Part-Sensor-Metrology Data Linking for Faster Corrective Actions
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Solution Overview
Problem
Conventional methods for improving product quality in manufacturing are manual and time-consuming, often resulting in waste and inefficiency due to the separation of part, sensor, and metrology data stored in different formats, which hinders effective analysis and corrective actions in substrate processing equipment.
Innovation Solution
Integration of part, sensor, and metrology data to generate aggregated data sets, allowing for analysis to determine corrective actions through machine learning models, reducing manual trial and error and improving yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If part, sensor, and metrology data are stored in separate formats and locations, then data storage and management are simpler, but data analysis effectiveness and productivity are reduced
Solution Approach 1:
The patent combines part data, sensor data, and metrology data into a unified data structure with common identifiers, enabling integrated analysis while maintaining manageable complexity through standardized formats and systematic organization
2Loss of time
If manual methods are used for quality improvement, then implementation is straightforward, but time consumption and waste increase
Solution Approach 1:
The system establishes feedback loops where metrology data from substrates is correlated with part and sensor data to automatically identify causes of quality issues and generate corrective actions, reducing both time loss and operational complexity
Solution Approach 2:
The patent performs preliminary data integration and correlation analysis so that when quality issues arise, corrective actions can be rapidly determined without manual trial and error, reducing time loss while maintaining ease of operation
3Measurement precision
If data integration is performed across multiple sources, then measurement precision and analysis accuracy improve, but data processing complexity increases
Solution Approach 1:
The patent segments data from different sources (part data, sensor data, metrology data) into distinct but correlated sets, each maintaining its own structure while being linkable through common identifiers, thus improving measurement precision without overwhelming processing complexity
Data Source
AI summary
A method includes receiving part data associated with a corresponding part of substrate processing equipment, sensor data associated with one or more corresponding substrate processing operations performed by the substrate processing equipment to produce one or more corresponding substrates, and metrology data associated with the one or more corresponding substrates produced by the one or more corresponding substrate processing operations performed by the substrate processing equipment that includes the corresponding part. The method further includes generating sets of aggregated part-sensor-metrology data including a corresponding set of part data, a corresponding set of sensor data, and a corresponding set of metrology data. The method further includes causing analysis of the sets of aggregated part-sensor-metrology data to generate one or more outputs to perform a corrective action associated with the corresponding part of the substrate processing equipment.