• Prediction for thickness of HfO2 using PEALD

• Plasma process parameter as input / thickness as output 

• Optimization of various Machine Learning 

Preparation 2025 

• Prediction for thickness of SiO2 using RIE

• Digital Image Colorimetry (DIC) M.L for etch depth

• RGB from DIC, RIE parameter as input, etch depth as output

• Optimization of various Machine Learning (ANN, BNN) 

[arXiv: 2505.03826] 

Advanced Intelligent System 2025 2500517

• Analysis of High-Temperature Fluorocarbon plasma in semiconductor process

• Discovery of new reaction pathways in Fluorocarbon plasma

• Multidimensional data visualization and process clustering

• Scalable methodology of other gas-based processes

Sensors 2024 24(22) 7307

• Three in-situ plasma dianosis tool (OES, QMS, ToF-MS)

• Plasma process parameter based plasma information using RIE 

• Correlation analysis was applied to cycleGAN (generative adversarial network)

Journal of Sensor and Actuator Network 2024 13(6), 75

• Prediction of atomic layer control for MoS2

• Two models: linear-, polynominal-regression

• Machine Learning 

Applied Science and Convergence Technology 2023 32, 106-109

(Cover)

(Best Paper Award)

• ML approach for time-varying 10th harmonics of low-k oxide (SiOF)

• High density plasma (HDP) CVD (Ultima, Applied Materials) 

• Artificial Neural Network (ANN) 

• Binary cross-entropy loss (BCEL) function

Sensors 2023 23, 8226