Last updated: 09 October 2026

Dong-Joon Yi

Ph.D. student, Department of Electronic Engineering, Hanyang University

Semiconductor Nanophotonics Laboratory | Advisor: Prof. Mun Seok Jeong

iverydj@hanyang.ac.kr | iver.ydj@gmail.com | iverydj.github.io | Google Scholar | github.com/iverydj

Research Interests

  • Study physics in solid-state materials: how defects & quasiparticles affect their electrical and optical properties.
  • Analyze defect states and local structure with transient spectroscopy (PICTS/DLTS) and tip-enhanced nanospectroscopy.
  • Develop and apply interpretable machine learning to extract physically meaningful information from experimental data.

Education

Ph.D. student in Electronic Engineering (GPA 4.3 / 4.5) Mar 2024 – present (expected Aug 2027)
Department of Electronic Engineering, Hanyang University Seoul, Republic of Korea
  • Semiconductor Nanophotonics Laboratory
  • Advisor: Prof. Mun Seok Jeong
M.S. in Solid-state Physics (GPA 4.5 / 4.5) Sep 2022 – Feb 2024
Department of Physics, Dongguk University Seoul, Republic of Korea
  • Advanced Physics on Electromaterial Laboratory
  • Advisor: Prof. Kwun-Bum Chung
  • Thesis: “Performance Control of InSnZnO/InGaZnO Multi-Channel-Layered Transistors with Defect Analysis of Amorphous Oxide Semiconductors”
B.S. in Physics (GPA 4.2 / 4.5) Mar 2016 – Aug 2022
Department of Physics, Dongguk University Seoul, Republic of Korea
  • Undergraduate researcher, Advanced Physics on Electromaterial Laboratory (Jul 2021 – Aug 2022)
  • Military service, Republic of Korea (Jun 2019 – Apr 2021)

Research Fellowship

Principal Investigator Sep 2025 – Aug 2027
Doctoral Research Fellowship, Basic Science Research Program, National Research Foundation of Korea (NRF) KRW 50 million (total, 2 years)
  • Project: “Deep-Level Transient Spectroscopy and Nanospectroscopy for Ultrafine Defect Analysis Using Explainable AI in 2D Quantum Semiconductors”
  • Funded by the Ministry of Education through the National Research Foundation of Korea (NRF).

Research

1. Defect Physics in Solid-State Materials

2. Defect and Nanoscale Spectroscopy

  • Transient spectroscopy (PICTS/DLTS) of defect states in 2D semiconductors.
  • Nanoscale mapping of defects and strain in 2D materials by tip-enhanced spectroscopy.
  • Electromagnetic simulation of near-field enhancement in tip-enhanced spectroscopy. (Mater. Des. 2026)

3. Interpretable AI for Science

  • Interpretable machine learning relating Raman spectra to electrical properties.
  • Deep-learning noise reduction of experimental spectra that preserves spectral parameters.
  • Simulation of analog in-memory learning based on measured synaptic characteristics of ferroelectric InSe neuromorphic devices.
  • Numerical precision and the reliability of scientific machine learning. (ICML 2026)
  • Review of the physical interpretability of machine-learning analysis in spectroscopy.

