公开(公告)号：US20240177286A1

公开(公告)日：2024-05-30

申请号：US18070744

申请日：2022-11-29

Applicant: APPLIED MATERIALS, INC.

Inventor： Yuanhong Guo ,  Sachin Dangayach ,  Rahul Reddy Komatireddi ,  Tianyuan Wu

IPC: G06T7/00 ,  G01N21/95

CPC classification number: G06T7/0004 ,  G01N21/9501 ,  G06T2207/30148

Abstract: The subject matter of this specification can be implemented in, among other things, methods, systems, computer-readable storage medium. A method can include a processing device storing a plurality of feature vectors representative of previously processed image frames that correspond to various substrate processing defects. The method further includes receiving first image data comprising one or more image frames indicative of a first substrate processing defect. The method further includes determining a first feature vector corresponding to the first image data. The method further includes determining a selection of the plurality of feature vectors based on a proximity between the first feature vector and each of the selection of the plurality of feature vectors. The method further includes determining second image data comprising one or more image frames corresponding to the selection of the plurality of embedding vectors and performing an action based on determining the second image data.

公开(公告)号：US20240144464A1

公开(公告)日：2024-05-02

申请号：US17976726

申请日：2022-10-28

Applicant: Applied Materials, Inc.

Inventor： Chandrani Roy Chowdhury ,  Sanjiv Mittal ,  James Henry Gardner, Jr. ,  Mohana Roy Chowdhury ,  Sachin Dangayach ,  Victor Rotion D'souza ,  Rajesh Kumar Singal ,  Rajesh Naidu Penagalapati ,  Priyanka Jain

IPC: G06T7/00 ,  G01N21/88 ,  G06V10/764

CPC classification number: G06T7/0008 ,  G01N21/8851 ,  G06V10/764 ,  G01N2021/8861 ,  G06T2207/20081

Abstract: A method includes obtaining, by a processing device, data indicative of locations of defects of a substrate. The method further includes generating an image indicating the locations of the defects. The method further includes providing the image indicating the locations of the defects to a trained machine learning model. The method further includes obtaining, as output from the trained machine learning model, a classification of the locations of the defects. The method further includes performing a corrective action in view of the output from the trained machine learning model.

公开(公告)号：US20250117915A1

公开(公告)日：2025-04-10

申请号：US18482209

申请日：2023-10-06

Applicant: Applied Materials, Inc.

Inventor： Navneet Kumar Singh ,  Arun Ramaswamy Srivatsa ,  Sachin Dangayach ,  Zvi Hersh Goldshtein ,  Rahul Reddy Komatireddi ,  Sutapa Dutta ,  Arv Nagpal ,  Yen-Tien Wu

IPC: G06T7/00

Abstract: Implementations disclosed describe, among other things, a systems and techniques for perform efficient inspection of a semiconductor manufacturing sample. The techniques include collecting optical inspection data for training sample(s) that have a plurality of defects. The techniques further include generating, using the optical inspection data, a training data set that includes descriptions, images, and ground truth classifications for the defects. The techniques further include using the training data set to train a plurality of machine learning (ML) classifiers to generate predicted classifications for the defects in the training sample(s). The techniques further include selecting, using the predicted classifications and the ground truth classifications, one or more ML classifiers that meet one or more accuracy criteria, and using the selected ML classifier(s) to classify defects in the semiconductor manufacturing sample.

公开(公告)号：US20220383121A1

公开(公告)日：2022-12-01

申请号：US17330096

申请日：2021-05-25

Applicant: Applied Materials, Inc.

Inventor： Tameesh Suri ,  Bor-Chau Juang ,  Nathaniel See ,  Bilal Shafi Sheikh ,  Naveed Zaman ,  Myron Shak ,  Sachin Dangayach ,  Udaykumar Diliprao Hanmante

IPC: G06N3/08 ,  G06N3/063

Abstract: A method of inducing sparsity for outputs of neural network layer may include receiving outputs from a layer of a neural network; partitioning the outputs into a plurality of partitions; identifying first partitions in the plurality of partitions that can be treated as having zero values; generating an encoding that identifies locations of the first partitions among remaining second partitions in the plurality of partitions; and sending the encoding and the second partitions to a subsequent layer in the neural network.