arXiv subjects

Yongsup Park

Publications and source records attributed to Yongsup Park.

4 recordsLinked to original sources

arXiv2026

SpectraFit XPS: a free, browser-based application for X-ray photoelectron spectroscopy peak fitting and quantification

SpectraFit XPS is a free web application for the analysis of X-ray photoelectron spectroscopy (XPS) data that runs entirely in the browser, with no installation and no server-side processing: spectra never leave the user's computer. It combines an interactive Levenberg--Marquardt fitting engine with true Voigt, Doniach--Šunjić, and summed Gaussian--Lorentzian line shapes; Shirley (static and dynamic), linear, and Tougaard (two- and four-parameter) backgrounds; spin--orbit doublet generation; and expression-based inter-peak constraints. Quantitative analysis supports ten relative-sensitivity-factor sets, several kinetic-energy correction schemes including TPP-2M inelastic mean free paths, angular correction, and atomic and weight percentages, with the underlying database and algorithms adapted from the open-source KherveFitting project. The application reads and writes ISO~14976 (VAMAS) files---including multi-region and depth-profile data and load-time transmission-function correction---and imports common vendor and spreadsheet formats. A publication plotter exports journal-ready SVG/PNG figures. The quantification pipeline reproduces KherveFitting reference values exactly in a 30-case regression suite, part of an automated test set of 100 tests. The application, an illustrated bilingual user guide, and a synthetic demonstration dataset are freely available at https://spectrafit-xps.web.app/.

cond-mat.mtrl-sci↗

arXiv2024

Color Centers in Hexagonal Boron Nitride

Atomically thin two-dimensional (2D) hexagonal boron nitride (hBN) has emerged as an essential material for the encapsulation layer in van der Waals heterostructures and efficient deep ultra-violet optoelectronics. This is primarily due to its remarkable physical properties and ultrawide bandgap (close to 6 eV, and even larger in some cases) properties. Color centers in hBN refer to intrinsic vacancies and extrinsic impurities within the 2D crystal lattice, which result in distinct optical properties in the ultraviolet (UV) to near-infrared (IR) range. Furthermore, each color center in hBN exhibits a unique emission spectrum and possesses various spin properties. These characteristics open up possibilities for the development of next-generation optoelectronics and quantum information applications, including room-temperature single-photon sources and quantum sensors. Here, we provide a comprehensive overview of the atomic configuration, optical and quantum properties, and different techniques employed for the formation of color centers in hBN. A deep understanding of color centers in hBN allows for advances in the development of next-generation UV optoelectronic applications, solid-state quantum technologies, and nanophotonics by harnessing the exceptional capabilities offered by hBN color centers.

physics.app-ph↗

arXiv2023

Trap-limited electrical properties of organic semiconductor devices

We investigated the electrical properties of a unipolar organic device with traps that were intentionally inserted into a particular position in the device. Depending on their inserted position, the traps significantly alter the charge distribution and the resulting electric field as well as the charge transport behavior in the device. In particular, as the traps are situated closer to a charge-injection electrode, the band bending of a trap-containing organic layer occurs more strongly so that it effectively imposes a higher charge injection barrier. We propose an electrical model that fully accounts for the observed change in the electrical properties of the device with respect to the trap position.

physics.app-ph↗

arXiv2022

Lightweight Image Enhancement Network for Mobile Devices Using Self-Feature Extraction and Dense Modulation

Convolutional neural network (CNN) based image enhancement methods such as super-resolution and detail enhancement have achieved remarkable performances. However, amounts of operations including convolution and parameters within the networks cost high computing power and need huge memory resource, which limits the applications with on-device requirements. Lightweight image enhancement network should restore details, texture, and structural information from low-resolution input images while keeping their fidelity. To address these issues, a lightweight image enhancement network is proposed. The proposed network include self-feature extraction module which produces modulation parameters from low-quality image itself, and provides them to modulate the features in the network. Also, dense modulation block is proposed for unit block of the proposed network, which uses dense connections of concatenated features applied in modulation layers. Experimental results demonstrate better performance over existing approaches in terms of both quantitative and qualitative evaluations.

eess.IV↗