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<p>Conventional human-centered process operations and explicit physics-based models face scalability limitations in high-dimensional, partially observable semiconductor and advanced materials processing. This review critically surveys the evolution of automation in materials processing, from heuristic-driven control through AI-assisted optimization to implicit, adaptive process modeling, where process-outcome relationships are learned directly from data and continuously refined through closed-loop feedback. We provide a unifying framework for developing scalable, robust, and knowledge-generating autonomous manufacturing systems.</p>

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Self-driving manufacturing: accelerating materials discovery with adaptive closed-loop processing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月1日

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  • Seulwon Choi,
  • Hanseong Ko,
  • Kyeongjin Lee,
  • Hwanyeol Park

摘要

Conventional human-centered process operations and explicit physics-based models face scalability limitations in high-dimensional, partially observable semiconductor and advanced materials processing. This review critically surveys the evolution of automation in materials processing, from heuristic-driven control through AI-assisted optimization to implicit, adaptive process modeling, where process-outcome relationships are learned directly from data and continuously refined through closed-loop feedback. We provide a unifying framework for developing scalable, robust, and knowledge-generating autonomous manufacturing systems.