Processing Chamber Trace Analysis for Corrective Action Matching
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Solution Overview
Problem
Conventional systems face challenges in efficiently isolating informative sensor data to determine corrective actions for manufacturing equipment, leading to sub-optimal product quality and increased waste.
Innovation Solution
The method involves processing trace sensor data using trained machine learning models to generate summary data, which allows for quick processing and reduced communication bandwidth, enabling efficient identification of differences and recommendation of corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems process full trace sensor data to determine corrective actions, then measurement precision is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The patent extracts only the most informative portions of sensor data by comparing actual trace sensor data against reconstructed data from machine learning models. This extraction approach identifies specific deviations and anomalies without processing the entire data set, thereby reducing analysis time while maintaining measurement precision for fault detection.
Solution Approach 2:
The patent segments the sensor data analysis into multiple stages: first generating summary data from full traces, then comparing against reconstructed data to identify specific informative segments. This segmentation allows the system to process data efficiently by focusing computational resources only on relevant portions that indicate potential faults.
2Reliability
If conventional systems analyze complete sensor data traces, then reliability of fault detection is improved, but device complexity increases
Solution Approach 1:
The patent introduces machine learning models as intermediaries that generate reconstructed sensor data and summary statistics. These intermediaries serve as a bridge between raw sensor data and fault detection logic, simplifying the analysis by providing pre-processed comparisons that highlight deviations without requiring complex direct analysis of complete trace data.
Solution Approach 2:
The patent creates reconstructed copies of sensor data using machine learning models trained on historical data. These synthetic copies serve as reference benchmarks against which actual sensor data is compared, enabling reliable fault detection through difference analysis rather than requiring complex processing of raw data alone.
3Productivity
If trace sensor data is processed in real-time, then productivity is improved, but loss of information increases due to reduced processing depth
Solution Approach 1:
The patent performs preliminary processing of sensor data by generating summary statistics and reconstructed data models during or immediately after manufacturing processes. This preliminary action prepares the data for rapid comparison and analysis, enabling real-time productivity maintenance while preserving detailed information through stored summary data and model representations for later deep analysis when needed.
Data Source
AI summary
A method includes receiving trace sensor data associated with a first manufacturing process of a processing chamber. The method further includes processing the trace sensor data using one or more trained machine learning models that generate a representation of the trace sensor data, and then generate reconstructed sensor data based on the representation of the trace sensor data. The method further includes comparing the trace sensor data to the reconstructed sensor data. The method further includes determining one or more differences between the reconstructed sensor data and the trace sensor data. The method further includes determining whether to recommend a corrective action associated with the processing chamber based on the one or more differences between the trace sensor data and the reconstructed sensor data.