Sensor-Metrology Data Integration for Wafer Corrective Action
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Solution Overview
Problem
Existing methods for associating sensor data and metrology data in manufacturing processes, such as in semiconductor production, are inefficient, error-prone, and costly in terms of time, energy, and resources, particularly due to manual association and the use of optical character recognition (OCR) systems.
Innovation Solution
A method is introduced to automatically associate sensor data and metrology data by identifying common identifiers, generating aggregated sensor-metrology data, and using this data to train a machine learning model for predictive analytics, thereby 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 performed, but the process becomes time-consuming 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 significantly reducing both time consumption and human error in data association.
Solution Approach 2:
The system enables self-service data integration by automatically matching sensor and metrology data through identifier comparison and temporal correlation. The processing device autonomously performs data association without requiring manual oversight, improving efficiency while maintaining accuracy through systematic automated procedures.
2Reliability
If OCR systems are used to associate sensor data and metrology data, then data linking can be achieved, but energy consumption and processor overhead increase significantly
Solution Approach 1:
The patent extracts and utilizes existing data identifiers embedded in both sensor and metrology data structures. Instead of employing energy-intensive OCR systems to read and interpret data, the system directly leverages pre-existing identifiers and timestamps for automated matching, dramatically reducing processor overhead and energy consumption while maintaining association accuracy.
3Measurement precision
If comprehensive sensor and metrology data are collected and stored, then better predictive analytics can be achieved, but storage requirements and processing complexity increase
Solution Approach 1:
The patent merges sensor data and metrology data into a unified integrated dataset by linking them through common data identifiers and temporal relationships. This consolidation enables comprehensive predictive analytics on correlated parameters while reducing processing complexity compared to handling separate datasets, as the system processes unified data structures with established relationships rather than multiple independent data streams.
Data Source
AI summary
A method includes identifying sets of sensor data associated with wafers processed via wafer processing equipment and identifying sets of metrology data associated with the wafers processed via the wafer processing equipment. The method further includes generating sets of aggregated sensor-metrology data, each of the sets of aggregated sensor-metrology data including a respective set of sensor data and a respective set of metrology data. The method further includes causing, based on the sets of aggregated sensor-metrology data, performance of a corrective action associated with the wafer processing equipment.