Eunhyeok Heo

Doyoung Lim

Yoon Soo Lee

Hyeonseung Ahn

Chiyun Bang

Seungyeon Oh

Koeun Kim

Sangwon Lee

Tural Usubov

Sungsik Nam

Sangrae Cho

The Virtual Materials and Processes Design Laboratory (VDLab) endeavors to develop highly sophisticated methodologies for an essential understanding and innovative design of materials and processes across a broad spectrum, encompassing multiscale phenomena from quantum to continuum mechanics. By leveraging novel scale-bridging techniques, the laboratory addresses complex challenges in plasma-material interactions, which are pivotal for semiconductor processing. The VDLab is dedicated to optimizing material and process variables, employing multiphysics computational simulations to enhance the performance and yield of semiconductor manufacturing. Additionally, the laboratory actively explores integrating deep learning methods further to expand the applicability and coverage of computational materials science, aiming to revolutionize advanced materials design and semiconductor industry practices through predictive analytics and intelligent automation.

가상재료공정설계 연구실 (VDLab) 은 멀티스케일/멀티피직스 관점의 전산모사 기법 과 인공지능 기술 을 적극적으로 활용하여 반도체 분야 를 포함한 첨단 과학 분야의 차세대 재료 및 공정 개발 을 위한 근원적인 이해와 설계안을 제공하는 것을 목표로 합니다. 특히 반도체 제조 공정에서 핵심적인 플라즈마 공정 에 대한 재료 표면에서의 현상을 원자 수준에서 이해하고 이에 기반한 난제 공정의 이론적 한계 를 탐색하기 위한 고도화된 방법론 개발에 힘쓰고 있습니다. 이러한 기술적 혁신을 통하여 가상의 재료/공정 개발 플랫폼 을 구축하는 것을 장기적인 목표로 삼고 있습니다.

차세대 반도체 공정 설계를 위한 계산과학 활용

멀티스케일 전산모사를 통한 플라즈마 공정의 이해

In-situ post-doping plasma process during atomic layer deposition of Al-doped TiO2 for sub-nanometer lattice ordering and defect annihilation

Physics-guided machine learning model for predicting low-temperature NF3/H2 plasma etching rates of SiO2/SiN films

고종횡비 식각에서의 이온 수송 및 에너지 전달 거동 해석을 위한 AI 기반 하이브리드 시뮬레이션 프레임워크

반도체 제조 공정에서의 CAE 방법론 활용: 실질적 개선과 공정 메커니즘 분석

Post baking apparatus

Upper electrode and substrate processing apparatus including the same

Method of manufacturing a semiconductor package

Influence of crosslink density on the interfacial characteristics of epoxy nanocomposites

Fracture toughness enhancement of thermoplastic/epoxy blends by the plastic yield of toughening agents: A multiscale analysis

Multiscale modeling of interphase in crosslinked epoxy nanocomposites

Influences of the molecular structures of curing agents on the inelastic-deformation mechanisms in highly-crosslinked epoxy polymers

Toward the constitutive modeling of epoxy matrix: Temperature-accelerated quasi-static molecular simulations consistent with the experimental test

Multi-scale coarse-grained molecular dynamics simulation to investigate the thermo-mechanical behavior of shape-memory polyurethane copolymers

Multiscale study of the relationship between photoisomerization and mechanical behavior of azo-polymer based on the coarse-grained molecular dynamics simulation

Computational approach for plasma process optimization combined with deep learning model

Atomistic kinetic Monte Carlo simulation on atomic layer deposition of TiN thin film

Atomistic insights on hydrogen plasma treatment for stabilizing High-k/Si interface

Multiscale modeling of load transfer characteristics in crosslinked epoxy nanocomposites

Deep neural network-based reduced-order modeling of ion–surface interactions combined with molecular dynamics simulation

Combined molecular dynamics simulations and reaction kinetics study on wettability of trimethylsilyl functionalized silicon surfaces

Atomistic-scale Simulations on Surface Activation Process of Dielectric Oxides for Hybrid Bonding Applications

Molecular dynamics study of grain size effects on interfacial voids in Cu-Cu diffusion bonding

Efficient design of Voronoi energy-absorbing foams using Bayesian optimization

Deep learning model for ion sputtering dynamics with molecular dynamics simulation

Toward the multiscale modeling for the construction of cohesive zone model on the interface of laminated composite structure

Temperature-accelerated molecular dynamics simulations of quasi-static yield stress of epoxy polymers

VDLab

교수님 소개

김병조

연구실 소개

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

In-situ post-doping plasma process during atomic layer deposition of Al-doped TiO2 for sub-nanometer lattice ordering and defect annihilation

차세대 반도체 공정 설계를 위한 계산과학 활용

Physics-guided machine learning model for predicting low-temperature NF3/H2 plasma etching rates of SiO2/SiN films

연구실 보유 특허

대학별 입시끈

우리 연구실 모집

교수님 소개

김병조

연구실 키워드

알럼나이가 간 회사

연구실 지원 방법

대우 조건

연도별 논문 인용수

저널 & 학회 논문 출판 수

연구실 논문 목록

In-situ post-doping plasma process during atomic layer deposition of Al-doped TiO2 for sub-nanometer lattice ordering and defect annihilation

차세대 반도체 공정 설계를 위한 계산과학 활용

Physics-guided machine learning model for predicting low-temperature NF3/H2 plasma etching rates of SiO2/SiN films

연구실 보유 특허

최근 3년간 연구실적 높은 연구실

대학별 입시끈

우리 연구실 모집