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Deep reinforcement learning for scheduling semiconductor cluster tools in varying configurations.

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Area of Science:

  • Semiconductor Manufacturing
  • Artificial Intelligence
  • Operations Research

Background:

  • Traditional rule-based cluster tool scheduling is inflexible and struggles in dynamic semiconductor fabrication environments.
  • Existing research often uses simplified simulators, not reflecting real-world equipment complexity.
  • Modern fabs require adaptive scheduling solutions for varying process conditions and equipment.

Purpose of the Study:

  • To investigate the application of deep reinforcement learning (DRL) for optimizing cluster tool scheduling.
  • To develop and evaluate DRL agents within a comprehensive simulation of a vacuum (VTM) and atmospheric (ATM) robot cluster tool system.
  • To compare DRL performance against traditional rule-based scheduling methods.

Main Methods:

  • Developed a detailed simulation environment for a VTM-ATM cluster tool system.
  • Progressively evaluated DRL agents, starting with a single-agent deep Q-network (DQN).
  • Advanced to a multi-agent DQN (MADQN) framework for scheduling the integrated VTM-ATM system.

Main Results:

  • DRL agents consistently outperformed traditional rule-based methods in productivity and adaptability.
  • The MADQN agent showed robust performance in complex multi-agent scenarios.
  • Achieved up to 8.9% productivity improvement compared to standard rule-based scheduling.

Conclusions:

  • DRL offers a powerful solution to overcome limitations of traditional cluster tool scheduling.
  • The MADQN framework effectively optimizes scheduling in dynamic semiconductor manufacturing environments.
  • DRL has significant potential to enhance overall productivity in semiconductor fabrication.