Spatially arranged surfaces on the micro-rod structure, which was three-dimensionally (3D) architected on a Si(110) substrate have been thoroughly investigated by a system with micro-beam reflection high-energy electron diffraction (μ-RHEED) and scanning electron microscopy (SEM). The combination of μ-RHEED and SEM realized analytical structure investigation of 3D surfaces with the spatial resolution of sub micrometer for the 3D rectangular shaped rod consisting of a (110) top surface (20 μm wide) and {111} vertical side surfaces (10 μm wide). Exhaustive mapping revealed the peculiar reconstructed surface structures: Si(110) “16 × 2” single domain and {35 47 7} facet surfaces locally appeared on the interconnected edge region on the 3D structure in addition to the “16 × 2” and 7 × 7 super structures on flat top (110) and side {111} surfaces, respectively. The formation mechanism for “16 × 2” single-domain structure near the corner edge of the (110) surfaces and {35 47 7} facets on the corner edges between (110) and {111} surfaces were discussed from the viewpoint of the surface stability on the 3D geometrical shaped Si structure.

Extended X-ray absorption fine structure (EXAFS) plays an important role in the surface science and nanotechnology to characterize the non-crystalline materials using the curve fitting (CF) analysis. However, the CF has problems such as correlation between the structural parameters, the dependence on initial parameters, and the limitation of degree of freedom when EXAFS is applied to the complex system. In this paper, we propose a thorough search (TS) method to solve these problems. We analyzed EXAFS data for molybdenum oxide (α-MoO₃) using the TS method. MO₃ possesses a well-defined but complex local structure in which a central molybdenum (Mo) atom is surrounded by six oxygen (O) atoms. In CF analysis, the correlations of these six Mo—O bonds make it very difficult to derive reliable structural parameters from EXAFS data. In the TS analysis, the structural parameters regarded as a point (𝒫) were surveyed thoroughly over a certain range. The goodness of fit was evaluated by R-factor. All 𝒫 with R-factors less than a certain value were accepted. The accepted points 𝒫 made a domain in which it was assumed that all points 𝒫 in the domain should occur with equal probability and consequently their averages were used as representative structural parameters. If multiple independent domains were obtained, they were all regarded as possible candidates and the TS analysis provided possible structural parameters. The feasibility and advantages of the TS method were compared with the CF analysis and the micro reverse Monte Carlo method.

In the Internet of Things (IoT) era using Big Data, metrology is recognized as a crucial process that provides added value in hyper-scaling semiconductor manufacturing processes. Miniaturization of semiconductors requires the discussion of quantum theory on the order of tens of nanometers, and metrology (measurement technology) that supports this requirement has the potential of creating new research fields. Super-resolution optical technology is a common measurement technique that exceeds the physical limit. Moreover, advanced integrated metrology techniques, which include a combination of various kinds of metrology techniques coupled with artificial intelligence (AI) and machine learning (ML), have the potential to evolve into an untapped technological field required by the market. We conduct extensive discussions on the implications of AI/ML. A new way of advanced integrated metrology can be considered as an important role for the fabrication of next generation integrated circuit and be connected to value-added creation.

Polymer electrolyte fuel cells (PEFCs) are a clean, sustainable device to convert chemical energy to electricity and can provide power for automobiles, trains, and ships. In PEFCs, the oxygen reduction reaction (ORR) occurs at the cathode and is catalyzed at electrocatalysts. The activity of ORR electrocatalysts is known to limit the overall performance of PEFCs because the ORR is more sluggish than the hydrogen oxidation reaction at the anode. In the state-of-the-art PEFC, platinum group metal (PGM)-based ORR electrocatalysts are used. Since PGMs are rare and expensive, highly active and durable non-PGM ORR electrocatalysts are required for widespread applications of PEFCs. In nature, metalloenzymes such as cytochrome c oxidase and multicopper oxidases efficiently catalyze the ORR and utilize multinuclear iron and/or copper complexes as active sites. The structure of these active sites and enzyme reaction mechanisms would give us design concepts of artificial non-PGM electrocatalysts for the ORR, possibly leading us to develop next-generation non-PGM electrocatalysts. Herein, recent research progress on understanding enzymatic ORR reaction mechanisms and developing non-PGM ORR electrocatalysts is reviewed from the viewpoint of bio-inspired approaches.

Photoelectron spectroscopy is our main tool to explore the electronic structure of novel material systems, the properties of which are often determined by an intricate interplay of competing interactions. Elucidating the role of this interactions requires studies over an extensive range of energy, momentum, length, and time scales. We show that immersion lens-based momentum microscopy with spin-resolution is able to combine these seemingly divergent requirements in a unifying experimental approach. We will discuss applications to different areas in information research, for example, resistive switching and spintronics. The analysis of resistive switching phenomena in oxides requires high lateral resolution and chemical selectivity, as the processes involve local redox processes and oxygen vacancy migration. In spintronics topological phenomena are currently a hot topic, which lead to complex band structures and spin textures in reciprocal space. Spin-resolved momentum microscopy is uniquely suited to address these aspects.