Substrate Support Assembly Screening via RF Impedance Electron Flow
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Solution Overview
Problem
Substrate support assemblies in manufacturing systems often have defective electrical components, which can damage substrates and lead to inefficiencies and increased system latency due to the need for modifying process recipes.
Innovation Solution
A system and method that utilize a trained machine learning model to rate substrate support assemblies based on impedance circuit electron flow by analyzing the amount of RF power flowed through electrical components during a testing process and determining whether the electron flow meets a specified criterion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If substrate support assemblies are used without pre-screening, then manufacturing productivity is maintained, but substrate damage occurs and system latency increases
Solution Approach 1:
The patent applies preliminary action by implementing a pre-screening testing process that evaluates substrate support assemblies before they are installed and used. The machine learning model analyzes electrical component performance in advance, identifying defective assemblies prior to deployment, thereby preventing substrate damage while maintaining manufacturing productivity.
2Measurement precision
If traditional testing methods are used, then device complexity is low, but measurement precision of electron flow is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/electrical testing methods with a machine learning-based measurement system. The ML model processes electrical component data to predict electron flow characteristics with high precision, substituting complex physical measurement apparatus with an intelligent software system that achieves superior measurement accuracy.
3Object-affected harmful factors
If defective substrate support assemblies are detected early, then substrate damage is prevented, but additional testing time is required
Solution Approach 1:
The patent uses copying by creating a virtual model of the substrate support assembly's electrical components through machine learning. The ML model generates predictions about electron flow and component performance based on input electrical characteristics, allowing rapid assessment without requiring extensive physical testing, thus minimizing time loss while effectively preventing substrate damage.
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
This approach allows for the identification of substrate support assemblies with defective electrical components before installation, preventing substrate damage and reducing system resource wastage, thereby decreasing overall system latency and increasing efficiency.
Implementation Method 1
extracting, from the one or more outputs, a measurement value for an electron flow across an impedance circuit of the current substrate support assembly
Implementation Method 2
data associated with an amount of radio frequency (RF) power flowed through an electrical component of a current substrate support assembly during a current testing process
Data Source
AI summary
Methods and systems for rating a current substrate support assembly based on impedance circuit electron flow are provided. Data associated with an amount of radio frequency (RF) power flowed through an electrical component of a current substrate support assembly during a current testing process performed for the current substrate support assembly is provided as input to a trained machine learning model. One or more outputs of the trained machine learning model are obtained. A measurement value for an electron flow across an impedance circuit of the current substrate support assembly is extracted from the one or more outputs. In response to a determination that the extracted measurement value for the electron flow satisfies an electron flow criterion, a first quality rating is assigned to the current substrate support assembly.