Statistical Data Decimation for Plasma Chamber Control
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Solution Overview
Problem
In plasma systems, controlling plasma properties such as uniformity and power requires processing large amounts of data from computer-generated models, which is resource-intensive and inefficient, leading to high processing costs and storage challenges.
Innovation Solution
Generating a statistical value from the output of these models, rather than analyzing all values, to determine if the plasma chamber needs adjustment, thereby reducing data processing requirements and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all values from computer-generated models are processed to control plasma chamber, then control precision is improved, but processing cost and resource consumption increase
Solution Approach 1:
The patent extracts only the essential statistical information (mean, variance, skewness, kurtosis) from the complete model output data, discarding redundant individual data points. This extraction approach maintains control precision by preserving the key characteristics of the plasma parameters while significantly reducing processing requirements and resource consumption.
Solution Approach 2:
Instead of processing all model output values, the patent applies partial action by computing only the necessary statistical moments (first four moments) that capture the essential behavior of plasma parameters. This partial processing approach is sufficient for effective plasma chamber control without the excessive resource consumption of complete data analysis.
2Loss of information
If all model output values are stored and analyzed, then data completeness is improved, but storage requirements increase
Solution Approach 1:
The patent extracts only the essential statistical characteristics (mean, variance, skewness, kurtosis) from the complete model output, storing only these aggregated values rather than all individual data points. This extraction maintains sufficient information for control decisions while dramatically reducing storage requirements.
Solution Approach 2:
The patent transforms the raw model output data into different parameter forms (statistical moments) that condense large datasets into compact representations. By changing the data parameters from individual values to aggregated statistical measures, the system preserves essential information while minimizing storage needs.
3Productivity
If statistical values are used instead of all model outputs, then processing efficiency is improved, but control precision may deteriorate
Solution Approach 1:
The patent changes the parameter representation from individual data points to statistical moments (mean, variance, skewness, kurtosis), which efficiently capture the essential characteristics of plasma parameters. This parameter transformation maintains control precision by preserving the distributional properties of the data while achieving high processing efficiency through reduced data volume.
Solution Approach 2:
The patent replaces the mechanical approach of processing and storing all raw data with a computational substitution using statistical moment calculations. This substitution achieves the same control objective with significantly reduced processing requirements by using mathematical transformations rather than brute-force data handling.
Data Source
AI summary
Systems and methods for statistical data decimation are described. The method includes receiving a variable from a radio frequency (RF) system, propagating the variable through a model of the RF system, and counting an output of the model for the variable to generate a count. The method further includes determining whether the count meets a count threshold, generating a statistical value of the variable at the output of the model upon determining that the count meets the count threshold, and sending the statistical value to the RF system to adjust the variable.