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<p>Chemical Mechanical Polishing (CMP) is the most sophisticated planarization technique among finishing processes in precision engineering and is an essential technology in semiconductor manufacturing. Although CMP is used in the fabrication of nearly all semiconductors, predicting CMP results remains difficult due to the complexity of process parameters. Traditionally, the material removal rate (MRR) in CMP has been predicted based on engineers’ experience or mathematical models, yet accurate prediction of CMP results continues to be a major challenge in the field. With the recent advancement of artificial intelligence (AI) technologies, studies on predicting CMP results and optimizing CMP processes using AI have been actively conducted. To effectively utilize AI in CMP, high-quality and reliable data are required not only from CMP machines but also from consumables. This study reviews the current state of AI-based CMP research and the development status of CMP pads and PVA brushes aimed at enabling more predictable CMP processes. In addition, this paper introduces potential application areas in which AI-based CMP technologies and consumable innovations should be applied in the future.</p>

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Precision Engineering and Intelligent Technologies for Predictable CMP

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Somin Shin,
  • Hyun Jun Ryu,
  • Sanha Kim,
  • Haedo Jeong,
  • Hyunseop Lee

摘要

Chemical Mechanical Polishing (CMP) is the most sophisticated planarization technique among finishing processes in precision engineering and is an essential technology in semiconductor manufacturing. Although CMP is used in the fabrication of nearly all semiconductors, predicting CMP results remains difficult due to the complexity of process parameters. Traditionally, the material removal rate (MRR) in CMP has been predicted based on engineers’ experience or mathematical models, yet accurate prediction of CMP results continues to be a major challenge in the field. With the recent advancement of artificial intelligence (AI) technologies, studies on predicting CMP results and optimizing CMP processes using AI have been actively conducted. To effectively utilize AI in CMP, high-quality and reliable data are required not only from CMP machines but also from consumables. This study reviews the current state of AI-based CMP research and the development status of CMP pads and PVA brushes aimed at enabling more predictable CMP processes. In addition, this paper introduces potential application areas in which AI-based CMP technologies and consumable innovations should be applied in the future.