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.

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.

Abstract: Methods, metrology modules and target designs are provided, which improve the accuracy of metrology measurements. Methods provide flexible handling of multiple measurement recipes and setups and enable relating them to landscape features that indicate their relation to resonance regions and to flat regions. Clustering of recipes, self-consistency tests, common processing of aggregated measurements, noise reduction, cluster analysis, detailed analysis of the landscape and targets with skewed cells are employed separately or in combination to provide cumulative improvements of measurement accuracy.

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.

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.

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.

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.

Abstract: A system and methods for OCD metrology are provided including receiving reference parameters, receiving multiple sets of measured scatterometric data, and receiving an optical model designed to generate one or more sets of model scatterometric data according to a set of pattern parameters, and training a machine learning model by applying, during the training, target features including the reference parameters, and by applying input features including the sets of measured scatterometric data and the sets of model scatterometric data, such that the trained machine learning model estimates new wafer pattern parameters from subsequently sets of measured scatterometric data.

Abstract: A system and methods for OCD metrology are provided including receiving training data for training an OCD machine learning (ML) model, including multiple pairs of corresponding sets of scatterometric data and reference parameters. For each of the pairs, one or more corresponding outlier metrics are by calculated and corresponding outlier thresholds are applied whether a given pair is an outlier pair. The OCD MIL model is then trained with the training data less the outlier pairs.

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.

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.

Abstract: Methods, metrology modules and target designs are provided, which improve the accuracy of metrology measurements. Methods provide flexible handling of multiple measurement recipes and setups and enable relating them to landscape features that indicate their relation to resonance regions and to flat regions. Clustering of recipes, self-consistency tests, common processing of aggregated measurements, noise reduction, cluster analysis, detailed analysis of the landscape and targets with skewed cells are employed separately or in combination to provide cumulative improvements of measurement accuracy.

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.

Abstract: Methods are provided for deriving a partially continuous dependency of metrology metric(s) on recipe parameter(s), analyzing the derived dependency, determining a metrology recipe according to the analysis, and conducting metrology measurement(s) according to the determined recipe. The dependency may be analyzed in form of a landscape such as a sensitivity landscape in which regions of low sensitivity and/or points or contours of low or zero inaccuracy are detected, analytically, numerically or experimentally, and used to configure parameters of measurement, hardware and targets to achieve high measurement accuracy. Process variation is analyzed in terms of its effects on the sensitivity landscape, and these effects are used to characterize the process variation further, to optimize the measurements and make the metrology both more robust to inaccuracy sources and more flexible with respect to different targets on the wafer and available measurement conditions.

Abstract: Metrology methods, systems and targets are provided, which implement a side by side paradigm. Adjacent cells with periodic structures are used to extract the overlay error, e.g., by introducing controllable phase shifts or image shifts which enable algorithmic computation of the overlay. The periodic structures are designed to exhibit a rotational symmetry to support the computation and reduce errors.

Abstract: Angle-resolved reflectometers and reflectometry methods are provided, which comprise a coherent light source, an optical system arranged to scan a test pattern using a spot of coherent light from the light source to yield realizations of the light distribution in the collected pupil, wherein the spot covers a part of the test pattern and the scanning is carried out optically or mechanically according to a scanning pattern, and a processing unit arranged to generate a composite image of the collected pupil distribution by combining the pupil images. Metrology systems and methods are provided, which reduce diffraction errors by estimating, quantitatively, a functional dependency of measurement parameters on aperture sizes and deriving, from identified diffraction components of the functional dependency which relate to the aperture sizes, correction terms for the measurement parameters with respect to the measurement conditions.

Abstract: Metrology methods and targets are provided, for estimating inter-cell process variation by deriving, from overlay measurements of at least three target cells having different designed misalignments, a dependency of a measured inaccuracy on the designed misalignments (each designed misalignment is between at least two overlapping periodic structures in the respective target cell). Inaccuracies which are related to the designed misalignments are reduced, process variation sources are detected and targets and measurement algorithms are optimized according to the derived dependency.

Abstract: Metrology systems and methods are provided herein, which comprise an optical element that is positioned between an objective lens of the system and a target. The optical element is arranged to enhance evanescent modes of radiation reflected by the target. Various configurations are disclosed: the optical element may comprise a solid immersion lens, a combination of Moiré-elements and solid immersion optics, dielectric-metal-dielectric stacks of different designs, and resonating elements to amplify the evanescent modes of illuminating radiation. The metrology systems and methods are configurable to various metrology types, including imaging and scatterometry methods.

Abstract: Angle-resolved reflectometers and reflectometry methods are provided, which comprise a coherent light source, an optical system arranged to scan a test pattern using a spot of coherent light from the light source to yield realizations of the light distribution in the collected pupil, wherein the spot covers a part of the test pattern and the scanning is carried out optically or mechanically according to a scanning pattern, and a processing unit arranged to generate a composite image of the collected pupil distribution by combining the pupil images. Metrology systems and methods are provided, which reduce diffraction errors by estimating, quantitatively, a functional dependency of measurement parameters on aperture sizes and deriving, from identified diffraction components of the functional dependency which relate to the aperture sizes, correction terms for the measurement parameters with respect to the measurement conditions.

Abstract: A method for target measurement is provided which comprises designing a reflection-symmetric target and measuring overlays of the target along at least one reflection symmetry direction of the target. Also, a tool calibration method comprising calibrating a scatterometry measurement tool with respect to a reflection symmetry of a reflection symmetric target. Further provided are methods of measuring scatterometry overlay using first order and zeroth order scatterometry measurements of a reflection-symmetric scatterometry targets. Also, a scatterometry target comprising a reflection-symmetric target having two cells in each of at least two measurement directions, wherein respective cells have different offsets along one measurement direction and similar offsets along other measurement directions. Further, a scatterometry measurement system comprising a reflection symmetric illumination and calibrated to measure reflection symmetric targets.