Hierarchical Tool Anomaly Prediction for Wafer Grinding Sensors
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Solution Overview
Problem
Existing systems for detecting tool degradation or misalignment in semiconductor wafer grinding processes are prone to overfitting and fail to accurately predict anomalies in real-time, leading to potential material loss and inefficiencies in production.
Innovation Solution
A computer device with a processor that receives real-time datasets from sensors, generates a time slide window, and uses a random probability distribution curve to compare data for anomaly detection, providing prediction results to prevent tool failures and maintain production quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning systems are used for anomaly detection, then they can process real-time data, but they suffer from overfitting and fail to accurately predict tool degradation
Solution Approach 1:
The patent segments the anomaly detection task into multiple hierarchical levels: individual sensor anomaly detection, tool-level anomaly aggregation, and production-line-level prediction. This hierarchical segmentation allows the system to process complex multi-sensor data without overfitting, as each level operates with appropriate aggregation strategies that reduce noise and improve generalization.
Solution Approach 2:
The patent introduces a temporal dimension by implementing time-window-based sliding analysis and trend prediction. Instead of analyzing static snapshots, the system analyzes sequences of data points over time, enabling it to detect gradual tool degradation patterns that single-point measurements would miss, thereby improving both accuracy and reliability.
2Reliability
If comprehensive real-time monitoring is implemented, then detection coverage is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent divides the monitoring system into independent hierarchical levels that can process data in parallel. Each sensor and each tool processes its data locally, and only aggregated results are passed up the hierarchy. This segmentation enables comprehensive monitoring coverage while maintaining efficient processing times through distributed computation.
Solution Approach 2:
The patent implements selective anomaly detection at different hierarchical levels, focusing computational resources on detecting and analyzing only the most significant anomalies. Rather than processing every data point with equal depth, the system applies deeper analysis only where needed, reducing overall processing time while maintaining high detection coverage.
3Manufacturing precision
If frequent tool adjustments are made to maintain quality, then production quality is improved, but production efficiency decreases due to unnecessary adjustments
Solution Approach 1:
The patent implements predictive anomaly detection that identifies tool degradation trends before they actually cause quality defects. By detecting early signs of tool wear or misalignment and predicting future anomalies, the system enables maintenance to be performed proactively rather than reactively, ensuring quality is maintained while minimizing unnecessary adjustments.
Solution Approach 2:
The patent establishes a closed-loop feedback system where anomaly detection results and trend predictions are continuously fed back to adjust monitoring parameters and maintenance schedules. This feedback mechanism allows the system to learn from past performance and optimize the balance between quality maintenance and production efficiency, reducing unnecessary adjustments while preventing quality issues.
Data Source
AI summary
A computer device is programmed to receive a plurality of real-time datasets from one or more sensors associated with a tool to be analyzed, calibrate the plurality of real-time datasets, generate a time slide window for each real-time dataset of the plurality of real-time datasets, generate a random probability distribution curve, compare the random probability distribution curve to each time slide window to determine if the time slide window includes anomaly data, and generate prediction results based on the comparison.