Multi-Device Event Status Modeling for Semiconductor Anomaly Detection
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Solution Overview
Problem
Existing technologies face challenges in representing the status of multiple devices in semiconductor manufacturing apparatuses, making it difficult to select teacher data for machine learning applications using time-series data such as sounds, images, and vibrations.
Innovation Solution
An information processing device that acquires total event status information by synthesizing event status information from multiple devices and uses this information along with time-series detection results to perform machine learning processing, enabling the generation and determination of learning models for improved data representation and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring systems are used to track device status in semiconductor manufacturing, then vibration waveforms can be detected, but the status of multiple devices becomes difficult to represent and analyze
Solution Approach 1:
The patent segments the complex monitoring task by creating separate detection units for different device types (first detection unit for first device, second detection unit for second device). Each detection unit independently monitors its specific device, simplifying the overall system architecture while maintaining comprehensive coverage of multiple devices
Solution Approach 2:
The patent introduces an intermediary processing mechanism that receives detection results from multiple detection units and integrates them into a unified status representation. This intermediary layer transforms complex multi-device data into manageable information that can be effectively analyzed and displayed
2Reliability
If machine learning is applied to time-series data from manufacturing processes, then anomaly detection capability is improved, but selection of teacher data becomes difficult without easy device status representation
Solution Approach 1:
The patent performs preliminary organization of device status information before machine learning processing. By pre-structuring the status data from multiple devices into a standardized format, the system prepares the groundwork for efficient teacher data selection and machine learning model training, eliminating the need for complex data preprocessing later
Solution Approach 2:
The patent transforms raw detection data into standardized status parameters that are suitable for machine learning input. By changing the parameter representation format to a unified structure, the system makes teacher data selection straightforward while maintaining the information needed for accurate anomaly detection
Data Source
AI summary
An information processing device of the present invention includes a first acquisition unit, a second acquisition unit, and a machine learning processing unit. The first acquisition unit acquires total event status information. The second acquisition unit acquires time-series detection result information. The machine learning processing unit performs one or both of learning processing and determination processing. In the learning processing, a learning model is generated by performing machine learning with the time-series detection result information acquired by the second acquisition unit as an input for each piece of the total event status information acquired by the first acquisition unit. In the determination processing, a determination is performed on the generated learning model by inputting the time-series detection result information acquired by the second acquisition unit for each piece of the total event status information acquired by the first acquisition unit.