Substrate Equipment Data Integration for Faster Yield Correction
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Solution Overview
Problem
Conventional methods for improving product quality in substrate processing are manual, time-consuming, and wasteful, as they involve trial-and-error in replacing parts and adjusting manufacturing parameters without integrated analysis of part, sensor, and metrology data, leading to inefficient corrective actions and suboptimal product yield.
Innovation Solution
A system and method for integrating part, sensor, and metrology data to generate aggregated data sets, which are analyzed to perform corrective actions on substrate processing equipment, reducing waste and improving yield by identifying optimal part designs and manufacturing parameters through data-driven insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual trial-and-error methods are used to replace parts and adjust manufacturing parameters, then corrective actions can be performed, but the process becomes time-consuming and wasteful
Solution Approach 1:
The system performs preliminary data collection and integration from multiple sources (part data, sensor data, metrology data) before actual corrective actions are needed. By pre-establishing data linkages and performing preliminary analysis, the system eliminates the need for time-consuming manual trial-and-error when quality issues arise, as the root cause can be quickly identified from the integrated data
Solution Approach 2:
The system implements a feedback mechanism where data from part manufacturing, substrate processing, and metrology measurements are continuously collected and integrated. This feedback loop enables the system to automatically identify correlations between part characteristics and substrate outcomes, allowing for rapid corrective actions based on data-driven insights rather than manual guessing
2Reliability
If manual trial-and-error methods are used to improve product quality, then corrective actions can be performed, but material waste increases
Solution Approach 1:
By pre-integrating part data with substrate processing and metrology data, the system identifies the root cause of quality issues before manufacturing defects propagate through the production process. This preliminary analysis prevents waste of substrates and materials that would otherwise be consumed during manual trial-and-error testing
Solution Approach 2:
The system replaces manual mechanical trial-and-error methods with an automated data analysis system. Instead of physically replacing parts and re-running manufacturing processes to test hypotheses, the system uses integrated data analysis to identify root causes, eliminating the material waste associated with physical experimentation
3Productivity
If integrated data analysis is implemented, then corrective actions become rapid and accurate, but system complexity increases
Solution Approach 1:
The system segments data collection and integration into distinct modules: part data collection, sensor data collection, metrology data collection, and integrated analysis. Each module handles a specific type of data, making the overall complex system manageable through functional segmentation. This modular approach maintains high productivity while controlling complexity through organized data flow
4Ease of manufacture
If manual methods are used for quality improvement, then implementation is simple, but manufacturing precision deteriorates
Solution Approach 1:
The system implements automated feedback loops that continuously monitor part characteristics, substrate processing parameters, and metrology measurements. This feedback enables precise identification of quality issues and their root causes, achieving high manufacturing precision. The system maintains ease of manufacture by automating the complex analysis tasks, so users benefit from advanced precision without the complexity of manual implementation
Data Source
AI summary
A method includes identifying sets of part data associated with substrate processing equipment. Each of the sets of part data includes corresponding part values and a corresponding part identifier. Each of the sets of part data is associated with hardware parameters of a corresponding equipment part of substrate processing equipment. The method further includes generating sets of aggregated data. Each of the sets of aggregated data includes a corresponding set of part data of the sets of part data and a corresponding set of additional non-part data of sets of non-part data. The method further includes causing, based on the sets of aggregated data, performance of a corrective action associated with the substrate processing equipment.