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<p>Glass substrates are widely used in optical devices, electronic displays, and semiconductor manufacturing, where surface quality critically impacts product performance and reliability. However, during fabrication and processing, glass substrates are prone to contamination by particles, metal ions, and organic residues, which may degrade optical properties, cause electrical failures, and reduce device lifespan. This study employs a controlled variable experimental approach to systematically investigate the effects of temperature (30, 40, 50, 60, and 70&#xa0;°C) and ultrasonic duration (0, 2, 5, 8, and 10&#xa0;min) on glass substrate cleaning efficiency. By precisely controlling key parameters (e.g., ultrasonic frequency, cleaning agent concentration), we evaluated cleaning performance based on surface contaminant removal rate and visual cleanliness. The optimal cleaning parameters were determined through statistical analysis, balancing high cleaning efficacy with energy efficiency.</p>

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Research on Surface Contamination Removal Technology of Glass Substrates

  • 期刊论文

  • 发表日期：

    2026年4月20日

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  • Chun Qian,
  • Wenjing Han,
  • Yuxian Tao,
  • Yuqin Zhang

摘要

Glass substrates are widely used in optical devices, electronic displays, and semiconductor manufacturing, where surface quality critically impacts product performance and reliability. However, during fabrication and processing, glass substrates are prone to contamination by particles, metal ions, and organic residues, which may degrade optical properties, cause electrical failures, and reduce device lifespan. This study employs a controlled variable experimental approach to systematically investigate the effects of temperature (30, 40, 50, 60, and 70 °C) and ultrasonic duration (0, 2, 5, 8, and 10 min) on glass substrate cleaning efficiency. By precisely controlling key parameters (e.g., ultrasonic frequency, cleaning agent concentration), we evaluated cleaning performance based on surface contaminant removal rate and visual cleanliness. The optimal cleaning parameters were determined through statistical analysis, balancing high cleaning efficacy with energy efficiency.