分子束外延
声子
材料科学
光电子学
等离子体
外延
纳米技术
凝聚态物理
物理
图层(电子)
量子力学
作者
Devki N. Talwar,Li‐Chyong Chen,Kuei‐Hsien Chen,Zhe Chuan Feng
出处
期刊:Nanomaterials
[MDPI AG]
日期:2025-02-14
卷期号:15 (4): 291-291
被引量:1
摘要
The narrow bandgap InN material, with exceptional physical properties, has recently gained considerable attention, encouraging many scientists/engineers to design infrared photodetectors, light-emitting diodes, laser diodes, solar cells, and high-power electronic devices. The InN/Sapphire samples of different film thicknesses that we have used in our methodical experimental and theoretical studies are grown by plasma-assisted molecular-beam epitaxy. Hall effect measurements on these samples have revealed high-electron-charge carrier concentration, η. The preparation of InN epifilms is quite sensitive to the growth temperature T, plasma power, N/In ratio, and pressure, P. Due to the reduced distance between N atoms at a higher P, one expects the N-flow kinetics, diffusion, surface components, and scattering rates to change in the growth chamber which might impact the quality of InN films. We believe that the ionized N, rather than molecular, or neutral species are responsible for controlling the growth of InN/Sapphire epifilms. Temperature- and power-dependent photoluminescence measurements are performed, validating the bandgap variation (~0.60–0.80 eV) of all the samples. High-resolution X-ray diffraction studies have indicated that the increase in growth temperature caused the perceived narrow peaks in the X-ray-rocking curves, leading to better-quality films with well-ordered crystalline structures. Careful simulations of the infrared reflectivity spectra provided values of η and mobility μ, in good accordance with the Hall measurements. Our first-order Raman scattering spectroscopy study has not only identified the accurate phonon values of InN samples but also revealed the low-frequency longitudinal optical phonon plasmon-coupled mode in excellent agreement with theoretical calculations.
科研通智能强力驱动
Strongly Powered by AbleSci AI