Multiple-valued logic-in-memory VLSI based on a floating-gate-MOS pass-transistor network

科研費報告書収録論文(課題番号:09558027・基盤研究(B)(2)・H9～H12/研究代表者:羽生, 貴弘/1トランジスタセル多値連想メモリの試作とその応用

Rare Helium-Bearing Compound FeO2He Stabilized at Deep-Earth Conditions

There is compelling geochemical evidence for primordial helium trapped in Earth’s lower mantle, but the origin and nature of the helium source remain elusive due to scarce knowledge on viable helium-bearing compounds that are extremely rare. Here we explore materials physics underlying this prominent challenge. Our structure searches in conjunction with first-principles energetic and thermodynamic calculations uncover a remarkable helium-bearing compound FeO2He at high pressure-temperature conditions relevant to the core-mantle boundary. Calculated sound velocities consistent with seismic data validate FeO2He as a feasible constituent in ultralow velocity zones at the lowermost mantle. These mutually corroborating findings establish the first and hitherto only helium-bearing compound viable at pertinent geophysical conditions, thus providing vital physics mechanisms and materials insights for elucidating the enigmatic helium reservoir in deep Earth

Pressure-induced structural modulations in coesite

Silica phases, SiO2, have attracted significant attention as important phases in the fields of condensed-matter physics, materials science, and (in view of their abundance in the Earth's crust) geoscience. Here, we experimentally and theoretically demonstrate that coesite undergoes structural modulations under high pressure. Coesite transforms to a distorted modulated structure, coesite-II, at 22–25 GPa with modulation wave vector q=0.5b∗. Coesite-II displays further commensurate modulation along the y axis at 36–40 GPa and the long-range ordered crystalline structure collapses beyond ∼40GPa and starts amorphizing. First-principles calculations illuminate the nature of the modulated phase transitions of coesite and elucidate the modulated structures of coesite caused by modulations along the y-axis direction. The structural modulations are demonstrated to result from phonon instability, preceding pressured-induced amorphization. The recovered sample after decompression develops a rim of crystalline coesite structure, but its interior remains low crystalline or partially amorphous. Our results not only clarify that the pressure-induced reversible phase transitions and amorphization in coesite originate from structural modulations along the y-axis direction, but also shed light on the densification mechanism of silica under high pressure

Carbon network evolution from dimers to sheets in superconducting ytrrium dicarbide under pressure

Carbon-bearing compounds display intriguing structural diversity, due to variations in hybrid bonding of carbon. Here, first-principles calculations and unbiased structure searches on yttrium dicarbide at pressure reveal four new structures with varying carbon polymerisation, in addition to the experimentally observed high-temperature low-pressure I4/mmm dimer phase. At low pressures, a metallic C2/m phase (four-member single-chain carbide) is stable, which transforms into a Pnma phase (single-chain carbide) upon increasing pressure, with further transformation to an Immm structure (double-chain carbide) at 54 GPa and then to a P6/mmm phase (sheet carbide) at 267 GPa. Yttrium dicarbide is structurally diverse, with carbon bonded as dimers (at lowest pressure), four-member single chains, infinite single chains, double chains and eventually sheet structures on compression. Electron–phonon coupling calculations indicate that the high-pressure phases are superconducting. Our results aid the understanding and design of new superconductors and illuminate pressure-induced carbon polymerisation in carbides

Digital Modeling on Large Kernel Metamaterial Neural Network

Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power consumption, which are critical limitations in applications such as the Internet of Things (IoT), edge computing, and the usage of drones. Recent advances in optical computational units (e.g., metamaterial) have shed light on energy-free and light-speed neural networks. However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication. Moreover, the unique advantages of MNN's (e.g., light-speed computation) are not fully explored via standard 3x3 convolution kernels. In this paper, we propose a novel large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN with model re-parametrization and network compression, while also considering the optical limitation explicitly. The new digital learning scheme can maximize the learning capacity of MNN while modeling the physical restrictions of meta-optic. With the proposed LMNN, the computation cost of the convolutional front-end can be offloaded into fabricated optical hardware. The experimental results on two publicly available datasets demonstrate that the optimized hybrid design improved classification accuracy while reducing computational latency. The development of the proposed LMNN is a promising step towards the ultimate goal of energy-free and light-speed AI

Quantum structural fluxion in superconducting lanthanum polyhydride

The discovery of 250-kelvin superconducting lanthanum polyhydride under high pressure marked a significant advance toward the realization of a room‐temperature superconductor. X-ray diffraction (XRD) studies reveal a nonstoichiometric LaH9.6 or LaH10±δ polyhydride responsible for the superconductivity, which in the literature is commonly treated as LaH10 without accounting for stoichiometric defects. Here, we discover significant nuclear quantum effects (NQE) in this polyhydride, and demonstrate that a minor amount of stoichiometric defects will cause quantum proton diffusion in the otherwise rigid lanthanum lattice in the ground state. The diffusion coefficient reaches ~10−7 cm2/s in LaH9.63 at 150 gigapascals and 240 kelvin, approaching the upper bound value of interstitial hydrides at comparable temperatures. A puzzling phenomenon observed in previous experiments, the positive pressure dependence of the superconducting critical temperature Tc below 150 gigapascals, is explained by a modulation of the electronic structure due to a premature distortion of the hydrogen lattice in this quantum fluxional structure upon decompression, and resulting changes of the electron-phonon coupling. This finding suggests the coexistence of the quantum proton fluxion and hydrogen-induced superconductivity in this lanthanum polyhydride, and leads to an understanding of the structural nature and superconductivity of nonstoichiomectric hydrogen-rich materials.The project is supported by the National Natural Science Foundation of China (Grant No. 11974135, 11874176, 12174170, and 12074138), the Natural Sciences and Engineering Research Council of Canada, the EPSRC through grants EP/P022596/1, and EP/S021981/1, and the startup funds of the office of the Dean of SASN of Rutgers University-Newark. P. T. S. thanks the Department of Materials Science and Metallurgy at the University of Cambridge for generous funding. The work of P. T. S. is further supported through a Trinity Hall research studentship. I. E. acknowledges financial support by the European Research Council (ERC) under the EuropeanUnion’sHorizon 2020 research and innovation program (grant agreement no. 802533)

Route to high-energy density polymeric nitrogen t-N via He−N compounds

Polymeric nitrogen, stabilized by compressing pure molecular nitrogen, has yet to be recovered to ambient conditions, precluding its application as a high-energy density material. Here we suggest a route for synthesis of a tetragonal polymeric nitrogen, denoted t-N, via He-N compounds at high pressures. Using first-principles calculations with structure searching, we predict a class of nitrides with stoichiometry HeN4 that are energetically stable (relative to a mixture of solid He and N2) above 8.5 GPa. At high pressure, HeN4 comprises a polymeric channel-like nitrogen framework filled with linearly arranged helium atoms. The nitrogen framework persists to ambient pressure on decompression after removal of helium, forming pure polymeric nitrogen, t-N. t-N is dynamically and mechanically stable at ambient pressure with an estimated energy density of ~11.31 kJ/g, marking it out as a remarkable high-energy density material. This expands the known polymeric forms of nitrogen and indicates a route to its synthesis