Semiconductor Process Recipe Generation Using RL Digital Twins
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Solution Overview
Problem
Semiconductor manufacturing faces challenges in achieving desired performance within minimized recipe time due to the complexity of process parameters and reliance on empirical methods, which are time-consuming and labor-intensive, and there is a need for advanced methods to optimize these processes efficiently.
Innovation Solution
The use of a digital twin and reinforcement learning (RL) system, combined with neural networks, to autonomously generate process recipes by simulating interactions and optimizing process parameters, balancing performance and recipe time through techniques like Monte Carlo Tree Search (MCTS).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional empirical methods and iterative testing are used to develop process recipes, then performance specifications can be met, but recipe development time and labor intensity increase significantly
Solution Approach 1:
The patent creates a digital twin (virtual copy) of the semiconductor processing system that replicates the physical system's behavior. This digital model allows for virtual experimentation and recipe development without affecting the actual manufacturing process, enabling parallel development of multiple process recipes simultaneously while maintaining performance specifications.
Solution Approach 2:
The system performs preliminary simulations and optimizations in the digital twin environment before implementing recipes in the physical system. By pre-testing and refining process parameters virtually, the patent eliminates iterative trial-and-error in the actual manufacturing, significantly reducing recipe development time while ensuring performance requirements are met.
2Manufacturing precision
If more process parameters are optimized to achieve high performance, then manufacturing quality improves, but the complexity of the recipe generation process increases
Solution Approach 1:
The patent replaces complex manual recipe generation processes with an autonomous AI-based system. The neural network and reinforcement learning algorithms automatically optimize multiple process parameters simultaneously, substituting human expert analysis and iterative adjustment with automated computational optimization that handles high-dimensional parameter spaces efficiently.
Solution Approach 2:
The digital twin system autonomously generates and optimizes process recipes without requiring continuous human intervention. The reinforcement learning agent independently explores the parameter space, evaluates outcomes through simulation, and refines recipes automatically, reducing the complexity burden on operators while maintaining high manufacturing quality.
3Reliability
If extensive trial-and-error methods are used to explore parameter space, then optimal process parameters can be found, but manufacturing costs and development time increase
Solution Approach 1:
By creating a virtual replica of the manufacturing system, the patent enables unlimited virtual experimentation without consuming actual materials or production time. The digital twin allows the reinforcement learning system to explore the parameter space extensively and identify optimal parameters through simulated trials, eliminating the cost and time penalties of physical trial-and-error while maintaining high reliability in parameter optimization.
Data Source
AI summary
Disclosed is a system and method for a semiconductor process system utilizing reinforcement learning (RL) algorithms to generate optimized process recipes with minimized recipe times. This system includes a comprehensive digital twin, encompassing subsystem, chamber plasma, and process digital twins, and employs neural network models to enhance efficiency. By integrating a policy neural network with Monte Carlo Tree Search (MCTS), the system autonomously achieves an optimized trade-off in the process recipe.