Semiconductor Equipment Deterioration Prediction Across Trend Shifts
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Solution Overview
Problem
Existing methods for predicting equipment deterioration in semiconductor manufacturing and inspection equipment struggle when the trend component tendencies change from increasing to decreasing or vice versa, making it difficult to estimate equipment condition accurately.
Innovation Solution
A deterioration prediction system that includes an input device for time series data, an estimation unit to distinguish between setting changes and deterioration-induced fluctuations, a division unit to segment data into periods, a discrimination unit to identify trend components, and a prediction unit to forecast equipment deterioration based on these components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the trend component is extracted from time series data to estimate equipment deterioration, then the deterioration can be estimated when the trend has a single tendency (increasing or decreasing), but the method fails when the tendency changes from increase to decrease or vice versa
Solution Approach 1:
The time series data is divided into multiple periods based on detected change points where the trend tendency shifts. Each period is analyzed separately to extract trend components, allowing the system to handle changing tendencies by segmenting the data into manageable portions where each segment has a consistent trend direction.
Solution Approach 2:
The system dynamically adjusts the analysis by detecting change points in the trend tendency and adapting the period division accordingly. This dynamic approach allows the method to respond to changing equipment conditions and varying trend patterns, making the deterioration estimation adaptable to different operational phases.
2Reliability
If time series data is continuously monitored to detect equipment deterioration, then real-time prediction is achieved, but it becomes difficult to distinguish between fluctuations caused by setting changes and those caused by actual deterioration
Solution Approach 1:
The system uses feedback from trend analysis to identify change points, which then inform the period division strategy. By continuously monitoring the trend component and comparing it against expected patterns, the system can distinguish between normal setting adjustments and actual deterioration signals, reducing false positives in real-time prediction.
Solution Approach 2:
The method performs preliminary analysis to detect change points and establish period boundaries before conducting the main deterioration assessment. This preliminary action prepares the data structure in advance, making it easier to distinguish between setting changes and deterioration by having already segmented the data into periods with consistent characteristics.
Data Source
AI summary
A deterioration prediction system for a semiconductor manufacturing equipment or a semiconductor inspection equipment, including: an input device receiving, as an input, time series data indicating a state of the equipment; a deterioration prediction device having an estimation unit discriminating fluctuation in the time series data into fluctuation caused by changing setting of the equipment and fluctuation caused by deterioration of the equipment and estimating a time point when the setting is changed, a division unit dividing the time series data into the plurality of periods bounded by the time points, a discrimination unit discriminating at least a trend component from the fluctuation in the time series data in the period, and a prediction unit predicting the deterioration of the equipment based on at least the trend component; and an output device outputting a result of the prediction.