Substrate Transfer Abnormality Prediction Using Similarity Reference Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing substrate transfer devices lack accurate methods for predicting abnormality occurrence, which can lead to inefficiencies and downtime in substrate processing systems.
Innovation Solution
An abnormality management method that involves acquiring target data on the transfer operation of substrate transfer devices, comparing it with reference data, and estimating an abnormality occurrence prediction time based on similar historical data to output user-visible information for proactive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring methods are used for substrate transfer devices, then device operation continues without interruption, but abnormality prediction accuracy is insufficient leading to unexpected downtime
Solution Approach 1:
The system performs preliminary analysis by comparing current transfer operation data with historical abnormality data before actual failure occurs. The abnormality determination unit proactively identifies patterns that precede failures, enabling maintenance to be scheduled in advance rather than reacting to unexpected breakdowns.
Solution Approach 2:
The monitoring system segments the analysis by separating normal operation data from abnormality precursor data. The determination unit divides historical data into training datasets for learning normal patterns and test datasets for identifying abnormality signatures, enabling precise differentiation between normal variations and failure precursors.
2Measurement precision
If comprehensive monitoring data is collected and analyzed, then abnormality prediction accuracy improves, but system complexity increases
Solution Approach 1:
The system creates a virtual model by copying and storing historical transfer operation data including both normal and abnormal patterns. This digital repository serves as a reference library that the determination unit queries to compare against current operations, eliminating the need for complex real-time analysis algorithms while maintaining high detection precision.
Solution Approach 2:
The system implements feedback by continuously comparing current transfer operation features with historical abnormality patterns. When similarities are detected, the system provides feedback signals that trigger maintenance alerts, creating a closed-loop monitoring system that improves accuracy through iterative learning from past failures.
Data Source
AI summary
An abnormality management method used in a substrate transfer device includes: an acquisition operation of acquiring target data, which is transition data of an abnormality scale calculated based on a feature amount relating to a transfer operation of the substrate transfer device; a specification operation of comparing the target data acquired in the acquisition operation with plural pieces of reference data and specifying at least one piece of the reference data similar to the target data among the plural pieces of reference data based on a comparison result; an estimation operation of estimating an abnormality occurrence prediction time in the substrate transfer device from which the target data has been acquired, based on an abnormality occurrence time in the at least one piece of reference data specified in the specification operation; and an output operation of outputting information which indicates the abnormality occurrence prediction time estimated in the estimation operation.