Wafer Parameter Analysis Using PCA Loading Vectors
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Solution Overview
Problem
Existing semiconductor manufacturing processes face challenges in efficiently analyzing multidimensional data generated during and after semiconductor processes, which are crucial for evaluating process quality and efficiency.
Innovation Solution
A data processing method utilizing a machine learning processor to perform dimensionality reduction through principal component analysis (PCA) and generate principal components and loading vectors, creating a guide line to analyze differences and relationships between data groups, facilitating efficient data analysis in a low-dimensional space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multidimensional data is analyzed using traditional methods, then measurement precision is maintained, but device complexity and analysis time increase significantly
Solution Approach 1:
The patent applies dimensionality reduction techniques to transform high-dimensional semiconductor process data into lower-dimensional representations while preserving essential patterns and relationships. This reduces computational complexity and facilitates easier visualization and analysis of process parameters without sacrificing measurement precision.
2Productivity
If dimensionality reduction is applied to multidimensional data, then productivity is improved through faster analysis, but loss of information may occur
Solution Approach 1:
The patent transforms process data by changing parameters through dimensionality reduction, converting multiple correlated parameters into fewer independent principal components. This transformation maintains the essential information content while improving analysis efficiency, as the principal components capture the variance in the original data.
3Measurement precision
If multiple parameters are monitored in semiconductor processes, then measurement precision is maintained, but ease of operation deteriorates due to complex data interpretation
Solution Approach 1:
The patent extracts the most significant patterns and relationships from complex multidimensional process data by identifying principal components and their loading vectors. This extraction process separates the essential information from the complexity, making it easier to interpret process variations and quality metrics while maintaining measurement precision.
Data Source
AI summary
A data processing method including receiving obtaining multidimensional data including a plurality of parameters of a wafer, generating a principal component and a loading vector based on the multidimensional data, wherein the principal component represents dimensionally reduced characteristics of the of the multidimensional data and the loading vector represents a weight of the principal component to the multidimensional data, generating a guide line based on a first group of parameters among the plurality of parameters and a second group of parameters among the plurality of parameters, and generating an analysis result of a parameter among the plurality of parameters of the wafer based on the guide line and the loading vector.