Zone-Based Flow Distribution Using Sensitivity Matrix Clustering
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Solution Overview
Problem
Existing solutions for controlling fluid and thermal zones in semiconductor manufacturing require physical models and extensive experimentation, which are costly and time-consuming, especially when applied to multiple processing tools.
Innovation Solution
A model-based approach that includes running baseline and sensitivity simulations to generate sensitivity matrices, optimizing objective functions, and using clustering algorithms to group zones in fluid and thermal systems, eliminating the need for physical models and extensive experimentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If physical models and extensive experimentation are used to optimize zone grouping, then manufacturing precision is improved, but loss of time and loss of substance increase significantly
Solution Approach 1:
The patent creates a virtual copy of the physical system through computational modeling. Instead of physically experimenting with different zone groupings on actual showerheads and thermal control plates, the invention uses software simulations to replicate system behavior and test various configurations digitally, thereby eliminating time-consuming physical prototypes while maintaining optimization accuracy
Solution Approach 2:
The patent replaces the mechanical experimentation process with computational algorithms. The optimization that previously required physical assembly, testing, and measurement of different zone configurations is now performed through computer-based sensitivity analysis and objective function optimization, substituting mechanical trial-and-error with mathematical computation
2Manufacturing precision
If physical models and extensive experimentation are used to optimize zone grouping, then manufacturing precision is improved, but loss of substance increases due to material consumption
Solution Approach 1:
The patent creates a virtual copy of the physical system through computational modeling. Instead of physically experimenting with different zone groupings on actual showerheads and thermal control plates, the invention uses software simulations to replicate system behavior and test various configurations digitally, thereby eliminating material-consuming physical prototypes while maintaining optimization accuracy
Solution Approach 2:
The computational model serves itself by using mathematical algorithms to evaluate different zone configurations without requiring external physical resources. The sensitivity analysis and objective function optimization are performed entirely within the computational environment, eliminating the need for consumable materials that would be required in physical experimentation
3Ease of operation
If zones are grouped to simplify control, then ease of operation is improved, but manufacturing precision deteriorates due to cross-talk between zones
Solution Approach 1:
The patent applies segmentation by dividing the system into distinct controllable zones (showerhead zones and thermal control plate zones) that can be independently optimized. The sensitivity analysis identifies which pitch circles and heating elements should be grouped together to minimize cross-talk while maintaining control simplicity, creating an optimal segmentation that balances both ease of operation and manufacturing precision
Solution Approach 2:
The patent uses parameter changes by adjusting the zone grouping configuration based on sensitivity analysis results. The objective function evaluates different parameter combinations of zone assignments and selects the optimal configuration that minimizes cross-talk effects while maintaining simple control structure, thereby improving process uniformity without sacrificing operational simplicity
Data Source
AI summary
Embodiments disclosed herein include a method for optimizing zones in a fluid flow system. In an embodiment, the method comprises running a baseline simulation for the fluid flow system, and running a plurality of sensitivity simulations, where each sensitivity simulation perturbs a flowrate through one of a plurality of pitch circles in the fluid flow system by an offset percentage. In an embodiment, the method further comprises generating a sensitivity matrix from the plurality of sensitivity simulations, and optimizing an objective function to enable grouping of the plurality of pitch circles into a plurality of zones.