Assembly Line

Could Reinforcement Learning play a part in the future of wafer fab scheduling?

📅 Date:

✍️ Author: Marcus Vitelli

🔖 Topics: Reinforcement Learning

🏭 Vertical: Semiconductor

🏢 Organizations: Flexciton

However, as the use of RL for JSS problems is still a novelty, it is not yet at the level of sophistication that the semiconductor industry would require. So far, the approaches can handle standard small problem scenarios but cannot handle flexible problems or batching decisions. Many constraints need to be obeyed in wafer fabs (e.g., timelinks and reticle availability) and it is not easily guaranteed that the agent will adhere to them. The objective set for the agent must be defined ahead of training, which means that any change made afterwards will require a repeat of training before new decisions can be obtained. This is less problematic for solving the instance proposed by Tassel et al., although their approach relies on a specifically modelled reward function which would not easily adapt to changing objectives.

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