Ultralow-dielectric-constant amorphous boron nitride.

Decrease in processing speed due to increased resistance and capacitance delay is a major obstacle for the down-scaling of electronics1-3. Minimizing the dimensions of interconnects (metal wires that connect different electronic components on a chip) is crucial for the miniaturization of devices. Interconnects are isolated from each other by non-conducting (dielectric) layers. So far, research has mostly focused on decreasing the resistance of scaled interconnects because integration of dielectrics using low-temperature deposition processes compatible with complementary metal-oxide-semiconductors is technically challenging. Interconnect isolation materials must have low relative dielectric constants (κ values), serve as diffusion barriers against the migration of metal into semiconductors, and be thermally, chemically and mechanically stable. Specifically, the International Roadmap for Devices and Systems recommends4 the development of dielectrics with κ values of less than 2 by 2028. Existing low-κ materials (such as silicon oxide derivatives, organic compounds and aerogels) have κ values greater than 2 and poor thermo-mechanical properties5. Here we report three-nanometre-thick amorphous boron nitride films with ultralow κ values of 1.78 and 1.16 (close to that of air, κ = 1) at operation frequencies of 100 kilohertz and 1 megahertz, respectively. The films are mechanically and electrically robust, with a breakdown strength of 7.3 megavolts per centimetre, which exceeds requirements. Cross-sectional imaging reveals that amorphous boron nitride prevents the diffusion of cobalt atoms into silicon under very harsh conditions, in contrast to reference barriers. Our results demonstrate that amorphous boron nitride has excellent low-κ dielectric characteristics for high-performance electronics.

In Situ Scanning Transmission Electron Microscopy Study of MoS2 Formation on Graphene with a Deep-Learning Framework.

Atomic-scale information is essential for understanding and designing unique structures and properties of two-dimensional (2D) materials. Recent developments in in situ transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) enable research to provide abundant insights into the growth of nanomaterials. In this study, 2D MoS2 is synthesized on a suspended graphene substrate inside a TEM column through thermolysis of the ammonium tetrathiomolybdate (NH4)2MoS4 precursor at 500 °C. To avoid misinterpretation of the in situ STEM images, a deep-learning framework, DeepSTEM, is developed. The DeepSTEM framework successfully reconstructs an object function in atomic-resolution STEM imaging for accurate determination of the atomic structure and dynamic analysis. In situ STEM imaging with DeepSTEM enables observation of the edge configuration, formation, and reknitting progress of MoS2 clusters with the formation of a mirror twin boundary. The synthesized MoS2/graphene heterostructure shows various twist angles, as revealed by atomic-resolution TEM. This deep-learning framework-assisted in situ STEM imaging provides atomic information for in-depth studies on the growth and structure of 2D materials and shows the potential use of deep-learning techniques in 2D material research.

Contrast Transfer Function-Based Exit-Wave Reconstruction and Denoising of Atomic-Resolution Transmission Electron Microscopy Images of Graphene and Cu Single Atom Substitutions by Deep Learning Framework.

The exit wave is the state of a uniform plane incident electron wave exiting immediately after passing through a specimen and before the atomic-resolution transmission electron microscopy (ARTEM) image is modified by the aberration of the optical system and the incoherence effect of the electron. Although exit-wave reconstruction has been developed to prevent the misinterpretation of ARTEM images, there have been limitations in the use of conventional exit-wave reconstruction in ARTEM studies of the structure and dynamics of two-dimensional materials. In this study, we propose a framework that consists of the convolutional dual-decoder autoencoder to reconstruct the exit wave and denoise ARTEM images. We calculated the contrast transfer function (CTF) for real ARTEM and assigned the output of each decoder to the CTF as the amplitude and phase of the exit wave. We present exit-wave reconstruction experiments with ARTEM images of monolayer graphene and compare the findings with those of a simulated exit wave. Cu single atom substitution in monolayer graphene was, for the first time, directly identified through exit-wave reconstruction experiments. Our exit-wave reconstruction experiments show that the performance of the denoising task is improved when compared to the Wiener filter in terms of the signal-to-noise ratio, peak signal-to-noise ratio, and structural similarity index map metrics.

van der Waals Epitaxial Formation of Atomic Layered α-MoO3 on MoS2 by Oxidation.

The electronic, catalytic, and optical properties of transition metal dichalcogenides (TMDs) are significantly affected by oxidation, and using oxidation to tune the properties of TMDs has been actively explored. In particular, because transition metal oxides (TMOs) are promising hole injection layers, a TMD-TMO heterostructure can be potentially applied as a p-type semiconductor. However, the oxidation of TMDs has not been clearly elucidated because of the structural instability and the extremely small quantity of oxides formed. Here, we reveal the phases and morphologies of oxides formed on two-dimensional molybdenum disulfide (MoS2) using transmission electron microscopy analysis. We find that MoS2 starts to oxidize around 400 °C to form orthorhombic-phase molybdenum trioxide (α-MoO3) nanosheets. The α-MoO3 nanosheets so formed are stacked layer-by-layer on the underlying MoS2 via van der Waals interaction and the nanosheets are aligned epitaxially with six possible orientations. Furthermore, the band gap of MoS2 is increased from 1.27 to 3.0 eV through oxidation. Our study can be extended to most TMDs to form TMO-TMD heterostructures, which are potentially interesting as p-type transistors, gas sensors, or photocatalysts.

Spontaneous Formation of a ZnO Monolayer by the Redox Reaction of Zn on Graphene Oxide.

Graphene-based two-dimensional heterostructures are of substantial interest both for fundamental studies and their various potential applications. Particularly interesting are atomically thin semiconducting oxides on graphene, which uniquely combine a wide band gap and optical transparency. Here, we report the atomic-scale investigation of a novel self-formation of a ZnO monolayer from the Zn metal on a graphene oxide substrate. The spontaneous oxidation of the ultrathin Zn metal occurs by a reaction with oxygen supplied from the graphene oxide substrate, and graphene oxide is deoxygenated by a transfer of oxygen from O-containing functional groups to the zinc metal. The ZnO monolayer formed by this spontaneous redox reaction shows a graphene-like structure and a band gap of about 4 eV. This study demonstrates a unique and straightforward synthetic route to atomically thin two-dimensional heterostructures made from a two-dimensional metal oxide and graphene, formed by the spontaneous redox reaction of a very thin metal layer directly deposited on graphene oxide.