Recursive Flow Gas Distribution Stack With Deformation-Controlled Bonding
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Solution Overview
Problem
Conventional methods for fabricating multi-layer components, such as showerheads for substrate processing systems, using metallic layers result in slower gas flow rates, increased contamination due to filler material particles, and reduced yield and quality due to longer gas purge times and substrate contamination.
Innovation Solution
A method involving the diffusion bonding of metal plates based on predicted deformation values, using a processing device that determines optimal bonding parameters such as pressure and temperature, and employing a machine learning model to optimize the bonding process and reduce particle contamination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional brazing or welding methods are used to join metallic layers, then the multi-layer component can be assembled, but filler material particles cause contamination and slower gas flow rates
Solution Approach 1:
The patent removes the filler material entirely from the bonding process by adopting diffusion bonding technology. This extraction of the harmful filler material eliminates particle contamination while maintaining the assembly capability of multi-layer components, directly resolving the technical contradiction between ease of manufacture and harmful factors.
Solution Approach 2:
The patent replaces conventional mechanical bonding methods (brazing/welding with filler material) with diffusion bonding, which relies on atomic-level interdiffusion between metal surfaces. This substitution eliminates the need for filler material while achieving strong joints, thereby eliminating contamination and improving gas flow rates.
2Ease of manufacture
If conventional multi-layer components with filler material are used, then assembly is achieved, but gas purge time increases and yield decreases
Solution Approach 1:
By extracting the filler material from the bonding process, the patent eliminates the source of particle contamination that causes substrate contamination and extended gas purge times. This directly improves productivity by reducing purge time and increasing yield.
Solution Approach 2:
The patent changes the bonding parameters from conventional brazing/welding temperatures and methods to diffusion bonding parameters (lower temperatures, controlled atmospheres, prolonged heating times). This parameter change enables contamination-free bonding that improves gas flow characteristics and reduces purge times, thereby increasing productivity.
3Object-generated harmful factors
If diffusion bonding is used to join metal plates, then particle contamination is reduced and gas flow rates improve, but deformation control during bonding becomes challenging
Solution Approach 1:
The patent applies preliminary actions by using predictive models and simulations before the actual diffusion bonding process to determine optimal bonding parameters that minimize deformation. This preliminary planning ensures that deformation remains within acceptable tolerances while achieving contamination-free bonding, thus resolving the contradiction between reducing harmful factors and maintaining manufacturing precision.
Solution Approach 2:
The patent implements feedback mechanisms by using predictive models that incorporate material properties, geometry, and bonding parameters to forecast and control deformation during diffusion bonding. This feedback loop allows real-time adjustment of bonding parameters to maintain manufacturing precision while eliminating particle contamination.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method enhances gas flow rates, reduces particle contamination, increases yield and quality by optimizing the bonding process, and decreases energy consumption.
Implementation Method 1
causing, based on the plurality of predicted deformation values, the plurality of metal plates to be diffusion bonded to produce a bonded metal plate structure
Data Source
AI summary
A method includes receiving one or more parameters associated with a plurality of metal plates. The method further includes determining, based on the one or more parameters, a plurality of predicted deformation values associated with the plurality of metal plates. Each of the plurality of predicted deformation values correspond to a corresponding metal plate of the plurality of metal plates. The method further includes causing, based on the plurality of predicted deformation values, the plurality of metal plates to be diffusion bonded to produce a bonded metal plate structure.