Process Trace Analysis for Equipment Fail Mode Prediction
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Solution Overview
Problem
Current anomaly detection in semiconductor manufacturing is primarily univariate, insufficient for identifying equipment fail modes, as it considers anomalies on a feature-by-feature basis, lacking the capability to effectively detect and classify root causes of process trace anomalies.
Innovation Solution
A predictive model employing multivariate analysis of sensor trace data using machine learning techniques, including the definition of windows for anomaly regions, calculation of key features, and comparison with a database of past trace data to classify anomalies and determine root causes and corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If univariate analysis is used for anomaly detection, then the analysis is simple and easy to implement, but it is insufficient for identifying equipment fail modes and root causes
Solution Approach 1:
The patent transitions from univariate (single-dimensional) analysis to multivariate (multi-dimensional) analysis by examining multiple sensor parameters simultaneously. This dimensional expansion enables the system to capture complex interrelationships between sensors and accurately identify equipment fail modes that cannot be detected through single-parameter analysis alone.
Solution Approach 2:
The patent combines multiple sensor traces and parameters into a unified multivariate analysis framework. By merging data from numerous sensors and analyzing them collectively through techniques like Principal Component Analysis (PCA), the system achieves comprehensive fault detection capability that surpasses the sum of individual univariate analyses.
2Reliability
If multivariate analysis is implemented to improve fail mode identification, then the ability to identify equipment fail modes is enhanced, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent segments the complex multivariate analysis into manageable components: (1) dividing sensor data into time-windowed segments, (2) computing statistical indicators for each segment, (3) applying PCA to reduce dimensionality, and (4) comparing segmented results against baseline profiles. This segmentation makes the computationally intensive multivariate analysis tractable and implementable in real-time manufacturing environments.
Solution Approach 2:
The patent introduces statistical indicators and Principal Component Analysis (PCA) as intermediary transformations between raw sensor data and fail mode identification. These intermediaries simplify the complex multivariate data structure into reduced-dimensional representations that retain essential fault information while reducing computational burden for subsequent analysis and comparison.
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.