Smart Transducer Interface Modules for Sensor Network Configuration
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Solution Overview
Problem
Current data logging systems for substrate processing tools are limited by the need for dedicated leads or cables for each sensor, complex software for data analysis, and inflexible reporting frequencies, making it difficult to configure and monitor process parameters effectively.
Innovation Solution
A user-configurable data collection system using Smart Transducer Interface Modules (STIMs) connected via a network, allowing multiple sensors to be sampled at different rates and configured for specific applications, with syntactic pattern recognition for analyzing data patterns to identify process anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dedicated leads or cables are used for each sensor, then sensor data can be collected, but the device complexity and ease of operation deteriorate due to the need for multiple cables and complex configuration
Solution Approach 1:
Multiple sensor connections are merged into a single network infrastructure. Sensors are connected through a common network interface rather than requiring individual dedicated cables, consolidating multiple connection paths into a unified communication system that reduces physical complexity while maintaining data collection reliability
Solution Approach 2:
The network interface is designed to handle multiple sensor types and data collection functions through a single universal connection system. This multi-functional interface can accommodate various sensor protocols and data formats without requiring separate dedicated infrastructure for each sensor type
2Loss of information
If complex software is used for data analysis, then data analysis capability is improved, but ease of operation and ease of manufacture worsen due to difficulty in customization and implementation
Solution Approach 1:
The system performs automated data analysis and pattern recognition without requiring complex manual software configuration. The intelligent agent automatically collects sensor data, analyzes patterns, and generates alerts based on predefined criteria, allowing the system to serve itself rather than requiring extensive user programming or configuration
Solution Approach 2:
The system uses configurable parameters and thresholds that can be easily adjusted without changing the underlying complex software architecture. By exposing key analysis parameters as simple configurable values, the system maintains sophisticated analysis capability while improving ease of operation through parameter-based customization rather than code-level configuration
3Productivity
If uniform reporting frequency is used for sensor data, then data collection is simplified, but measurement precision and adaptability worsen when different sensors require different sampling rates
Solution Approach 1:
The data collection system dynamically adjusts sampling frequencies for different sensors based on their specific requirements. Rather than using a static uniform sampling rate, the system adaptively modifies collection intervals and rates to match each sensor's optimal sampling characteristics, improving measurement precision while maintaining overall system efficiency through intelligent resource allocation
Data Source
AI summary
A sensor network collects time-series data from a process tool and supplies the data to an analysis system where pattern analysis techniques are used to identify structures and to monitor subsequent data based on analysis instructions or a composite model. Time-series data from multiple process runs are used to form a composite model of a data structure including variation. Comparison with the composite model gives an indication of tool health. A sensor network may have distributed memory for a more simplified configuration.