US 11,657,122 B2
Anomaly detection from aggregate statistics using neural networks
Jimmy Iskandar, Fremont, CA (US); and Michael D. Armacost, San Jose, CA (US)
Assigned to Applied Materials, Inc., Santa Clara, CA (US)
Filed by Applied Materials, Inc., Santa Clara, CA (US)
Filed on Jul. 16, 2020, as Appl. No. 16/947,052.
Prior Publication US 2022/0019863 A1, Jan. 20, 2022
Int. Cl. G06K 9/62 (2022.01); G06N 3/04 (2006.01)
CPC G06K 9/6298 (2013.01) [G06K 9/6284 (2013.01); G06K 9/6289 (2013.01); G06N 3/04 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
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;
generating, using a plurality of outlier detection models, a plurality of outlier scores, wherein one or more of the plurality of outlier scores are representative of a degree of presence, in the plurality of sensor statistics, of an anomaly associated with the manufacturing operation, and wherein each of the plurality of outlier scores is generated based on the reduced representation of the plurality of sensor statistics using a respective one of the plurality of outlier detection models; and
processing the plurality of outlier scores using a detector neural network to generate an anomaly score indicative of a likelihood of the anomaly associated with the manufacturing operation.