Predictive modeling of metrology in semiconductor processes

    公开(公告)号:US11187992B2

    公开(公告)日:2021-11-30

    申请号:US16151035

    申请日:2018-10-03

    Abstract: Implementations described herein generally relate to improving silicon wafer manufacturing. In one implementation, a method includes receiving data from one or more manufacturing tools about a manufacturing process of a silicon wafer. The method further includes determining, based on the data, predictive information about a quality of the silicon wafer. The method further includes providing the predictive information to a manufacturing system, wherein the predictive information is used to determine whether to take corrective action.

    Using design proximity index and effect-to-design proximity ratio to control semiconductor processes and achieve enhanced yield

    公开(公告)号:US10579769B2

    公开(公告)日:2020-03-03

    申请号:US15829668

    申请日:2017-12-01

    Abstract: A method for detecting a design-impacting defect in an integrated circuit substrate is disclosed. In one implementation, a controller determines a distribution of intended geometric features in a design window of the integrated circuit substrate based on proximities of a plurality of points of interest in the design window to the intended geometric features. The controller obtains a set of intended contours from the distribution. The controller obtains a set of imaged contours from one or more images of the integrated circuit substrate. The controller compares the set of imaged contours to the set of intended contours to obtain a set of potential design-impacting defects in the intended geometric features. The controller determines a probability that a potential design-impacting defect from the set of potential design-impacting defects is a valid design-impacting defect. The controller takes a corrective action based on the determined probability.

    USING ELEMENTAL MAPS INFORMATION FROM X-RAY ENERGY-DISPERSIVE SPECTROSCOPY LINE SCAN ANALYSIS TO CREATE PROCESS MODELS

    公开(公告)号:US20230081446A1

    公开(公告)日:2023-03-16

    申请号:US17447337

    申请日:2021-09-10

    Abstract: Implementations disclosed describe a method of using a model to predict a change of a physical state of a sample caused by one or more stages of a technological process in a substrate processing apparatus and obtaining imaging data associated with an actual performance of the one or more stages of the technological process. The imaging data includes a distribution of one or more chemical elements for a number of regions of the sample. The method further includes identifying, based on the imaging data, a difference between the predicted change of the physical state of the sample and an actual change of the physical state of the sample caused by the actual performance of the one or more stages of the technological process. The method further includes determining parameters of the model based on the identified difference.

    Semiconductor process control method

    公开(公告)号:US10579041B2

    公开(公告)日:2020-03-03

    申请号:US15829626

    申请日:2017-12-01

    Abstract: Implementations described herein generally relate method for detecting excursions in time-series traces received from sensors of manufacturing tools. A server extracts one or more time series traces and metrology data collected from one or more sensors associated with one or more manufacturing tools configured to produce a silicon substrate. The server identifies one or more candidate excursions of the one or more time series traces by comparing the one or more time series traces to one or more traces associated with a working reference sensor. The server verifies that a candidate excursion of the one or more candidate excursions is a true excursion based on correlating the one or more time series traces to the metrology data. The server instructs a manufacturing system to take corrective action to remove the selected true excursion.

    PIECEWISE FUNCTIONAL FITTING OF SUBSTRATE PROFILES FOR PROCESS LEARNING

    公开(公告)号:US20240054333A1

    公开(公告)日:2024-02-15

    申请号:US17884462

    申请日:2022-08-09

    CPC classification number: G06N3/08

    Abstract: A method includes receiving, by a processing device, data indicative of a plurality of measurements of a profile of a substrate. The method further includes separating the data into a plurality of sets of data, a first set of the plurality of sets associated with a first region of the profile, and a second set of the plurality of sets associated with a second region of the profile. The method further includes fitting data of the first set to a first function to generate a first fit function. The first function is selected from a library of functions. The method further includes fitting data of the second set to a second function to generate a second fit function. The method further includes generating a piecewise functional fit of the profile of the substrate. The piecewise functional fit includes the first fit function and the second fit function.

    Data analytics and computational analytics for semiconductor process control

    公开(公告)号:US10481199B2

    公开(公告)日:2019-11-19

    申请号:US15829632

    申请日:2017-12-01

    Abstract: Implementations described herein generally relate to detecting excursions in intended geometric features in an integrated circuit substrate. In one implementation, a method includes determining a set of suspect contours in a design window of the integrated circuit substrate based on proximities of a plurality of points of interest in the design window to intended geometric features. The method further includes obtaining a set of imaged contours from one or more images of a defect-free integrated circuit substrate. The method further includes comparing the set of imaged contours to the set of suspect contours to obtain a set of potential excursions from the imaged contours. The method further includes determining a probability that a potential excursion from the set of potential excursions is a valid excursion. The method further includes taking a corrective action based on the determined probability.

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