A sheet resistance of 246.8 ± 38.1 Ω/□ measured across a 3-in. (75 mm) wafer highlights the uniformity and performance potential of δ-doped AlN/GaN/AlN heterostructures for high-power, high-frequency electronic applications. 

Recently, superconductivity was discovered with a superconducting transition temperature (Tc) of 2 K in strained (110)-oriented RuO2 films grown on TiO2(110) single-crystal substrates. In this work, we predict and realize superconductivity in strained (100)-oriented RuO2 thin films grown on TiO2(100) single-crystal substrates. We show that while density functional theory predicts the Tc of strained RuO2(100) films to be even higher than the RuO2(110) films, our transport and angle-resolved photoemission spectroscopy measurements find the Tc to be about 1 K in strained RuO2(100) films grown on TiO2(100) substrates. Nonetheless, the thickness dependence of the Tc follows a similar trend in both cases. Our scanning SQUID measurements reveal a local superfluid response, indicating a 100 mK inhomogeneity in Tc over a 100 μm scale. 

Aberration-corrected Scanning Transmission Electron Microscopy (STEM) has become an essential tool in understanding materials at the atomic scale. However, tuning the aberration corrector to produce a sub-Ångström probe is a complex and time-costly procedure, largely due to the difficulty of precisely measuring the optical state of the system. When measurements are both costly and noisy, Bayesian methods provide rapid and efficient optimization. To this end, we develop a Bayesian approach to fully automate the process by minimizing a new quality metric, beam emittance, which is shown to be equivalent to performing aberration correction. In part I, we derived several important properties of the beam emittance metric and trained a deep neural network to predict beam emittance growth from a single Ronchigram. Here we use this as the black box function for Bayesian optimization and demonstrate automated tuning of simulated and real electron microscopes. We explore different surrogate functions for the Bayesian optimizer and implement a deep neural network kernel to effectively learn the interactions between different control channels without the need to explicitly measure a full set of aberration coefficients. Both simulation and experimental results show the proposed method outperforms conventional approaches by achieving a better optical state with a higher convergence rate. 

Precise alignment of the electron beam is critical for successful application of scanning transmission electron microscopes (STEM) to understanding materials at atomic level. Despite the success of aberration correctors, aberration correction is still a complex process. Here we approach aberration correction from the perspective of accelerator physics and show it is equivalent to minimizing the emittance growth of the beam, the span of the phase space distribution of the probe. We train a deep learning model to predict emittance growth from experimentally accessible Ronchigrams. Both simulation and experimental results show the model can capture the emittance variation with aberration coefficients accurately. We further demonstrate the model can act as a fast-executing function for the global optimization of the lens parameters. Our approach enables new ways to quickly quantify and automate aberration correction that takes advantage of the rapid measurements possible with high-speed electron cameras. In part II of the paper, we demonstrate how the emittance metric enables rapid online tuning of the aberration corrector using Bayesian optimization. 

Abstract Electron ptychography has recently achieved unprecedented resolution, offering valuable insights across diverse material systems, including in three dimensions. However, high-quality ptychographic reconstruction is computationally expensive and time consuming, requiring a significant amount of manually tuning even for experts. Additionally, essential tools for ptychographic analysis are often scattered across multiple software packages, with some advanced features available only in costly commercial software like MATLAB. To address these challenges, we introduce PtyRAD (Ptychographic Reconstruction with Automatic Differentiation), an open-source software framework offers a comprehensive, flexible, and computationally efficient solution for electron ptychography. PtyRAD provides seamless optimization of multiple parameters—such as sample thickness, local tilts, probe positions, and mixed probe and object modes—using gradient-based methods with automatic differentiation. By utilizing PyTorch’s highly optimized tensor operations, PtyRAD achieves up to a 24× speedup in reconstruction time compared to existing packages without compromising image quality. In addition, we propose a real-space depth regularization, which avoids wrap-around artifacts and can be useful for twisted two-dimensional material datasets and vertical heterostructures. Moreover, PtyRAD integrates a Bayesian optimization workflow that streamlines hyperparameter selection. We hope the open-source nature of PtyRAD will foster reproducibility and community-driven development for future advances in ptychographic imaging.