View source for 18075055. METHODS AND MECHANISMS FOR AUTOMATIC SENSOR GROUPING TO IMPROVE ANOMALY DETECTION simplified abstract (Applied Materials, Inc.)

=METHODS AND MECHANISMS FOR AUTOMATIC SENSOR GROUPING TO IMPROVE ANOMALY DETECTION=


==Organization Name==

[[:Category:Applied Materials, Inc.|Applied Materials, Inc.]]


[[Category:Applied Materials, Inc.]]

==Inventor(s)==

[[:Category:Peter J. Lindner of Saratoga Springs NY (US)|Peter J. Lindner of Saratoga Springs NY (US)]][[Category:Peter J. Lindner of Saratoga Springs NY (US)]]

[[:Category:John G. Albright of Stone Ridge NY (US)|John G. Albright of Stone Ridge NY (US)]][[Category:John G. Albright of Stone Ridge NY (US)]]

[[:Category:Jimmy Iskandar of Fremont CA (US)|Jimmy Iskandar of Fremont CA (US)]][[Category:Jimmy Iskandar of Fremont CA (US)]]

[[:Category:Michael D. Armacost of San Jose CA (US)|Michael D. Armacost of San Jose CA (US)]][[Category: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.



[[Category:G06F18/25]]
[[Category:G06F18/21]]
[[Category:G06F18/2113]]
[[Category:G06F18/23]]
[[Category:G06F18/2433]]
[[Category:CPC_G06F18/25]]