Primitive materials formed in the Protosolar System have been remained in astromaterials. The primitive materials record astrophenomena which occurred when the Solar System born at ～4.6 billion years ago. The physicochemical environments and the evolution can be clarified by microanalyses of the primitive materials, e.g., chemical and isotopic compositions of composed minerals. In this report, I explain how clarify origin and evolution of solar system using the oldest rock in the solar system, CAIs, for example, by multiple analysis using characteristic X-ray and electron backscatter diffraction of scanning electron microscopy, stable and radio isotopes by secondary ion mass spectrometry/isotope microscopy, and CAI-formation experiments in laboratory simulated Protosolar system environments.

We introduce the steep subthreshold slope (SS) device “PN-Body Tied SOI-FET (PNBT-FET),” as we proposed at IEDM 2015. It has SS ＜ 1 mV/decade over several order drain currents, even with ultralow drain voltage. Currently, it is considered to be one of the ideal steep slope devices ever reported. We also introduce its application to CMOS, RF energy harvesting, and also a neuromorphic device. NMOS/PMOS PNBT-FET, CMOS inverter, 10 mV rectification, single device neuron function have been demonstrated.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS), which detects chemical information of a solid sample at the most upper surface less than 2 nm and chemical images with a high spatial resolution approximately 100 nm, is widely applied to analyze biological samples, organic materials, and electronic materials. Because the TOF-SIMS spectra and image data are generally very complicated, numerical analysis methods are often required to interpret TOF-SIMS data. Multivariate analysis such as principal component analysis has successfully been applied to TOF-SIMS data interpretation. Recently, sparse modeling and machine learning, which are now applied to many fields, are also employed for TOF-SIMS data analysis. In this article one of the latest examples of machine learning application to TOF-SIMS data is introduced.

The thermal oxidation process of Si consists of four processes: (1) diffusion of oxidizing molecules in the oxide film, (2) reaction of oxidizing molecules and Si at the interface, (3) generation and transport of interstitial Si atoms at the interface associated with the reaction, and (4) volume expansion, viscous flow and deformation of the oxidized region. Such complexity is not noticeable in the thermal oxidation of flat Si, and at first glance it seems that it can be easily understood only by (1) and (2). But in the thermal oxidation of Si three-dimensional nanostructures, the complexity is immediately revealed. We introduce the overall microscopic picture of thermal oxidation process mainly based on our research.

In order to deposit high-quality transparent conductive oxide (TCO) films, the crystallinity and stoichiometry of the films should be optimized precisely. The dc sputter deposition processes using the slightly reduced targets or using metallic targets with the specially designed feed-back systems enable us to deposit the low resistivity TCO films stably with high reproducibility. In such reactive sputtering processes, the feed-back systems of discharge impedance or plasma emission intensity combined with mid-frequency pulsing (50 kHz) are quite effective. The various high-quality TCOs, such as Sn-doped In₂O₃ (ITO), Al-doped ZnO (AZO), Nb-doped TiO₂ (NTO), Sb or Ta-doped SnO₂ (ATO or TTO) films can be successfully deposited using In-Sn, Zn-Al, Ti-Nb, Sn-(Sb or Ta) alloy targets, respectively.

Plasma etching involves various reactive ions and neutral species generated in plasmas. A better understanding of surface etching reactions with such species enables highly controlled fabrication of micro/nano-structures on surfaces of various materials. To develop such process technologies, it is crucial to obtain fundamental data on the interactions of various incident ions and radicals with the surfaces of interest and to establish surface reaction models based on such data. In this article, the methods to obtain such data with the use of mass-selected reactive ion beam experiments, numerical simulation, and machine learning techniques are discussed.

To realize a decarbonized society, we human beings need to reconsider our energy use. Considering this historical background, we have serialized “Energy Technology Now and in the Future ― The World of Power Generation, Transmission, and Storage ―” as part of the Fundamental Lecture 2021. The purpose of this series is for readers to understand energy from a wide range of perspectives. In this article, which is the last of the series, we will look back on the series from the perspective of the editorial committee in charge of planning the same. After describing the structure of the series and the position of each article, each of their points will be introduced.