Search Region Feedback for Faster Reaction Condition Optimization
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Solution Overview
Problem
Existing search methods in chemical reaction devices and semiconductor processing take a long time to reach the optimum solution due to the simplistic change in search regions based on the difference between actual measurement values and target values, leading to slow improvement in achieving the target output.
Innovation Solution
A search device that generates a prediction data set, acquires actual measurement values, calculates improvement rates, and updates the known data set to refine the search region using a genetic algorithm, allowing for more efficient exploration of parameter spaces and faster convergence to optimal conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the search region is simply changed based on the difference between actual measurement value and target value, then the search method is easy to implement, but it takes a long time to reach the optimum solution
Solution Approach 1:
The patent changes the parameter used for search region adjustment from simple difference (target value - actual measurement value) to improvement rate (difference between actual measurement value and known value closest to target value). This parameter transformation enables faster convergence to the optimum solution while maintaining the simplicity of the search method.
Solution Approach 2:
The patent introduces a feedback mechanism where the improvement rate is calculated and used to dynamically adjust the search region. By continuously monitoring the improvement rate and using it to guide the next search region selection, the system achieves faster convergence without complex implementation.
2Measurement precision
If the search region is frequently adjusted to reduce the difference between actual measurement value and target value, then the accuracy of reaching target value is improved, but the search speed decreases
Solution Approach 1:
The patent transforms the accuracy assessment parameter from absolute difference to improvement rate, which measures the relative progress toward the target value. This allows the system to achieve high accuracy (by continuously improving the measurement) while maintaining search speed (by using improvement rate to guide efficient search region selection).
Data Source
AI summary
A prediction-data-set-generation-unit generates a prediction-data-set composed of a plurality of prediction-data where an explanatory-variable for an unknown-value of a response-variable and a prediction-value of the response-variable are associated with each other by using a known-data-set. A first-actual-measurement-value-acquisition-unit acquires an actual-measurement-value of the response-variable included in the prediction-data where the prediction-value is closest to a target-value. An improvement-rate-calculation-unit calculates an improvement-rate representing a difference between a known-value of the response-variable closest to the target-value and the actual-measurement-value. A known-data-set-update-unit adds the actual-measurement-value and a value of the explanatory-variable corresponding to the actual-measurement-value to the known-data-set in a case where the improvement-rate is equal to or higher than a target improvement-rate. A second-actual-measurement-value-acquisition-unit acquires an actual-measurement-value of the response-variable for a value of the explanatory-variable included in the prediction-data, which is not used for acquiring the actual-measurement-value by the first-actual-measurement-value-acquisition-unit, in a case where the improvement-rate is lower than the target improvement-rate.