公开(公告)号：US20240184858A1

公开(公告)日：2024-06-06

申请号：US18075055

申请日：2022-12-05

Applicant: Applied Materials, Inc.

Inventor： Peter J. Lindner ,  John G. Albright ,  Jimmy Iskandar ,  Michael D. Armacost

IPC: G06F18/25 ,  G06F18/21 ,  G06F18/2113 ,  G06F18/23 ,  G06F18/2433

CPC classification number: G06F18/25 ,  G06F18/2113 ,  G06F18/2193 ,  G06F18/23 ,  G06F18/2433

Abstract: 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.

公开(公告)号：US20250165591A1

公开(公告)日：2025-05-22

申请号：US18513930

申请日：2023-11-20

Applicant: Applied Materials, Inc.

Inventor： Peter J. Lindner ,  Chih Wei Lin ,  Jimmy Iskandar ,  Michael D. Armacost

IPC: G06F21/55 ,  G06N20/20

Abstract: A system configured to obtain current trace data associated with a substrate processing system and provide the trace data as input to a first predictive subsystem trained to detect anomalies using a first technique. Responsive to detecting an anomaly in the trace data, the system provides the trace data as input to a second predictive subsystem trained to detect anomalies using a second technique. Output data obtained from the first predictive subsystem and the second predictive subsystem is provided to a third predictive subsystem. Output data from the third predictive subsystem is obtained. The output data is reflective of a trace shape associated with the anomaly. Based on the trace shape, a type of issue that caused the anomaly in the trace data is identified.