公开(公告)号：US20230384777A1

公开(公告)日：2023-11-30

申请号：US17824282

申请日：2022-05-25

Applicant: Applied Materials, Inc.

Inventor： Jui-Che Lin ,  Chao-Hsien Lee ,  Shauh-Teh Juang

IPC: G05B19/418

CPC classification number: G05B19/41885 ,  G05B19/4183 ,  G05B19/41875

Abstract: An electronic device manufacturing system configured to receive, by a processor, input data reflecting a feature related to a manufacturing process of a substrate. The manufacturing system is further configured to generate a characteristic sequence defining a relationship between at least two manufacturing parameters, and determine a relationship between one or more variables related to the feature and the characteristic sequence. The manufacturing system is further configured to determine a weight based on the determined relationship and apply the weight to the feature. The manufacturing system is further configured to train a machine-learning model in view of the weighted feature.

公开(公告)号：US20230306300A1

公开(公告)日：2023-09-28

申请号：US17704298

申请日：2022-03-25

Applicant: Applied Materials, Inc.

Inventor： Ping-Yu Chou ,  Ya-Chu Chang ,  Jui-Che Lin ,  Hao-Wei Peng ,  Chao-Hsien Lee ,  Shauh-Teh Juang

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: An electronic device manufacturing system configured to obtain sensor data associated with a deposition process performed in a process chamber to deposit a film stack on a surface of a substrate. The film stack can include a known film pattern and an unknown film pattern. The manufacturing system is further configured to input the sensor data into a first trained machine-learning model to obtain a first output value of the first trained machine-learning model. The first output value can be associated with the known film pattern. The manufacturing system is further configured to input the first output value into a second trained machine-learning model to obtain a second output value of the second trained machine-learning model. The second output value can be indicative of metrology data of the known film pattern.

公开(公告)号：US11120182B2

公开(公告)日：2021-09-14

申请号：US16526845

申请日：2019-07-30

Applicant: Applied Materials, Inc.

Inventor： Jason Zse-cherng Lin ,  Shauh-Teh Juang

IPC: G06F30/30

Abstract: A hot spot methodology incorporates wafer physical measurement with digital simulation for identifying and monitoring critical hot spots. Wafer physical data are collected from the processed wafer of the semiconductor device on a plurality of target locations. Hot spot candidates and corresponding simulation data are generated by digital simulation based on models and verifications of optical proximity and lithographic process correction according to the design data of a semiconductor device. Data analytics provides data correlation between the collected wafer physical data and the simulation data. Data analytics further performs data correction on the simulation data according to the wafer physical data that have best correlation with the simulation data to better predict critical hot spots.

公开(公告)号：US20230342016A1

公开(公告)日：2023-10-26

申请号：US17728021

申请日：2022-04-25

Applicant: Applied Materials, Inc.

Inventor： Jui-Che Lin ,  Yan-Jhu Chen ,  Chao-Hsien Lee ,  Shauh-Teh Juang ,  Pengyu Han ,  Wallace Wang

IPC: G06F3/04847 ,  G06F3/0481 ,  H01L21/02

CPC classification number: G06F3/04847 ,  G06F3/0481 ,  H01L21/02104

Abstract: An electronic device manufacturing system configured to receive, by a processor, input data reflecting a feature related to a manufacturing process of a substrate. The manufacturing system is further configured to train a machine-learning model based on the input data reflecting the feature. The manufacturing system is further configured to modify the machine-learning model in view of the virtual knob for the feature.

公开(公告)号：US12236077B2

公开(公告)日：2025-02-25

申请号：US17728021

申请日：2022-04-25

Applicant: Applied Materials, Inc.

Inventor： Jui-Che Lin ,  Yan-Jhu Chen ,  Chao-Hsien Lee ,  Shauh-Teh Juang ,  Pengyu Han ,  Wallace Wang

IPC: G06F3/04847 ,  G06F3/0481 ,  H01L21/02

Abstract: An electronic device manufacturing system configured to receive, by a processor, input data reflecting a feature related to a manufacturing process of a substrate. The manufacturing system is further configured to train a machine-learning model based on the input data reflecting the feature. The manufacturing system is further configured to modify the machine-learning model in view of the virtual knob for the feature.

公开(公告)号：US10754309B2

公开(公告)日：2020-08-25

申请号：US16525434

申请日：2019-07-29

Applicant: Applied Materials, Inc.

Inventor： Jason Zse-Cherng Lin ,  Shauh-Teh Juang

IPC: G05B13/02 ,  G05B19/418

Abstract: A system for auto defect screening using adaptive machine learning includes an adaptive model controller, a defect/nuisance library and a module for executing data modeling analytics. The adaptive model controller has a feed-forward path for receiving a plurality of defect candidates in wafer inspection, and a feedback path for receiving defects of interest already screened by one or more existing defect screening models after wafer inspection. The adaptive model controller selects data samples from the received data, interfaces with scanning electron microscope (SEM) review/inspection to acquire corresponding SEM results that validate if each data sample is a real defect or nuisance, and compiles model training and validation data. The module of executing data modeling analytics is adaptively controlled by the adaptive model controller to generate and validate one or more updated defect screening models using the model training and validation data according to a target specification.