Patents by Inventor Sharon Aharon
Sharon Aharon has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 12013634Abstract: Metrology methods and targets are provided for reducing or eliminating a difference between a device pattern position and a target pattern position while maintaining target printability, process compatibility and optical contrast—in both imaging and scatterometry metrology. Pattern placement discrepancies may be reduced by using sub-resolved assist features in the mask design which have a same periodicity (fine pitch) as the periodic structure and/or by calibrating the measurement results using PPE (pattern placement error) correction factors derived by applying learning procedures to specific calibration terms, in measurements and/or simulations. Metrology targets are disclosed with multiple periodic structures at the same layer (in addition to regular target structures), e.g., in one or two layers, which are used to calibrate and remove PPE, especially when related to asymmetric effects such as scanner aberrations, off-axis illumination and other error sources.Type: GrantFiled: December 6, 2022Date of Patent: June 18, 2024Assignee: KLA-TENCOR CORPORATIONInventors: Yoel Feler, Vladimir Levinski, Roel Gronheid, Sharon Aharon, Evgeni Gurevich, Anna Golotsvan, Mark Ghinovker
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Publication number: 20230099105Abstract: Metrology methods and targets are provided for reducing or eliminating a difference between a device pattern position and a target pattern position while maintaining target printability, process compatibility and optical contrast—in both imaging and scatterometry metrology. Pattern placement discrepancies may be reduced by using sub-resolved assist features in the mask design which have a same periodicity (fine pitch) as the periodic structure and/or by calibrating the measurement results using PPE (pattern placement error) correction factors derived by applying learning procedures to specific calibration terms, in measurements and/or simulations. Metrology targets are disclosed with multiple periodic structures at the same layer (in addition to regular target structures), e.g., in one or two layers, which are used to calibrate and remove PPE, especially when related to asymmetric effects such as scanner aberrations, off-axis illumination and other error sources.Type: ApplicationFiled: December 6, 2022Publication date: March 30, 2023Inventors: Yoel Feler, Vladimir Levinski, Roel Gronheid, Sharon Aharon, Evgeni Gurevich, Anna Golotsvan, Mark Ghinovker
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Patent number: 11537043Abstract: Metrology methods and targets are provided for reducing or eliminating a difference between a device pattern position and a target pattern position while maintaining target printability, process compatibility and optical contrast—in both imaging and scatterometry metrology. Pattern placement discrepancies may be reduced by using sub-resolved assist features in the mask design which have a same periodicity (fine pitch) as the periodic structure and/or by calibrating the measurement results using PPE (pattern placement error) correction factors derived by applying learning procedures to specific calibration terms, in measurements and/or simulations. Metrology targets are disclosed with multiple periodic structures at the same layer (in addition to regular target structures), e.g., in one or two layers, which are used to calibrate and remove PPE, especially when related to asymmetric effects such as scanner aberrations, off-axis illumination and other error sources.Type: GrantFiled: January 28, 2021Date of Patent: December 27, 2022Assignee: KLA-TENCOR CORPORATIONInventors: Yoel Feler, Vladimir Levinski, Roel Gronheid, Sharon Aharon, Evgeni Gurevich, Anna Golotsvan, Mark Ghinovker
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Patent number: 11101153Abstract: A parameter-stable misregistration measurement amelioration system and method including providing a wafer, including a plurality of multilayered semiconductor devices formed thereon, selected from a batch wafers intended to be identical, using a misregistration metrology tool to measure misregistration at multiple sites between at least a first layer and a second layer of the wafer, using a plurality of sets of measurement parameters, thereby generating measured misregistration data for each of the sets of measurement parameters, identifying and removing a parameter-dependent portion and a mean error portion from the measured misregistration data for the wafer for each of the sets of measurement parameters, thereby generating ameliorated parameter-stable ameliorated misregistration data for the wafer.Type: GrantFiled: August 23, 2019Date of Patent: August 24, 2021Assignee: KLA CorporationInventors: Vladimir Levinski, Yuri Paskover, Sharon Aharon, Amnon Manassen
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Publication number: 20210149296Abstract: Metrology methods and targets are provided for reducing or eliminating a difference between a device pattern position and a target pattern position while maintaining target printability, process compatibility and optical contrast—in both imaging and scatterometry metrology. Pattern placement discrepancies may be reduced by using sub-resolved assist features in the mask design which have a same periodicity (fine pitch) as the periodic structure and/or by calibrating the measurement results using PPE (pattern placement error) correction factors derived by applying learning procedures to specific calibration terms, in measurements and/or simulations. Metrology targets are disclosed with multiple periodic structures at the same layer (in addition to regular target structures), e.g., in one or two layers, which are used to calibrate and remove PPE, especially when related to asymmetric effects such as scanner aberrations, off-axis illumination and other error sources.Type: ApplicationFiled: January 28, 2021Publication date: May 20, 2021Inventors: Yoel Feler, Vladimir Levinski, Roel Gronheid, Sharon Aharon, Evgeni Gurevich, Anna Golotsvan, Mark Ghinovker
