Process Simulation Database Optimization via Linearity Analysis
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Solution Overview
Problem
Analyzing and minimizing variations in process parameters across multiple production steps is challenging, especially when incorporating process history, as existing methods struggle to accurately predict outcomes while managing large datasets effectively.
Innovation Solution
An apparatus and method that utilize a process parameter classifier to calculate a linearity indicator for each varied process parameter, adjusting the number of included process simulation data points based on this indicator to create a processed database, reducing data volume while maintaining accuracy, and employing a process simulator to generate additional data for non-linear parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the initial number of process simulation data points is reduced, then the dataset size is reduced, but the prediction accuracy may deteriorate
Solution Approach 1:
The patent extracts and removes irrelevant process simulation data points from the dataset based on the linearity indicator calculation. By identifying and eliminating data points that do not contribute to prediction accuracy, the dataset size is reduced while maintaining the essential information needed for accurate predictions.
Solution Approach 2:
The patent changes the parameter of dataset composition by selectively retaining or removing data points based on the linearity indicator. This parameter change allows the dataset to be optimized in terms of size while preserving the critical data needed for accurate predictions.
2Measurement precision
If more process simulation data points are included, then the prediction accuracy is improved, but the data processing time and computational resources increase
Solution Approach 1:
The patent extracts only the necessary process simulation data points that contribute to prediction accuracy, eliminating redundant data. This extraction process reduces the computational burden and processing time while maintaining the essential information needed for accurate predictions.
Solution Approach 2:
The patent applies partial action by including only the sufficient number of data points needed for accurate predictions, rather than processing all available data. This approach achieves the required prediction accuracy without the excessive computational resources and time that would be needed to process the complete dataset.
3Adaptability or versatility
If all process parameters are considered with full data, then the analysis completeness is improved, but the device complexity and data management difficulty increase
Solution Approach 1:
The patent segments the process parameters and data points based on their linearity indicator values. By dividing the data into relevant and irrelevant segments, the system maintains complete analysis of important parameters while simplifying data management by excluding unnecessary data points.
Solution Approach 2:
The patent changes the data management approach by dynamically adjusting which data points are retained based on the linearity indicator parameter. This parameter-driven selection simplifies data management while preserving the completeness of analysis for influential parameters.
Data Source
AI summary
A process simulation database includes process simulation data of a plurality of process simulations based on a different variation of a plurality of process parameters. For each process parameter, for an initial number of different process parameter values, process simulation data of a process simulation are included in the process simulation database. A process parameter classifier calculates a linearity indicator for a varied process parameter based on an analysis of the process simulation database with respect to a linearity of an influence of a variation of at least the varied process parameter on the process simulation data in the process simulation database. Further, a process simulation data processor changes the initial number of different process parameter values of the varied process parameter, for which process simulation data of a process simulation are included in the process simulation database, based on the calculated linearity indicator, to obtain a processed process simulation database.