Plasma Tool Sensor Data Capture for Memory-Efficient Process Control
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Solution Overview
Problem
In plasma tools, a large amount of data is collected during wafer processing, leading to inefficiencies in memory usage and processing control, as existing systems lack effective methods for intelligently collecting and analyzing sensor data, particularly in determining optimal locations and time windows for data capture.
Innovation Solution
The implementation of a processor-based system that analyzes metric data to determine specific locations and time windows for data capture, generating variable data to control the plasma tool, and storing statistical values instead of raw data to reduce memory usage and enhance processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all sensor data is collected during wafer processing, then complete process monitoring is achieved, but memory usage and data processing burden increase significantly
Solution Approach 1:
The patent extracts and stores only the essential characteristics (statistical values) from the complete sensor data set, rather than storing all raw data. This selective extraction maintains process monitoring capability while dramatically reducing memory requirements.
Solution Approach 2:
Instead of collecting all data and then analyzing it, the system inverts the approach by first determining what information is essential, then collecting only that specific data. This reverses the traditional data collection paradigm to achieve efficiency.
2Measurement precision
If sensor data is collected at high sampling rates, then measurement precision is improved, but data processing complexity and memory requirements increase
Solution Approach 1:
The system extracts only the essential features from high-rate sensor data (such as statistical values like mean, variance, or specific temporal characteristics) rather than processing the complete data stream. This maintains measurement precision while reducing processing complexity.
Solution Approach 2:
The patent applies partial action by collecting data at high sampling rates only during critical time windows or for specific parameters that require high precision, rather than uniformly high sampling across all parameters and time periods.
3Manufacturing precision
If sensor data is collected from all locations continuously, then comprehensive process control is achieved, but the system complexity and data volume increase
Solution Approach 1:
The patent applies local quality by determining different data collection strategies for different spatial locations and process parameters. Critical locations receive continuous high-rate sampling while less critical areas use lower sampling rates or event-triggered collection.
Solution Approach 2:
The system dynamically adjusts data collection parameters (sampling rate, locations, time windows) based on real-time process conditions and historical analysis, rather than using static uniform collection across all locations.
4Quantity of substance
If statistical values are stored instead of raw data, then memory usage is reduced, but data analysis capability may be limited
Solution Approach 1:
The patent transforms raw sensor data into different parameter representations (statistical values such as mean, standard deviation, skewness, or other process-specific metrics) that condense information while preserving essential process characteristics needed for control decisions.
Data Source
AI summary
A method for controlling a plasma tool is described. The method includes receiving, by a processor, a first set of metric data from a plasma tool. The method further includes analyzing the first set of metric data to determine a first location and a first time window for capturing of a second set of metric data. The method includes providing, by the processor, the first location and the first time window to a data processing system of the plasma tool. The method also includes receiving the second set of metric data captured at the first location and for the first time window. The method includes analyzing the second set of metric data to generate variable data and controlling the plasma tool according to the variable data.