Germanium alloyed with α-tin (GeSn) transitions to a direct bandgap semiconductor of significance for optoelectronics. It is essential to localize the carriers within the active region for improving the quantum efficiency in a GeSn based laser. In this work, epitaxial GeSn heterostructure material systems were analyzed to determine the band offsets for carrier confinement: (i) a 0.53% compressively strained Ge 0.97 Sn 0.03 /AlAs; (ii) a 0.81% compressively strained Ge 0.94 Sn 0.06 /Ge; and (iii) a lattice matched Ge 0.94 Sn 0.06 /In 0.12 Al 0.88 As. The phonon modes in GeSn alloys were studied using Raman spectroscopy as a function of Sn composition, that showed Sn induced red shifts in wavenumbers of the Ge–Ge longitudinal optical phonon mode peaks. The material parameter b representing strain contribution to Raman shifts of a Ge 0.94 Sn 0.06 alloy was determined as b = 314.81 ± 14 cm −1 . Low temperature photoluminescence measurements were performed at 79 K to determine direct and indirect energy bandgaps of E g,Γ = 0.72 eV and E g,L = 0.66 eV for 0.81% compressively strained Ge 0.94 Sn 0.06 , and E g,Γ = 0.73 eV and E g,L = 0.68 eV for lattice matched Ge 0.94 Sn 0.06 epilayers. Chemical effects of Sn atomic species were analyzed using X-ray photoelectron spectroscopy (XPS), revealing a shift in Ge 3d core level (CL) spectra towards the lower binding energy affecting the bonding environment. Large valence band offset of Δ E V = 0.91 ± 0.1 eV and conduction band offset of Δ E C,Γ–X = 0.64 ± 0.1 eV were determined from the Ge 0.94 Sn 0.06 /In 0.12 Al 0.88 As heterostructure using CL spectra by XPS measurements. The evaluated band offset was found to be of type-I configuration, needed for carrier confinement in a laser. In addition, these band offset values were compared with the first-principles-based calculated Ge/InAlAs band alignment, and it was found to have arsenic up-diffusion limited to 1 monolayer of epitaxial GeSn overlayer, ruling out the possibility of defects induced modification of band alignment. Furthermore, this lattice matched GeSn/InAlAs heterostructure band offset values were significantly higher than GeSn grown on group IV buffer/substrates. Therefore, a lattice matched GeSn/InAlAs material system has large band offsets offering superior carrier confinement to realize a highly efficient GeSn based photonic device.
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Developing Potential Energy Surfaces for Graphene-Based 2D–3D Interfaces From Modified High-Dimensional Neural Networks for Applications in Energy Storage
Abstract Designing a new heterostructure electrode has many challenges associated with interface engineering. Demanding simulation resources and lack of heterostructure databases continue to be a barrier to understanding the chemistry and mechanics of complex interfaces using simulations. Mixed-dimensional heterostructures composed of two-dimensional (2D) and three-dimensional (3D) materials are undisputed next-generation materials for engineered devices due to their changeable properties. The present work computationally investigates the interface between 2D graphene and 3D tin (Sn) systems with density functional theory (DFT) method. This computationally demanding simulation data is further used to develop machine learning (ML)-based potential energy surfaces (PES). The approach to developing PES for complex interface systems in the light of limited data and the transferability of such models has been discussed. To develop PES for graphene-tin interface systems, high-dimensional neural networks (HDNN) are used that rely on atom-centered symmetry function to represent structural information. HDNN are modified to train on the total energies of the interface system rather than atomic energies. The performance of modified HDNN trained on 5789 interface structures of graphene|Sn is tested on new interfaces of the same material pair with varying levels of structural deviations from the training dataset. Root-mean-squared error (RMSE) for test interfaces fall in the range of 0.01–0.45 eV/atom, depending on the structural deviations from the reference training dataset. By avoiding incorrect decomposition of total energy into atomic energies, modified HDNN model is shown to obtain higher accuracy and transferability despite a limited dataset. Improved accuracy in the ML-based modeling approach promises cost-effective means of designing interfaces in heterostructure energy storage systems with higher cycle life and stability.
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- Award ID(s):
- 1911900
- NSF-PAR ID:
- 10347534
- Date Published:
- Journal Name:
- Journal of Electrochemical Energy Conversion and Storage
- Volume:
- 19
- Issue:
- 4
- ISSN:
- 2381-6872
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4054781
