Equipment Fail Mode Prediction from Multivariate Process Traces
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Solution Overview
Problem
Current anomaly detection systems in semiconductor manufacturing primarily rely on univariate analysis, which is insufficient for identifying equipment fail modes related to detected anomalies, limiting the ability to quickly respond to process instability and correct faults.
Innovation Solution
A predictive model that performs multivariate analysis of trace data using machine learning techniques, including the detection of anomalies, calculation of key features, and comparison with a database of past trace data to classify and determine the root cause of anomalies, enabling the retrieval of corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If univariate analysis is used for anomaly detection, then the detection process is simple and fast, but the ability to identify equipment fail modes is insufficient
Solution Approach 1:
The patent transitions from univariate analysis to multivariate analysis by examining multiple sensor traces simultaneously. The system analyzes correlations and patterns across multiple variables (sensor readings) to identify equipment fail modes, moving beyond single-variable detection to multi-dimensional pattern recognition that captures complex equipment states.
Solution Approach 2:
The patent introduces an intermediary classification system that bridges anomaly detection and root cause identification. A classification model is trained on historical trace data to map detected anomalies to specific fail modes, serving as a mediator that translates raw anomaly signals into actionable diagnostic information without requiring complex real-time analysis of all sensor data.
2Measurement precision
If multivariate analysis is implemented to improve fail mode identification, then the analysis accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing sensor trace data and pre-training classification models offline. Historical trace data is collected, labeled with known fail modes, and used to train classification models before deployment. This preliminary preparation reduces the computational burden during real-time operation, as the heavy multivariate analysis work is completed in advance.
Solution Approach 2:
The patent uses historical trace data as copies of real equipment operation to train classification models. Instead of performing complex multivariate analysis on live data only, the system creates a digital replica of equipment behavior through historical data, allowing the classification model to learn fail mode patterns without the computational overhead of real-time multivariate processing of all sensor streams.
3Reliability
If comprehensive sensor monitoring is performed to detect all anomalies, then the detection coverage is complete, but the difficulty of identifying root causes increases
Solution Approach 1:
The patent extracts relevant features from comprehensive sensor data to focus root cause analysis on critical information only. Instead of analyzing all sensor readings equally, the system identifies and extracts key features that are most indicative of specific fail modes, reducing the complexity of root cause identification while maintaining complete anomaly detection coverage through the comprehensive sensor network.
Data Source
AI summary
A predictive model for equipment fail modes. An anomaly is detected in a collection of trace data, then key features are calculated. A search is conducted for the same or similar anomalies having the same key features in a database of past trace data. If the same anomaly occurred before and is in the database, then the type of anomaly, its root cause, and action steps to correct can be retrieved from the database.