Invention Application
- Patent Title: ANOMALY DETECTION FROM AGGREGATE STATISTICS USING NEURAL NETWORKS
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Application No.: US16947052Application Date: 2020-07-16
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Publication No.: US20220019863A1Publication Date: 2022-01-20
- Inventor: Jimmy Iskandar , Michael D. Armacost
- Applicant: Applied Materials, Inc.
- Applicant Address: US CA Santa Clara
- Assignee: Applied Materials, Inc.
- Current Assignee: Applied Materials, Inc.
- Current Assignee Address: US CA Santa Clara
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06N3/04

Abstract:
Implementations disclosed describe a method and a system to perform the method of obtaining a reduced representation of a plurality of sensor statistics representative of data collected by a plurality of sensors associated with a device manufacturing system performing a manufacturing operation. The method further includes generating, using a plurality of outlier detection models, a plurality of outlier scores, each of the plurality of outlier scores generated based on the reduced representation of the plurality of sensor statistics using a respective one of the plurality of outlier detection models. The method further includes processing the plurality of outlier scores using a detector neural network to generate an anomaly score indicative of a likelihood of an anomaly associated with the manufacturing operation.
Public/Granted literature
- US11657122B2 Anomaly detection from aggregate statistics using neural networks Public/Granted day:2023-05-23
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