X-Ray Thin Film Analysis Validation Using Neural Networks
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Solution Overview
Problem
Existing X-ray analysis methods struggle to accurately determine the validity of thin film parameters without relying on expert judgment, as the R value alone is insufficient for confirming data accuracy.
Innovation Solution
A neural network is trained using teacher data to diagnose the validity of thin film parameters by analyzing X-ray intensity data, allowing non-experts to verify the accuracy of analysis results through machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If R value is used as evaluation value for thin film analysis, then analysis operation becomes simple and accessible to non-experts, but the ability to determine data accuracy is insufficient
Solution Approach 1:
A neural network is introduced as an intermediary between the simple R-value calculation and expert judgment. The neural network is trained on expert-diagnosed data and automatically provides accuracy assessment, bridging the gap between simple operation and reliable accuracy determination without requiring expert involvement in each analysis.
2Measurement precision
If expert judgment is required to confirm data accuracy, then measurement precision improves, but productivity decreases due to increased processing time and cost
Solution Approach 1:
The neural network is trained by copying expert knowledge from teacher data (expert-diagnosed analysis results). Once trained, the network replicates expert-level accuracy assessment capability automatically, eliminating the need for actual expert involvement in each analysis while maintaining high measurement precision.
3Reliability
If comprehensive validation methods are implemented to ensure parameter validity, then reliability of analysis results improves, but device complexity increases
Solution Approach 1:
Complex mechanical/expert validation processes are replaced with a neural network-based automated system. The neural network performs comprehensive parameter validity diagnosis through learned patterns from training data, achieving high reliability without the complexity of manual expert validation or complex validation algorithms.
Data Source
AI summary
According to an aspect of the present invention, provided is an information processing apparatus, comprising: a processor configured to execute a program so as to output a diagnostic result diagnosing an analysis profile result by inputting an input profile result in relation to an intensity of X-ray from a thin film and the analysis result of the input profile result to a neural network, wherein the neural network is a neural network that is allowed to machine-learn teacher data using input profile data in relation to an intensity of X-ray from a thin film and analysis profile data obtained from the input profile data as input data, and using diagnostic data obtained by diagnosing the analysis profile data as output data.