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公开(公告)号：US20230408932A1
公开(公告)日：2023-12-21
申请号：US18034903
申请日：2021-11-30
发明人： Tamer COSKUN , Yen-Shuo LIN , Aidyn KEMELDINOV
CPC分类号： G03F7/706841 , G03F9/7092 , G06V10/7515 , G06V10/764 , G06T7/73 , G06T7/337 , G06T7/001 , G06T2207/30204 , G06T2207/20081 , G06T2207/30148
摘要： Embodiments described herein relate to a system, methods, and non-transitory computer-readable mediums that accurately align subsequent patterned layers in a photoresist utilizing a deep learning model and utilizing device patterns to replace alignment marks in lithography processes. The deep learning model is trained to recognize unique device patterns called alignment patterns in the FOV of the camera. Cameras in the lithography system capture images of the alignment patterns. The deep learning model finds the alignment patterns in the field of view of the cameras. An ideal image generated from a design file is matched with the camera with respect to the center of the field of view of the camera. A shift model and a rotation model are output from the deep learning model to create an alignment model. The alignment model is applied to the currently printing layer.
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公开(公告)号：US20230273528A1
公开(公告)日：2023-08-31
申请号：US18018034
申请日：2021-07-29
发明人： Rencheng SUN , Qi JIA , Meng LIU , Weixuan HU , Jen-Yi WUU , Hao CHEN
IPC分类号： G03F7/20 , G03F7/00 , G06N20/00 , G05B19/4099
CPC分类号： G03F7/705 , G03F7/70441 , G03F7/706841 , G06N20/00 , G05B19/4099 , G05B2219/45031
摘要： A method for selecting patterns for training a model to predict patterns to be printed on a substrate. The method includes (a) obtaining images of multiple patterns, wherein the multiple patterns correspond to target patterns to be printed on a substrate; (b) grouping the images into a group of special patterns and multiple groups of main patterns; and (c) outputting a set of patterns based on the images as training data for training the model, wherein the set of patterns includes the group of special patterns and a representative main pattern from each group of main patterns.
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公开(公告)号：US20240085805A1
公开(公告)日：2024-03-14
申请号：US18263305
申请日：2022-01-28
申请人： NOVA LTD.
发明人： Gilad BARAK , Amir Sagiv , Yishai Schreiber , Jacob Ofek , Zvi Gorohovsky , Daphna Peimer
IPC分类号： G03F7/00 , G01N21/956
CPC分类号： G03F7/7065 , G01N21/95607 , G03F7/70625 , G03F7/706831 , G03F7/706833 , G03F7/706841 , H01L22/12
摘要： A semiconductor device metrology including creating a time-domain representation of wavelength-domain measurement data of light reflected by a patterned structure of a semiconductor device, selecting a relevant and irrelevant portion of the time-domain representation, and determining one or more measurements of one or more parameters of interest of the patterned structure by performing model-based processing using the relevant portion of the time-domain representation.
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4.发明公开公开(公告)号：US20240085777A1
公开(公告)日：2024-03-14
申请号：US18326659
申请日：2023-05-31
发明人： Dae Young PARK , Jeong Hoon KO , Seong Ryeol KIM , Young-Gu KIM , Tae Hoon KIM , Hyun Joong KIM , Young Ju LEE
CPC分类号： G03F1/36 , G03F1/80 , G03F1/84 , G03F7/70625 , G03F7/70683 , G03F7/706841
摘要： Provided is a process proximity effect correction method capable of efficiently improving the dispersion of patterns. There is a process proximity effect correction method according to some embodiments, the process proximity effect correction method of a process proximity effect correction device for performing process proximity effect correction (PPC) of a plurality of patterns using a machine learning module executed by a processor, comprising: training a sensitivity model by inputting a layout image of the plurality of patterns and a layout critical dimension (CD) of the plurality of patterns into the machine learning module; estimating an after cleaning inspection critical dimension (ACI-CD) sensitivity prediction value of the plurality of patterns by inferring an ACI-CD prediction value of the plurality of patterns; and determining a correction rate of the layout CD of the plurality of patterns using the estimated sensitivity prediction value.
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5.发明公开公开(公告)号：US20240069450A1
公开(公告)日：2024-02-29
申请号：US18267734
申请日：2021-12-08
发明人： Nabeel Noor MOIN , Chenxi LIN , Yi ZOU
CPC分类号： G03F7/7065 , G03F7/706841 , G06N20/20
摘要： A method and apparatus for training a defect location prediction model to predict a defect for a substrate location is disclosed. A number of datasets having data regarding process-related parameters for each location on a set of substrates is received. Some of the locations have partial datasets in which data regarding one or more process-related parameters is absent. The datasets are processed to generate multiple parameter groups having data for different sets of process-related parameters. For each parameter group, a sub-model of the defect location prediction model is created based on the corresponding set of process-related parameters and trained using data from the parameter group. A trained sub-model(s) may be selected based on process-related parameters available in a candidate dataset and a defect prediction may be generated for a location associated with the candidate dataset using the selected sub-model.
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公开(公告)号：US20230315027A1
公开(公告)日：2023-10-05
申请号：US18013154
申请日：2021-06-17
发明人： Koos VAN BERKEL , Joost Johan BOLDER , Stijn BOSMA
CPC分类号： G05B13/027 , G03F7/706841 , G06N3/084
摘要： Variable setpoints and/or other factors may limit iterative learning control for moving components of an apparatus. The present disclosure describes a processor configured to control movement of a component of an apparatus with at least one prescribed movement. The processor is configured to receive a control input such as and/or including a variable setpoint. The control input indicates the at least one prescribed movement for the component. The processor is configured to determine, with a trained artificial neural network, based on the control input, a feedforward output for the component. The artificial neural network is pretrained with a training data set such that the artificial neural network determines the output regardless of whether or not the control input falls outside the training data set. The processor controls the component based on at least the output.
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公开(公告)号：US20230280662A1
公开(公告)日：2023-09-07
申请号：US18023708
申请日：2021-08-17
CPC分类号： G03F7/706841 , G03F7/70133
摘要： Methods of performing metrology. In one arrangement a substrate has a layer. The layer comprises a two-dimensional material. A target portion of the layer is illuminated with a beam of radiation and a distribution of radiation in a pupil plane is detected to obtain measurement data. The measurement data is processed to obtain metrology information about the target portion of the layer. The illuminating, detecting and processing are performed for plural different target portions of the layer to obtain metrology information for the plural target portions of the layer.
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公开(公告)号：US20230252347A1
公开(公告)日：2023-08-10
申请号：US18015162
申请日：2021-07-07
CPC分类号： G06N20/00 , G03F7/706841 , G03F7/70516 , G03F7/705
摘要： Method and apparatus for adapting a distribution model of a machine learning fabric. The distribution model is for mitigating the effect of concept drift, and is configured to provide an output as input to a functional model of the machine learning fabric. The functional model is for performing a machine learning task. The method may include obtaining a first data point, and providing the first data point as input to one or more distribution monitoring components of the distribution model. The one or more distribution monitoring components have been trained on a plurality of further data points. A metric representing a correspondence between the first data point and the plurality of further data points is determined, by at least one of the one or more distribution monitoring components. Based on the error metric, the output of the distribution model is adapted.
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