Semiconductor Process Digital Twins for RL Recipe and Subsystem Design

The patent creates a digital twin (virtual replica) of the semiconductor process system that copies the physical system's structure, parameters, and behavior. This digital model allows for virtual experimentation and optimization without affecting the actual physical system, enabling rapid iteration and evaluation of design alternatives while maintaining design quality through accurate modeling.

The patent performs preliminary design optimization and validation in the virtual digital twin environment before implementing changes in the physical system. By conducting simulations, parameter optimizations, and performance evaluations beforehand, the system identifies the best design configurations in advance, reducing the need for extensive physical experimentation and accelerating the overall design process.

The digital twin creates a faithful virtual replica of the physical process system, capturing its complex behaviors, interactions, and performance characteristics. This copy enables accurate performance prediction through simulation, reducing uncertainty without requiring extensive physical experimentation that would increase design complexity.

The system implements feedback loops where simulation results from the digital twin are used to automatically adjust design parameters and optimize performance. This closed-loop approach systematically reduces performance prediction uncertainty by continuously refining the design based on virtual experimentation data, avoiding the need for complex manual iteration.

The reinforcement learning algorithm operates autonomously within the digital twin environment, self-learning optimal design configurations through iterative exploration and exploitation. The system serves itself by automatically generating, evaluating, and refining design proposals without requiring extensive external intervention, thereby accelerating design speed while managing complexity through automated decision-making.

The digital twin acts as an intermediary between the reinforcement learning algorithm and the physical system. It provides a safe virtual environment where the RL algorithm can learn and experiment without directly affecting the physical equipment. This intermediary layer manages system complexity by confining complex AI operations to the virtual domain while maintaining simple interfaces with the physical system.

The reinforcement learning algorithm dynamically adjusts and transforms parameter representations during the exploration process, learning efficient parameter transformations that capture the essential design space structure. This allows comprehensive exploration of design options while reducing computational burden by focusing on the most influential parameters and their optimal configurations rather than exhaustively searching all possible parameter combinations.

The system employs partial exploration strategies where the reinforcement learning algorithm focuses on exploring the most promising regions of the parameter space based on learned patterns and heuristics. Rather than uniformly exploring all parameter combinations, the system concentrates computational resources on partial exploration of high-value design regions, achieving effective design optimization with reduced computational burden.