RL Process Recipe Generation with Digital Twins for Semiconductor Tools
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Solution Overview
Problem
Optimizing semiconductor manufacturing processes such as RIE, ALE, PECVD, and ALD is challenging due to their complexity and variability, requiring extensive experimentation and expert knowledge, which is time-consuming and resource-intensive.
Innovation Solution
Employing reinforcement learning (RL) algorithms and digital twins to autonomously generate process recipes, utilizing a system digital twin and neural networks to simulate and optimize process parameters efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional experimentation and expert knowledge are used to optimize process parameters, then manufacturing precision can be achieved, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent creates a digital twin that replicates the semiconductor process system in advance, allowing the reinforcement learning agent to learn optimal parameters in the virtual environment before real-world implementation. This preliminary learning action eliminates the need for time-consuming trial-and-error experimentation on actual production equipment.
Solution Approach 2:
The patent creates a virtual copy (digital twin) of the physical semiconductor process system. This copy includes replicated process chambers, tools, and parameters that mirror the real system. The RL agent trains on this copy, generating optimized parameters that are then transferred to the physical system, avoiding direct experimentation on expensive production equipment.
2Manufacturing precision
If traditional experimentation methods are used to explore parameter space, then optimal parameters can be found, but the process becomes resource-intensive
Solution Approach 1:
The patent creates a virtual copy (digital twin) of the physical semiconductor process system. This copy includes replicated process chambers, tools, and parameters that mirror the real system. The RL agent trains on this copy, generating optimized parameters that are then transferred to the physical system, avoiding direct experimentation on expensive production equipment.
Solution Approach 2:
The reinforcement learning agent autonomously explores the parameter space and optimizes process parameters without requiring human expert intervention. The agent self-learning through interaction with the digital twin environment, automatically identifying optimal parameters for semiconductor manufacturing processes.
3Manufacturing precision
If manual intervention is used in process recipe development, then expertise can be applied, but automation is reduced
Solution Approach 1:
The reinforcement learning agent autonomously explores the parameter space and optimizes process parameters without requiring human expert intervention. The agent self-learning through interaction with the digital twin environment, automatically identifying optimal parameters for semiconductor manufacturing processes.
Solution Approach 2:
The reinforcement learning framework implements a feedback mechanism where the agent receives rewards or penalties based on the performance of generated process recipes. This feedback loop enables the agent to continuously learn and improve, automatically refining parameter selections to achieve desired manufacturing outcomes without manual guidance.
4Manufacturing precision
If extensive experimentation is conducted to optimize processes, then precision can be improved, but productivity decreases
Solution Approach 1:
The patent creates a digital twin that replicates the semiconductor process system in advance, allowing the reinforcement learning agent to learn optimal parameters in the virtual environment before real-world implementation. This preliminary learning action eliminates the need for time-consuming trial-and-error experimentation on actual production equipment.
Solution Approach 2:
The reinforcement learning agent autonomously explores the parameter space and optimizes process parameters without requiring human expert intervention. The agent self-learning through interaction with the digital twin environment, automatically identifying optimal parameters for semiconductor manufacturing processes.
Data Source
AI summary
Disclosed herein are systems and methods for autonomously generating semiconductor process recipes using reinforcement learning (RL) based on digital twins. By employing a neural network version of the digital twin, the system enhances computing efficiency, allowing exploration of large parameter spaces. An RL agent, guided by a policy neural network and a Monte Carlo tree search (MCTS) program, autonomously generates many learning cases, calculates associated rewards, and continuously improves the policy neural network.