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<p>Silicon nitride (SiN) is recognized as a vital material in advanced semiconductor processes due to its excellent physical properties. However, SiN has been reported to sustain damage from radiation, and studies on radiation degradation have been hindered by challenges in data acquisition due to time and cost constraints. In response, this study was conducted by combining machine learning and molecular dynamics simulations to efficiently analyze radiation-induced degradation in SiN. To induce radiation degradation in SiN, the Total Ionizing Dose (TID) effect was applied, and spectral data were collected using Fourier Transform Infrared (FTIR) spectroscopy to evaluate the extent of degradation. This data was subsequently used to develop a machine learning model capable of classifying degrees of radiation degradation in SiN. Additionally, X-ray diffraction (XRD) and X-ray Photoelectron Spectroscopy (XPS) were utilized to confirm the structure and atomic composition of SiN for molecular modeling. The modeled SiN molecules were then subjected to molecular dynamics simulations to observe nanoscale changes in mechanical and thermal properties due to radiation exposure.</p>

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Classification of Radiation Degradation Degree in Silicon Nitride Using Machine Learning and Analysis of Mechanical Property Changes via Molecular Dynamics Simulation

  • 期刊论文

  • 发表日期：

    2025年7月8日

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  • Dong-Hyeon Kim,
  • Hui Seong Kim,
  • Sohdam Jeong,
  • Sung-Jae Chang,
  • Sung-Uk Zhang

摘要

Silicon nitride (SiN) is recognized as a vital material in advanced semiconductor processes due to its excellent physical properties. However, SiN has been reported to sustain damage from radiation, and studies on radiation degradation have been hindered by challenges in data acquisition due to time and cost constraints. In response, this study was conducted by combining machine learning and molecular dynamics simulations to efficiently analyze radiation-induced degradation in SiN. To induce radiation degradation in SiN, the Total Ionizing Dose (TID) effect was applied, and spectral data were collected using Fourier Transform Infrared (FTIR) spectroscopy to evaluate the extent of degradation. This data was subsequently used to develop a machine learning model capable of classifying degrees of radiation degradation in SiN. Additionally, X-ray diffraction (XRD) and X-ray Photoelectron Spectroscopy (XPS) were utilized to confirm the structure and atomic composition of SiN for molecular modeling. The modeled SiN molecules were then subjected to molecular dynamics simulations to observe nanoscale changes in mechanical and thermal properties due to radiation exposure.