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Jong Hyun Song

Publications

Citations

Publications by Year

20252025

Co-Authors:

Minho JeonAnil Kumar KhambampatiKyung Youn KimSeok Jun KoKeun Joong Yoon

Jong Hyun Song

Affiliation

Nano Process Technology Department

KAIST Affiliated National Nano Fab Center

Daejeon, Korea

Publication Topics

Artificial Neural Network,
Artificial Neural Network Model,
Free Volume,
Numerical Simulations,
Root Mean Square Error,
Scrubber,
Semiconductor Industry,
Annular Flow,
Artificial Neural Network Training,
Capacitance Values,
Capacity Data,
Electrical Measurements

Biography

Jong Hyun Song received the B.S. degree in chemical engineering from Chung-Ang University, Seoul, South Korea, in 2017, and the M.S. degree in electronics engineering from Chungnam National University, Daejeon, South Korea, in 2023, where he is currently pursuing the Ph.D. degree.,From 2017 to 2019, he worked for LG Display pOLED Division in Paju. He is a Senior Researcher with the KAIST Affiliated National Nano Fab Center for Diffusion Process, Daejeon. His research interests include the development of diffusion technology, ECT sensor, and monolithic 3D integration.(Based on document published on 30 December 2024).

Publications

Citations

Publications by Year

20252025

Co-Authors:

Minho Jeon
Anil Kumar Khambampati
Kyung Youn Kim
Seok Jun Ko
Keun Joong Yoon

Author's Published Works

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Kyung Youn Kim(2)
Anil Kumar Khambampati(2)
Keun Joong Yoon(2)
Minho Jeon(2)
Jong Hyun Song(2)
Nano Process Technology Department, KAIST Affiliated National Nano Fab Center, Daejeon, South Korea(1)
Nano Process Technology Department, KAIST Affiliated National Nano Fab Center, Daejeon, Korea(1)
Prosen Inc, Incheon Metropolitan City, Korea(1)
Kyoto College of Graduate Studies for informatics, Kyoto, Japan(1)
Prosen Company Ltd., Incheon, South Korea(1)
2025 International Conference on Electronics, Information, and Communication (ICEIC)(1)
IEEE Access(1)
IEEE(2)
Osaka, Japan(1)
Artificial Neural Network(2)
Artificial Neural Network Model(2)
Free Volume(2)
Numerical Simulations(2)
Root Mean Square Error(2)

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Monitoring Semiconductor Residue Deposits Using Graph Neural Networks

Minho Jeon;Anil Kumar Khambampati;Keun Joong Yoon;Jong Hyun Song;Seok Jun Ko;Kyung Youn Kim

2025 International Conference on Electronics, Information, and Communication (ICEIC)

Year: 2025 | Conference Paper |

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In semiconductor manufacturing processes, preventive maintenance (PM) tasks to remove toxic residues accumulated inside pipes are critical from a safety management perspective. However, performing PM work without a clear understanding of the situation inside the pipes can lead to inefficiencies in terms of cost depending on the maintenance cycle and can pose risks to the health of operators. There...Show More

Monitoring Semiconductor Residue Deposits Using Graph Neural Networks

Minho Jeon;Anil Kumar Khambampati;Keun Joong Yoon;Jong Hyun Song;Seok Jun Ko;Kyung Youn Kim

2025 International Conference on Electronics, Information, and Communication (ICEIC)

Year: 2025 | Conference Paper |

Industrial Monitoring of Residue Deposition in Semiconductor Process Exhaust Pipelines Using Electrical Capacitance Measurements

Minho Jeon;Anil Kumar Khambampati;Jong Hyun Song;Keun Joong Yoon;Kyung Youn Kim

IEEE Access

Year: 2025 | Volume: 13 | Journal Article |

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In semiconductor manufacturing, the accumulation of byproducts in exhaust pipelines under inadequate temperature control poses significant safety and operational risks. This study introduces an innovative approach employing an electrical capacitance measurement sensor system combined with an artificial neural network (ANN) to monitor residue buildup. The proposed method estimates the free volume i...Show More

Industrial Monitoring of Residue Deposition in Semiconductor Process Exhaust Pipelines Using Electrical Capacitance Measurements

Minho Jeon;Anil Kumar Khambampati;Jong Hyun Song;Keun Joong Yoon;Kyung Youn Kim

IEEE Access

Year: 2025 | Volume: 13 | Journal Article |

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