Publications

First†, Corresponding*

  • T. G. Weldemhret†, D.-J. Yi†, K. Jeong, K.-B. Chung*, “Evaluation of the high mobility and stability of InGaZnO/InSnZnO bilayer thin-film transistors via quantitative defect analysis,” Surfaces and Interfaces, 71, 106870 (2025), DOI:10.1016/j.surfin.2025.106870
  • S. Bang†, D. Choi†, H. C. Suh†, C. Lee, S. Lim, Y. J. Yu, D. H. Kim, S. H. Kim, J. Yoo, T. Kim, D. Lee, H. J. Park, U. J. Kim, D.-J. Yi, H. Oh*, D.-W. Kim*, M. S. Jeong*, “Ultrawide Charge-Trap Memory Window and Photoinduced Synaptic Behavior in p-Channel Amorphous Oxide Semiconductors,” Small, e74713 (2026), DOI:10.1002/smll.74713
  • Y. Cho†, J. Yoo†, S. Yang, D.-J. Yi, S. M. Lee, M. S. Jeong*, J. Choo*, “Position: Significant impact of numerical precision in scientific machine learning,” International Conference on Machine Learning (ICML), Position Paper Track, PMLR 306 (2026), PDF
  • H. C. Suh†, T. Kim†, D.-J. Yi, D. H. Kim, S. H. Kim, J. Yoo, S. Bang, D. Lee, J. H. Kim, Y. S. Won, K. K. Kim, M. S. Jeong*, “Prediction and assessment of nanoprobe for tip-enhanced Raman spectroscopy: Data-driven artificial intelligence approach,” Materials & Design, 266, 116066 (2026), DOI:10.1016/j.matdes.2026.116066
  • S. Bang†, C. Lee†, D. Choi, D. Y. Park, D. H. Kim, D. Lee, D.-J. Yi, J. Song, S. J. Yun, D.-W. Kim*, M. S. Jeong*, “High Performance P-Channel Transistor Based on Amorphous Tellurium Trioxide,” Advanced Materials, 37, 2504948 (2025), DOI:10.1002/adma.202504948
  • D. H. Kim†, J. Yoo, H. C. Suh, Y. S. Won, S. H. Kim, D.-J. Yi, B. G. Jeong, C. Lee, D. Lee, K. K. Kim, S. M. Lee, E. K. Koh, M. S. Jeong*, “Anomalous phonon softening with inherent strain in wrinkled monolayer WSe2,” Advanced Materials, 37, 2419414 (2025), DOI:10.1002/adma.202419414
  • K. Jeong†, D. Y. Shin, J.-M. Park, D.-J. Yi, H. Hong, H.-S. Kim*, K.-B. Chung*, “Noncontact Monitoring and Imaging of the Operation and Performance of Thin-Film Field-Effect Transistors,” Advanced Science, 12, 2407923 (2025), DOI:10.1002/advs.202407923
  • A. Song†, M. J. Kim†, D.-J. Yi, S. Kwon, D.-W. Kim, S. Kim, J.-H. Bae, S. Park, Y. S. Rim*, K.-S. Jeong*, K.-B. Chung*, “Control of sensitivity in metal oxide electrolyte gated field-effect transistor-based glucose sensor by electronegativity modulation,” Scientific Reports, 14, 27251 (2024), DOI:10.1038/s41598-024-76885-x
  • H. Hong†, M. J. Kim†, D.-J. Yi, D. Y. Shin, Y.-K. Moon, K.-S. Son, J. H. Lim, K. Jeong*, K.-B. Chung*, “Quantitative dynamic evolution of unoccupied states in hydrogen diffused InGaZnSnO TFT under positive bias temperature stress,” ACS Applied Electronic Materials, 6, 7584–7590 (2024), DOI:10.1021/acsaelm.4c01430
  • H. Hong†, D.-J. Yi, Y.-K. Moon, K.-S. Son, J. H. Lim, K. Jeong, K.-B. Chung*, “Double gated a-InGaZnO TFT properties based on quantitative defect analysis and computational modeling,” IEEE Transactions on Electron Devices, 71(2), 1097–1101 (2024), DOI:10.1109/TED.2023.3347503
  • H. Hong†, M. J. Kim, D.-J. Yi, Y.-K. Moon, K.-S. Son, J. H. Lim, K. Jeong*, K.-B. Chung*, “Quantitative analysis of defect states in InGaZnO within 2 eV below the conduction band via photo-induced current transient spectroscopy,” Scientific Reports, 13, 13407 (2023), DOI:10.1038/s41598-023-40162-0

In Preparation

  • D.-J. Yi† et al., “Predicting the electrical conductivity of carbon-nanotube films from Raman spectra with interpretable deep learning,” in preparation.
  • D.-J. Yi† et al., “Carrier-type conversion of two-dimensional Mo1−xWxTe2 for complementary inverters,” in preparation.
  • D.-J. Yi† et al., “Physics-preserving recovery of noisy Raman spectra of two-dimensional materials with deep learning,” in preparation.
  • D.-J. Yi† et al., “Deep-learning classification of bacterial SERS spectra on metal–organic framework substrates,” in preparation.
  • D.-J. Yi† et al., “AI-driven spectroscopy with explainability for physical interpretation,” Invited review, in preparation.
  • …, D.-J. Yi, …, “Ferroelectric InSe synaptic devices for analog in-memory learning,” in preparation.

Patents

  • K.-B. Chung, K. Jeong, D.-J. Yi. “Device Array Analysis Method and Analysis Apparatus Therefor.” Korean Patent, 10-2892259 (granted 2025); U.S. Patent Application 18/771,046 (pending). Assignee: Industry Academic Cooperation Foundation of Dongguk University.
  • K.-B. Chung, K. Jeong, D.-J. Yi, H. Hong. “Method of Confirming Performance of Secondary Battery and Apparatus of Performing the Same.” Korean Patent Application, 10-2023-0161220. Assignee: Industry Academic Cooperation Foundation of Dongguk University.

Scholarships & Awards

2026 Outstanding Presentation Award, KPS Spring Meeting, Oral Session Korean Physical Society
2025 Graduate Student Research Competition (HY-BrainKorea G3), 2nd Place Hanyang University
2024 IC-PBL Graduate Championship, 3rd Place Hanyang University
2022, 2023 Physics Department Alumni Scholarship Dongguk University
2022, 2023 Dongguk Future Talent Scholarship Dongguk University

Skills

Spectroscopy Optical spectroscopy (Raman, photoluminescence, ellipsometry), tip-enhanced nanospectroscopy, X-ray photoelectron spectroscopy
Scanning Probe Microscopy Atomic force microscopy (AFM), Kelvin probe force microscopy (KPFM), piezoresponse force microscopy (PFM)
Defect & Electrical Deep-level transient spectroscopy (DLTS), photo-induced current transient spectroscopy (PICTS), I–V
Machine Learning Deep learning for scientific data, explainable AI
Simulation Finite-difference time-domain (FDTD) simulation, including a self-developed CUDA-accelerated, multi-GPU fork of Meep
Fabrication Photolithography, magnetron sputtering
Dong-Joon Yi · Curriculum Vitae Last updated: 09 October 2026