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Patent number: 10901325Abstract: Methods are provided for designing metrology targets and estimating the uncertainty error of metrology metric values with respect to stochastic noise such as line properties (e.g., line edge roughness, LER). Minimal required dimensions of target elements may be derived from analysis of the line properties and uncertainty error of metrology measurements, by either CDSEM (critical dimension scanning electron microscopy) or optical systems, with corresponding targets. The importance of this analysis is emphasized in view of the finding that stochastic noise may have increased importance with when using more localized models such as CPE (correctables per exposure). The uncertainty error estimation may be used for target design, enhancement of overlay estimation and evaluation of measurement reliability in multiple contexts.Type: GrantFiled: February 27, 2018Date of Patent: January 26, 2021Assignee: KLA-Tencor CorporationInventors: Evgeni Gurevich, Michael E. Adel, Roel Gronheid, Yoel Feler, Vladimir Levinski, Dana Klein, Sharon Aharon
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Publication number: 20210020480Abstract: A parameter-stable misregistration measurement amelioration system and method including providing a wafer, including a plurality of multi-layered semiconductor devices formed thereon, selected from a batch wafers intended to be identical, using a misregistration metrology tool to measure misregistration at multiple sites between at least a first layer and a second layer of the wafer, using a plurality of sets of measurement parameters, thereby generating measured misregistration data for each of the sets of measurement parameters, identifying and removing a parameter-dependent portion and a mean error portion from the measured misregistration data for the wafer for each of the sets of measurement parameters, thereby generating ameliorated parameter-stable ameliorated misregistration data for the wafer.Type: ApplicationFiled: August 23, 2019Publication date: January 21, 2021Inventors: Vladimir LEVINSKI, Yuri PASKOVER, Sharon AHARON, Amnon MANASSEN
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Patent number: 10579768Abstract: Metrology targets and target design methods are provided, in which target elements are defined by replacing elements from a periodic pattern having a pitch p, by assist elements having at least one geometric difference from the replaced elements, to form a composite periodic structure that maintains the pitch p as a single pitch. Constructing targets within the bounds of compatibility with advanced multiple patterning techniques improves the fidelity of the targets and fill factor modulation enables adjustment of the targets to produce sufficient metrology sensitivity for extracting the overlay while achieving process compatibility of the targets.Type: GrantFiled: November 4, 2016Date of Patent: March 3, 2020Assignee: KLA-Tencor CorporationInventors: Vladimir Levinski, Eitan Hajaj, Tal Itzkovich, Sharon Aharon, Michael E. Adel, Yuri Paskover, Daria Negri, Yuval Lubashevsky, Amnon Manassen, Myungjun Lee, Mark D. Smith
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Publication number: 20190250504Abstract: Metrology methods and targets are provided for reducing or eliminating a difference between a device pattern position and a target pattern position while maintaining target printability, process compatibility and optical contrast—in both imaging and scatterometry metrology. Pattern placement discrepancies may be reduced by using sub-resolved assist features in the mask design which have a same periodicity (fine pitch) as the periodic structure and/or by calibrating the measurement results using PPE (pattern placement error) correction factors derived by applying learning procedures to specific calibration terms, in measurements and/or simulations. Metrology targets are disclosed with multiple periodic structures at the same layer (in addition to regular target structures), e.g., in one or two layers, which are used to calibrate and remove PPE, especially when related to asymmetric effects such as scanner aberrations, off-axis illumination and other error sources.Type: ApplicationFiled: April 16, 2018Publication date: August 15, 2019Inventors: Yoel Feler, Vladimir Levinski, Roel Gronheid, Sharon Aharon, Evgeni Gurevich, Anna Golotsvan, Mark Ghinovker
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Publication number: 20190049858Abstract: Methods are provided for designing metrology targets and estimating the uncertainty error of metrology metric values with respect to stochastic noise such as line properties (e.g., line edge roughness, LER). Minimal required dimensions of target elements may be derived from analysis of the line properties and uncertainty error of metrology measurements, by either CDSEM (critical dimension scanning electron microscopy) or optical systems, with corresponding targets. The importance of this analysis is emphasized in view of the finding that stochastic noise may have increased importance with when using more localized models such as CPE (correctables per exposure). The uncertainty error estimation may be used for target design, enhancement of overlay estimation and evaluation of measurement reliability in multiple contexts.Type: ApplicationFiled: February 27, 2018Publication date: February 14, 2019Inventors: Evgeni GUREVICH, Michael E. ADEL, Roel GRONHEID, Yoel FELER, Vladimir LEVINSKI, Dana KLEIN, Sharon AHARON
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Publication number: 20180157784Abstract: Metrology targets and target design methods are provided, in which target elements are defined by replacing elements from a periodic pattern having a pitch p, by assist elements having at least one geometric difference from the replaced elements, to form a composite periodic structure that maintains the pitch p as a single pitch. Constructing targets within the bounds of compatibility with advanced multiple patterning techniques improves the fidelity of the targets and fill factor modulation enables adjustment of the targets to produce sufficient metrology sensitivity for extracting the overlay while achieving process compatibility of the targets.Type: ApplicationFiled: November 4, 2016Publication date: June 7, 2018Inventors: Vladimir Levinski, Eitan Hajaj, Tal Itzkovich, Sharon Aharon, Michael E. Adel, Yuri Paskover, Daria Negri, Yuval Lubashevsky, Amnon Manassen, Myungjun Lee, Mark D. Smith