18075055. METHODS AND MECHANISMS FOR AUTOMATIC SENSOR GROUPING TO IMPROVE ANOMALY DETECTION simplified abstract (Applied Materials, Inc.)
Contents
METHODS AND MECHANISMS FOR AUTOMATIC SENSOR GROUPING TO IMPROVE ANOMALY DETECTION
Organization Name
Inventor(s)
Peter J. Lindner of Saratoga Springs NY (US)
John G. Albright of Stone Ridge NY (US)
Jimmy Iskandar of Fremont CA (US)
Michael D. Armacost of San Jose CA (US)
METHODS AND MECHANISMS FOR AUTOMATIC SENSOR GROUPING TO IMPROVE ANOMALY DETECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18075055 titled 'METHODS AND MECHANISMS FOR AUTOMATIC SENSOR GROUPING TO IMPROVE ANOMALY DETECTION
Simplified Explanation: The patent application describes an electronic device manufacturing system that uses data from sensors to detect anomalies in the manufacturing process.
- The system collects data from sensors during manufacturing processes.
- It analyzes the data to identify correlations between different sensors.
- If the correlation meets a certain threshold, the system groups the sensors into clusters.
- During subsequent manufacturing runs, the system generates anomaly scores for these clusters to detect any abnormalities in the process.
Key Features and Innovation:
- Utilizes data from sensors to monitor and analyze manufacturing processes.
- Detects correlations between sensors to improve process monitoring.
- Groups sensors into clusters based on correlations to enhance anomaly detection.
- Generates anomaly scores to identify abnormalities in manufacturing runs.
Potential Applications: The technology can be applied in various industries such as electronics manufacturing, semiconductor production, and pharmaceutical manufacturing to improve process efficiency and quality control.
Problems Solved:
- Enhances process monitoring and anomaly detection in manufacturing.
- Improves quality control by identifying abnormalities in real-time.
- Increases overall efficiency and reduces production errors.
Benefits:
- Enhances process efficiency and quality control.
- Reduces production errors and waste.
- Enables real-time anomaly detection for proactive problem-solving.
Commercial Applications: The technology can be utilized in electronic device manufacturing, semiconductor fabrication, and pharmaceutical production to streamline processes, improve product quality, and reduce manufacturing costs.
Prior Art: Prior research in the field of sensor data analysis and anomaly detection in manufacturing processes can provide valuable insights into similar technologies and approaches.
Frequently Updated Research: Stay informed about the latest advancements in sensor technology, data analytics, and anomaly detection algorithms to enhance the capabilities of the manufacturing system.
Questions about Electronic Device Manufacturing System: 1. How does the system determine correlations between sensors? 2. What are the potential challenges in implementing this technology in different manufacturing environments?
Original Abstract Submitted
An electronic device manufacturing system configured to obtain, by a processor, a plurality of datasets associated with a process recipe, wherein each dataset of the plurality of datasets comprises data generated by a plurality of sensors during a corresponding process run performed using the process recipe. The processor is further configured to determine, using the plurality of data sets associated with the process recipe, a correlation value between two or more sensors of the plurality of sensors. Responsive to the correlation value satisfying a threshold criterion, the processor assigns the two or more sensors to a cluster. During a subsequent process run, the processor generates an anomaly score associated with the cluster and indicative of an anomaly associated with at least one step of the subsequent process run.
