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公开(公告)号：US12045969B2
公开(公告)日：2024-07-23
申请号：US17034640
申请日：2020-09-28
申请人： Carl Zeiss SMT GmbH
发明人： Jens Timo Neumann , Eugen Foca , Ramani Pichumani , Abhilash Srikantha , Christian Wojek , Thomas Korb , Joaquin Correa
CPC分类号： G06T7/0004 , H01L22/26 , G06T2207/10061 , G06T2207/20081 , G06T2207/30148
摘要： A method includes obtaining at least one 2-D image dataset of semiconductor structures formed on a wafer including one or more defects during a wafer run of a wafer using a predefined fabrication process. The method also includes determining, based on at least one machine-learning algorithm trained on prior knowledge of the fabrication process and based on the at least one 2-D image dataset, one or more process deviations of the wafer run from the predefined fabrication process as a root cause of the one or more defects. A 3-D image dataset may be determined as a hidden variable.
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2.发明申请公开(公告)号：US20200258212A1
公开(公告)日：2020-08-13
申请号：US16786307
申请日：2020-02-10
申请人： Carl Zeiss SMT GmbH
摘要： Methods for determining one or more quality or size parameters of a structure in a semiconductor product, on the basis of an image of the semiconductor product which was generated with the aid of charged particles which have been radiated onto the semiconductor product, include: providing the image of the semiconductor product; applying the provided image to a machine-learning-based method such as, e.g., an artificial neural network which has been trained with training images of semiconductor products and which is configured to generate an output parameter from the provided image; and determining the size parameter of the structure on the basis of the output parameter.
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公开(公告)号：US20240087134A1
公开(公告)日：2024-03-14
申请号：US18487844
申请日：2023-10-16
申请人： Carl Zeiss SMT GmbH
发明人： Dmitry Klochkov , Jens Timo Neumann , Thomas Korb , Eno Töppe , Johannes Persch , Abhilash Srikantha , Alexander Freytag
CPC分类号： G06T7/149 , G06T7/0004 , G06T2207/10028 , G06T2207/10061 , G06T2207/10072 , G06T2207/30148
摘要： A method identifies ring structures in pillars of high aspect ratio (HAR) structures. For segmentation of rings, a machine learning-logic is used. A two-step training method for the machine learning logic is described.
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公开(公告)号：US20210358101A1
公开(公告)日：2021-11-18
申请号：US17388589
申请日：2021-07-29
申请人： Carl Zeiss SMT GmbH
摘要： A method includes obtaining an image data set that depicts semiconductor components, and applying a hierarchical bricking to the image data set. In this case, the bricking includes a plurality of bricks on a plurality of hierarchical levels. The bricks on different hierarchical levels have different image element sizes of corresponding image elements.
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公开(公告)号：US20200285976A1
公开(公告)日：2020-09-10
申请号：US16807581
申请日：2020-03-03
发明人： Abhilash Srikantha , Christian Wojek , Keumsil Lee , Thomas Korb , Jens Timo Neumann , Eugen Foca
摘要： Methods for determining metrology sites for products includes detecting corresponding objects in measurement data of one or more product samples, and aligning the detected objects are aligned. The methods also include analyzing the aligned objects, and determining metrology sites based on the analysis. Devices use such methods to determine metrology sites for products.
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公开(公告)号：US11436506B2
公开(公告)日：2022-09-06
申请号：US16807581
申请日：2020-03-03
发明人： Abhilash Srikantha , Christian Wojek , Keumsil Lee , Thomas Korb , Jens Timo Neumann , Eugen Foca
IPC分类号： G06N20/00 , G06N5/04 , G06K9/00 , G06V10/82 , G06K9/62 , G01N23/06 , G01N23/046 , G01N23/22 , G01N21/65 , G06N3/04 , G06N3/08
摘要： Methods for determining metrology sites for products includes detecting corresponding objects in measurement data of one or more product samples, and aligning the detected objects are aligned. The methods also include analyzing the aligned objects, and determining metrology sites based on the analysis. Devices use such methods to determine metrology sites for products.
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公开(公告)号：US20210097673A1
公开(公告)日：2021-04-01
申请号：US17034640
申请日：2020-09-28
申请人： Carl Zeiss SMT GmbH
发明人： Jens Timo Neumann , Eugen Foca , Ramani Pichumani , Abhilash Srikantha , Christian Wojek , Thomas Korb , Joaquin Correa
摘要： A method includes obtaining at least one 2-D image dataset of semiconductor structures formed on a wafer including one or more defects during a wafer run of a wafer using a predefined fabrication process. The method also includes determining, based on at least one machine-learning algorithm trained on prior knowledge of the fabrication process and based on the at least one 2-D image dataset, one or more process deviations of the wafer run from the predefined fabrication process as a root cause of the one or more defects. A 3-D image dataset may be determined as a hidden variable.
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