Monitoring Data Compression for Semiconductor Chamber Visualization
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Solution Overview
Problem
Existing information processing systems in semiconductor manufacturing struggle to efficiently analyze and visualize large volumes of multi-dimensional monitoring data from multiple chambers, making it difficult to identify individual differences and malfunctions.
Innovation Solution
The system acquires monitoring data, generates a dimensional compression model using machine learning techniques, and converts the data into a low-dimensional representation for visualization, allowing for the analysis of each category and chamber unit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If monitoring data from multiple chambers is collected and analyzed in detail, then analysis accuracy and malfunction detection capability are improved, but data processing time and computational complexity increase
Solution Approach 1:
The patent segments the analysis process into two distinct phases: an offline training phase where the dimensional compression model is built using principal component analysis, and an online analysis phase where the pre-built model rapidly compresses and analyzes new monitoring data. This segmentation allows computationally intensive operations to be performed only once during training, while subsequent analyses benefit from fast compression without repeating the full PCA computation.
Solution Approach 2:
The patent performs preliminary action by pre-computing the dimensional compression model using historical monitoring data before actual malfunction detection is needed. The principal component analysis is executed in advance to establish the transformation matrix and identify the most significant variance directions, so that when new data arrives, only a simple matrix multiplication is required rather than performing full PCA analysis each time.
2Loss of information
If high-dimensional monitoring data is processed and visualized, then information completeness is improved, but visualization complexity and difficulty of interpretation increase
Solution Approach 1:
The patent applies dimensionality change by transforming high-dimensional monitoring data into a lower-dimensional space using principal component analysis. The method identifies the top k principal components that capture the most variance in the data, projecting the original data onto these k dimensions. This allows the essential information to be preserved while reducing complexity, making visualization and interpretation feasible without losing critical patterns or relationships in the monitoring data.
3Productivity
If dimensional compression is applied to monitoring data, then data processing efficiency is improved, but risk of information loss increases
Solution Approach 1:
The patent uses parameter changes by adjusting the number of principal components k to balance compression efficiency and information retention. By selecting an appropriate k value (where k < original dimensionality), the system achieves dimensionality reduction while preserving the majority of variance in the data. The principal component analysis inherently prioritizes components that capture the most significant variations, ensuring that the most important information is retained in the compressed representation.
Data Source
AI summary
An information processing method includes, by an information processing apparatus: acquiring monitoring data which includes a plurality of items related to a state of each process performed by a target apparatus; generating a dimensional compression model for compressing the number of dimensions of the monitoring data; inputting the monitoring data into the dimensional compression model to convert the monitoring data into a low-dimensional representation; and outputting the monitoring data converted into the low-dimensional representation. In addition, the monitoring data may include a plurality of items classified into a plurality of categories, and the information processing apparatus may generate the dimensional compression model for each category, and may input the monitoring data into a dimensional compression model corresponding to each category to convert the monitoring data into a low-dimensional representation for each category.