Native surface oxide turns alloyed silicon membranes into nanophononic metamaterials with ultra-low thermal conductivity

A detailed understanding of the relation between microscopic structure and phonon propagation at the nan oscale is essential to design materials with desired phononic and thermal properties.Here we uncover a new mechanism of phonon interaction in surface oxidized membranes, i.e., native oxide layers interact with phonons in ultra-thin silicon membranes through local resonances. The local resonances reduce the low frequency phonon group velocities and shorten their mean free path. This effect opens up a new strategy for ultralow thermal conductivity design as it complements the scattering mechanism which scatters higher frequency modes effectively. The combination of native oxide layer and alloying with germanium in concentration as small as 5% reduces the thermal conductivity of silicon membranes to 100 time lower than the bulk. In addition, the resonance mechanism produced by native oxide surface layers is particularly effective for thermal condutivity reduction even at very low temperatures, at which only low frequency modes are populated.Comment: 6 pages, 5 figures, Accepted for publication in Physical Review

Anisotropic In-Plane Phonon Transport in Ultrathin Silicon Membranes Guided by Nano-Surface-Resonators

Anisotropic phonon transport along different lattice directions of two-dimensional (2D) materials has been observed, however, the effect decreases with increasing the thickness beyond a few atomic layers. Here we establish a novel mechanism to induce anisotropic phonon transport in quasi-2D materials with isotropic symmetry. The phonon propagation is guided by resonance hybridization with surface nanostructures. We demonstrate that the thermal conductivity of 3 nm-thick silicon membrane with surface nanofins is greater by $\sim50\%$ parallel to the fins than that perpendicular to the fins.Comment: 7 pages, 5 figure

Investigation of Phonon Lifetimes and Magnon-Phonon Coupling in YIG/GGG Hybrid Magnonic Systems in the Diffraction Limited Regime

Quantum memories facilitate the storage and retrieval of quantum information for on-chip and long-distance quantum communications. Thus, they play a critical role in quantum information processing (QIP) and have diverse applications ranging from aerospace to medical imaging. It is well established that quantized vibrations (phonons) in mechanical oscillators can behave quantum mechanically and play an important role in QIP. Bulk acoustic wave (BAW) phonons are promising candidates for storing quantum information due to their long lifetimes. In this work, we investigate a hybrid magnonic system, where BAW phonons are excited in a Gadolinium Iron Garnet (GGG) thick film via coupling with quantized electron spin-waves (magnons) in a Yttrium Iron Garnet (YIG) thin film. Recent experiments of a YIG/GGG device show that the memories are limited by the phonon lifetime of 0.2 $\mu$s at room temperature. The phonon lifetime is expected to be longer in the milliKelvin regime. However, the phonon lifetime could be limited by diffraction in that regime, especially for small scale devices. A complete understanding of the diffraction-limited performance of the hybrid magnonic devices is not available. We present theoretical and numerical analyses of the diffraction-limited BAW phonon lifetimes, modeshapes, and their coupling strengths to magnons in planar and confocal high-overtone bulk aoustic wave resonator (HBAR) structures. We utilize Fourier beam propagation and Hankel transform eigenvalue problem approaches and discuss the effectiveness of the two methods to predict the HBAR BAW phonons. Our analyses predicts the diffraction-limited phonon lifetime to be on the order of 100 milliseconds in confocal HBAR structures. We illustrate that a focusing dome could significantly improve the performance of hybrid magnonic HBAR structures, for quantum memory and transduction applications.Comment: 19 pages, 11 figure

First-Principles Prediction of Electronic Transport in Experimental Semiconductor Heterostructures via Physics-Based Machine Learning

First-principles techniques for electronic transport property prediction have seen rapid progress in recent years. However, it remains a challenge to model heterostructures incorporating variability due to fabrication processes. Machine-learning (ML)-based materials informatics approaches (MI) are increasingly used to accelerate design and discovery of new materials with targeted properties, and extend the applicability of first-principles techniques to larger systems. However, few studies exploited MI to learn electronic structure properties and use the knowledge to predict the respective transport coefficients. In this work, we propose an electronic-transport-informatics (ETI) framework that trains on ab initio models of small systems and predicts thermopower of silicon/germanium heterostructures beyond the length-scale accessible with first-principles techniques, matching measured data. We demonstrate application of MI to extract important physics that determines electronic transport in semiconductor heterostructures, breaking from combinatorial strategies pursued especially for thermoelectric materials. We anticipate that ETI would have broad applicability to diverse materials classes.Comment: 14 pages, 7 figure

Optimal thickness of silicon membranes to achieve maximum thermoelectric efficiency: A first principles study

Silicon nanostructures with reduced dimensionality, such as nanowires, membranes, and thin films, are promising thermoelectric materials, as they exhibit considerably reduced thermal conductivity. Here, we utilize density functional theory and Boltzmann transport equation to compute the electronic properties of ultra-thin crystalline silicon membranes with thickness between 1 and 12 nm. We predict that an optimal thickness of ∼7 nm maximizes the thermoelectric figure of merit of membranes with native oxide surface layers. Further thinning of the membranes, although attainable in experiments, reduces the electrical conductivity and worsens the thermoelectric efficiency