Bidirectional Sensor Metrology Data Linking for Fault Detection
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Solution Overview
Problem
Manufacturing engineers face a laborious and costly process when identifying issues with product quality, as they need to manually link and analyze time-based equipment sensor data with material-based metrology data stored in separate databases, leading to inefficient fault detection and root-cause analysis.
Innovation Solution
A method and system that link time-based equipment sensor data with material-based metrology data through a reporting tool, allowing users to drill across from sensor data to metrology data and vice versa by applying unique relationships between context attributes, enabling enhanced cross-drilling capabilities and efficient fault detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If sensor data and metrology data are stored in separate databases from different vendors, then data storage and management are simplified, but data access and analysis become laborious and costly
Solution Approach 1:
The patent merges sensor data and metrology data into a unified data structure with bidirectional associations. The system creates linked data models where sensor readings and metrology measurements can reference each other through common identifiers (lot ID, wafer ID, tool ID), enabling engineers to access both data types through a single interface without manual data gathering from separate databases.
Solution Approach 2:
The patent introduces an intermediary data access layer that sits between the separate databases and the user. This layer automatically joins and correlates data from multiple sources using defined relationships, presenting integrated results without requiring engineers to manually query multiple databases. The intermediary handles the complexity of data integration while providing simplified access to users.
2Measurement precision
If engineers manually review metrology data and search through time series sensor data to identify problems, then comprehensive analysis is possible, but the process is inefficient and error-prone
Solution Approach 1:
The patent performs preliminary data correlation and association before the engineer needs to analyze the data. The system pre-establishes relationships between sensor data and metrology data using common identifiers, so when an engineer queries for problem analysis, the relevant data is already linked and ready for immediate review, eliminating manual data gathering and reducing errors.
Solution Approach 2:
The patent implements feedback mechanisms where the system automatically presents correlated data and analysis results to engineers based on their queries. When an engineer identifies a potential issue through metrology data, the system automatically retrieves and presents related sensor data, creating a feedback loop that accelerates the diagnostic process while maintaining comprehensive analysis capabilities.
3Loss of time
If comprehensive data integration is implemented to enable bidirectional drilling between sensor and metrology data, then root-cause analysis is expedited, but system complexity increases
Solution Approach 1:
The patent segments the data integration system into modular components: data collection modules, data storage modules with defined schemas, association/rules engines that create links between data types, and data presentation modules. Each segment handles a specific aspect of the integration, making the overall complex system manageable through clear separation of concerns and independent development of each component.
Data Source
AI summary
A method and system for linking sensor data to metrology data and metrology data to sensor data is described herein. In one embodiment, a user selection of metrology data for a product is received, related process tool fault detection summary for the selected metrology data for the product is presented, a user selection of a process tool from the process tool fault detection summary is received, and related fault detection details for the selected process tool are presented.