公开(公告)号：US11874606B2

公开(公告)日：2024-01-16

申请号：US18003691

申请日：2021-07-06

Applicant: NOVA LTD.

Inventor： Barak Bringoltz ,  Ofer Shlagman ,  Ran Yacoby ,  Noam Tal

IPC: G03F7/20 ,  G03F7/00

CPC classification number: G03F7/70508 ,  G03F7/70625

Abstract: A system and method are presented for controlling measurements of various sample's parameters. The system comprises a control unit configured as a computer system comprising data input and output utilities, memory, and a data processor, and being configured to communicate with a measured data provider to receive measured data indicative of measurements on the sample. The data processor is configured to perform model-based processing of the measured data utilizing at least one predetermined model, and determine, for each of one or more measurements of one or more parameters of interest of the sample, an estimated upper bound on an error value for the measurement individually, and generate output data indicative thereof.

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

IPC: G06N5/04 ,  G01B11/06 ,  G03F7/00 ,  G06N20/00 ,  H01L21/66 ,  H01L21/68

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.

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

IPC: G05B13/02 ,  G03F7/00 ,  G03F7/20

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.

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

IPC: G06N5/04 ,  H01L21/66 ,  G06N20/00 ,  G01B11/06 ,  G03F7/00 ,  H01L21/68

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.