公开(公告)号：US20230153574A1

公开(公告)日：2023-05-18

申请号：US18151156

申请日：2023-01-06

Applicant: Applied Materials, Inc.

Inventor： Heng HAO ,  Sreekar BHAVIRIPUDI ,  Shreekant GAYAKA

IPC: G06N3/04 ,  G06N3/08 ,  G05B19/418 ,  G06V10/82

CPC classification number: G06N3/04 ,  G06N3/08 ,  G05B19/4189 ,  G06V10/82 ,  G05B2219/32335 ,  G05B2219/45031

Abstract: Implementations described herein generally relate to a method for detecting anomalies in time-series traces received from sensors of manufacturing tools. A server feeds a set of training time-series traces to a neural network configured to derive a model of the training time-series traces that minimizes reconstruction error of the training time-series traces. The server extracts a set of input time-series traces from one or more sensors associated with one or more manufacturing tools configured to produce a silicon substrate. The server feeds the set of input time-series traces to the trained neural network to produce a set of output time series traces reconstructed based on the model. The server calculates a mean square error between a first input time series trace of the set of input time series traces and a corresponding first output time series trace of the set of output time-series traces. The server declares the sensor corresponding to the first input time-series trace as having an anomaly when the mean square error exceeds a pre-determined value.

公开(公告)号：US20200082245A1

公开(公告)日：2020-03-12

申请号：US16545908

申请日：2019-08-20

Applicant: Applied Materials, Inc.

Inventor： Heng HAO ,  Sreekar BHAVIRIPUDI ,  Shreekant GAYAKA

IPC: G06N3/04 ,  G06N3/08 ,  G05B19/418

Abstract: Implementations described herein generally relate to a method for detecting anomalies in time-series traces received from sensors of manufacturing tools. A server feeds a set of training time-series traces to a neural network configured to derive a model of the training time-series traces that minimizes reconstruction error of the training time-series traces. The server extracts a set of input time-series traces from one or more sensors associated with one or more manufacturing tools configured to produce a silicon substrate. The server feeds the set of input time-series traces to the trained neural network to produce a set of output time series traces reconstructed based on the model. The server calculates a mean square error between a first input time series trace of the set of input time series traces and a corresponding first output time series trace of the set of output time-series traces. The server declares the sensor corresponding to the first input time-series trace as having an anomaly when the mean square error exceeds a pre-determined value.

公开(公告)号：US20190121317A1

公开(公告)日：2019-04-25

申请号：US15792436

申请日：2017-10-24

Applicant: Applied Materials, Inc.

Inventor： Heng HAO ,  James Tom PYE ,  Sreekar BHAVIRIPUDI

IPC: G05B19/4063 ,  G05B19/18

Abstract: A method for detecting an anomaly in sensor data generated in a substrate processing apparatus is disclosed herein. A plurality of data sets is received. A first data set from a first sensor and second data set from a second sensor are selected. The first second sensors are defined as a sensor pair. A reference correlation is generated by selecting a subset of values in each data set for each of the first and second data sets. A difference of remaining data correlation outside the subset of values in each data set to the reference correlation is normalized. The normalized data set is filtered to smooth the normalized difference to avoid isolated outliers with high chance of false positive candidates. One or more anomalies are identified. Process parameters of the substrate processing apparatus are adjusted, based on the one or more identified anomalies from the filtered data set.

公开(公告)号：US20190005357A1

公开(公告)日：2019-01-03

申请号：US15635367

申请日：2017-06-28

Applicant: Applied Materials, Inc.

Inventor： Sreekar BHAVIRIPUDI ,  Shreekant GAYAKA

IPC: G06K9/62 ,  G06N3/08 ,  G06N3/04

CPC classification number: G06K9/6267 ,  G06K9/4628 ,  G06K9/6256 ,  G06K9/6271 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/08 ,  G06N3/082 ,  G06N20/00

Abstract: A method of classifying substrates with a metrology tool is herein disclosed. The method begins by training a deep learning framework using convolutional neural networks with a training dataset for classifying image dataset. Obtaining a new image from the meteorology tool. Running the new image through the deep learning framework to classify the new image.