Wafer Data Integration Using Sensor-Metrology Matching
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Solution Overview
Problem
Existing methods for associating sensor data and metrology data in manufacturing processes, such as those used in semiconductor production, are inefficient, error-prone, and costly due to manual association and the use of optical character recognition (OCR) systems, leading to increased storage, processor overhead, and energy consumption.
Innovation Solution
A method is introduced to automatically associate sensor data and metrology data by identifying common identifiers and generating aggregated sensor-metrology data, which is then used to train a machine learning model for predictive analysis, reducing errors and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual association methods are used to link sensor data and metrology data, then data integration can be achieved, but the process becomes inefficient and error-prone
Solution Approach 1:
The patent replaces manual mechanical data association processes with automated computational methods. A processing device automatically links sensor data and metrology data using data identifiers and timestamps, eliminating manual intervention and its associated errors while significantly improving processing efficiency
Solution Approach 2:
The patent introduces data identifiers and timestamps as intermediary elements to facilitate automatic data association. These intermediaries enable the processing device to accurately match sensor data with corresponding metrology data without manual intervention, resolving the contradiction between automation and accuracy
2Reliability
If OCR systems are used to associate sensor data and metrology data, then data integration can be achieved, but energy consumption and processor overhead increase
Solution Approach 1:
The patent extracts and uses only the necessary data identifiers and timestamps from the data sets for association purposes. This selective extraction approach avoids the need for complex OCR systems while maintaining accurate data linkage, thereby reducing energy consumption and processor overhead
Solution Approach 2:
The patent substitutes OCR-based optical recognition systems with direct computational matching of data identifiers. This replacement eliminates the energy-intensive image processing requirements of OCR while achieving the same data association goal more efficiently
3Reliability
If manual association methods are used, then data integration can be achieved, but storage requirements and processor overhead increase
Solution Approach 1:
The patent extracts and stores only the essential data identifiers and timestamps needed for association. This minimal data storage approach enables accurate data linkage without the excessive storage requirements that would result from manual association documentation or OCR image archives
Data Source
AI summary
A method includes identifying sets of a first type of data associated with wafers processed via processing chambers of wafer processing equipment and identifying sets of a second type of data associated with the wafers processed via the processing chambers of the wafer processing equipment. The first type of data is different than the second type of data. The method further includes generating sets of aggregated data, where each of the sets of aggregated data includes a respective set of the first type of data and a respective set of the second type of data. The method further includes causing, based on the sets of aggregated data, performance of a corrective action associated with adjusting at least one operation associated with the wafer processing equipment.