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公开(公告)号:US11929291B2
公开(公告)日:2024-03-12
申请号:US17445727
申请日:2021-08-23
Applicant: NOVA LTD.
Inventor: Gil Loewenthal , Shay Yogev , Yoav Etzioni
CPC classification number: H01L22/26 , G01B11/24 , G01N21/9501 , H01L21/31111 , H01L21/67253 , G01B2210/56 , H10B41/27 , H10B43/27
Abstract: Controlling an etch process applied to a multi-layered structure, by calculating a spectral derivative of reflectance of an illuminated region of interest of a multi-layered structure during an etch process applied to the multi-layered structure, identifying in the spectral derivative a discontinuity that indicates that an edge of a void formed by the etch process at the region of interest has crossed a layer boundary of the multi-layered structure, determining that the crossed layer boundary corresponds to a preselected layer boundary of the multi-layered structure, and applying a predefined control action to the etch process responsive to determining that the crossed layer boundary corresponds to the preselected layer boundary of the multi-layered structure.
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公开(公告)号:US11300948B2
公开(公告)日:2022-04-12
申请号:US16454242
申请日:2019-06-27
Applicant: GLOBALFOUNDRIES INC. , NOVA LTD.
Inventor: Taher Kagalwala , Alok Vaid , Shay Yogev , Matthew Sendelbach , Paul Isbester , Yoav Etzioni
IPC: G05B19/418 , G05B13/02
Abstract: A process control method for manufacturing semiconductor devices, including determining a quality metric of a production semiconductor wafer by comparing production scatterometric spectra of a production structure of the production wafer with reference scatterometric spectra of a reference structure of reference semiconductor wafers, the production structure corresponding to the reference structure, the reference spectra linked by machine learning to a reference measurement value of the reference structure, determining a process control parameter value (PCPV) of a wafer processing step, the PCPV determined based on measurement of the production wafer and whose contribution to the PCPV is weighted with a first predefined weight based on the quality metric, and based on a measurement of a different wafer and whose contribution to the PCPV is weighted with a second predefined weight based on the quality metric, and controlling, with the PCPV, the processing step during fabrication.
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公开(公告)号:US11107738B2
公开(公告)日:2021-08-31
申请号:US16349287
申请日:2017-11-16
Applicant: NOVA LTD.
Inventor: Gil Loewenthal , Shay Yogev , Yoav Etzioni
IPC: H01L21/66 , G01B11/24 , G01N21/95 , H01L21/311 , H01L21/67 , H01L27/11556 , H01L27/11582
Abstract: Controlling an etch process applied to a multi-layered structure, by calculating a spectral derivative of reflectance of an illuminated region of interest of a multi-layered structure during an etch process applied to the multi-layered structure, identifying in the spectral derivative a discontinuity that indicates that an edge of a void formed by the etch process at the region of interest has crossed a layer boundary of the multi-layered structure, determining that the crossed layer boundary corresponds to a preselected layer boundary of the multi-layered structure, and applying a predefined control action to the etch process responsive to determining that the crossed layer boundary corresponds to the preselected layer boundary of the multi -layered structure.
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公开(公告)号:US12236364B2
公开(公告)日:2025-02-25
申请号:US18369221
申请日:2023-09-18
Applicant: NOVA LTD
Inventor: Eitan Rothstein , Ilya Rubinovich , Noam Tal , Barak Bringoltz , Yongha Kim , Ariel Broitman , Oded Cohen , Eylon Rabinovich , Tal Zaharoni , Shay Yogev , Daniel Kandel
Abstract: A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.
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公开(公告)号:US11815819B2
公开(公告)日:2023-11-14
申请号:US17995706
申请日:2021-04-06
Applicant: NOVA LTD.
Inventor: Barak Bringoltz , Ran Yacoby , Noam Tal , Shay Yogev , Boaz Sturlesi , Oded Cohen
CPC classification number: G03F7/70508 , G03F7/705 , G03F7/70525 , G05B13/0265 , G05B2219/2602 , G05B2219/45031
Abstract: A system and methods for Advance Process Control (APC) in semiconductor manufacturing include: for each of a plurality of waiter sites, receiving a pre-process set of scatterometric training data, measured before implementation of a processing step, receiving a corresponding post-process set of scatterometric training data measured after implementation of the process step, and receiving a set of process control knob training data indicative of process control knob settings applied during implementation of the process step; and generating a machine learning model correlating variations in the pre-process sets of scatterometric training data and the corresponding process control knob training data with the corresponding post-process sets of scatterometric training data, to train the machine learning model to recommend changes to process control knob settings to compensate for variations in the pre-process scatterometric data.
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公开(公告)号:US11763181B2
公开(公告)日:2023-09-19
申请号:US17400157
申请日:2021-08-12
Applicant: NOVA LTD
Inventor: Eitan Rothstein , Ilya Rubinovich , Noam Tal , Barak Bringoltz , Yongha Kim , Ariel Broitman , Oded Cohen , Eylon Rabinovich , Tal Zaharoni , Shay Yogev , Daniel Kandel
CPC classification number: G06N5/04 , G01B11/06 , G03F7/705 , G03F7/70616 , G06N20/00 , H01L21/681 , H01L22/26 , G01B2210/56
Abstract: A semiconductor metrology system including a spectrum acquisition tool for collecting, using a first measurement protocol, baseline scatterometric spectra on first semiconductor wafer targets, and for various sources of spectral variability, variability sets of scatterometric spectra on second semiconductor wafer targets, the variability sets embodying the spectral variability, a reference metrology tool for collecting, using a second measurement protocol, parameter values of the first semiconductor wafer targets, and a training unit for training, using the collected spectra and values, a prediction model using machine learning and minimizing an associated loss function incorporating spectral variability terms, the prediction model for predicting values for production semiconductor wafer targets based on their spectra.
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