Classifications

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  • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
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• • G—PHYSICS
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• • G—PHYSICS
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  • G03F7/00—Photomechanical, e.g. photolithographic, production of textured or patterned surfaces, e.g. printing surfaces; Materials therefor, e.g. comprising photoresists; Apparatus specially adapted therefor
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  • G03F7/70605—Workpiece metrology
  • G03F7/70616—Monitoring the printed patterns
  • G03F7/70633—Overlay, i.e. relative alignment between patterns printed by separate exposures in different layers, or in the same layer in multiple exposures or stitching
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  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N17/00—Diagnosis, testing or measuring for television systems or their details
  • H04N17/002—Diagnosis, testing or measuring for television systems or their details for television cameras
• • H—ELECTRICITY
  • H04—ELECTRIC COMMUNICATION TECHNIQUE
  • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
  • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
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  • G06V10/42—Global feature extraction by analysis of the whole pattern, e.g. using frequency domain transformations or autocorrelation
  • G06V10/431—Frequency domain transformation; Autocorrelation

Definitions

• the described embodiments relate to metrology systems and methods, and more particularly to methods and systems for improved model-based measurements.
• semiconductor devices are formed by these processing steps. For example, lithography among others is one semiconductor fabrication process that, involves generating a pattern on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing, etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated on a single semiconductor wafer and then
• Metrology processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers to promote higher yield.
• Optical metrology techniques offer the potential for high throughput without the risk of sample destruction.
• a number of optical metrology based techniques including scatterometry and reflectometry implementations and associated analysis algorithms are commonly used to characterize critical dimensions, film thicknesses, composition, overlay and other parameters of nanoscale structures.
• Non-imaging, model-based optical metrology techniques generally acquire measurement signals sequentially and usually from metrology targets that are sparsely located on. a field area of a semiconductor wafer.
• model -based optical metrology techniques offer high precision
• the number of locations that can be measured for a given wafer throughput requirement is limited.
• imaging based measurement systems collect large numbers of signals in parallel.
• the wafer area that can be characterized by imaging-based measurements for a given 'wafer throughput requirement is much larger compared to model-based optical metrology techniques.
• imaging-based measurements lack sufficient resolution to directly measure complex three dimensional structures that are commonly manufactured today.
• Image based measurements typically involve the recognition of specific target features (e.g., line
• the specialized target structures are specific to the image processing algorithm.
• the line segments associated with an overlay target e.g. , box-in- box target, frame-in- frame target, advanced imaging metrology (ATM) target
• ATM advanced imaging metrology
• process control is enabled by performing metrology on specific dedicated structures.
• These dedicated structures may be located in the scribe lines between dies, or within the die itself.
• the high information content present in measured images is transformed into estimated values of structural parameters of interest.
• An image-based signal response metrology (SRM) model is trained based on
• measured, image-based training data e.g. , images collected from a Design of Experiments (DOE) wafer
• the trained, image-based measurement model is then used to calculate values of one or more parameters of interest, directly from, measured image data collected from other wafers.
• the trained, image-based SRM models described herein receive image data directly as input and provide estimates of values of one or more parameters of interest as output. By streamlining the measurement process, the predictive results are improved along with a reduction in computation and user time.
• measurement model is trained based on image data collected from a particular metrology system and used to perform measurements based on images collected from the same metro1ogy system .
• measured images are transformed, into synthetic non-imaging based measurement signals associated with a model-based measurement technique at one or more locations a field.
• the model-based measurement technique is employed to estimate values of structural parameters of interest based on the synthetic signals.
• a measurement signal synthesis model is trained based on measured, image-based training data (e.g., images collected from a Design of Experiments (DOE) wafer) and corresponding non-imaging measurement data.
• synthetic signals are generated for multiple structures in different locations in each imaged, field.
• FIG. 1 illustrates a system 100 for performing measurements of parameters of interest in accordance with the exemplary methods presented herein.
• FIG. 2 is a flowchart illustrative of a method 200 of training an image based SRM model as described herein.
• FIG, 3 is a flowchart illustrative of a method 210 of performing measurements of a structure using the trained SRM model described with reference to method 400.
• FIG. 4 depicts a design of experiments wafer 1600 having a grid of measurement sites including structures that exhibit known variations of one or more parameters of interest .
• FIG. 5 depicts illustrative images 162-164 of different measurement sites of wafer 160.
• FIG. 6 illustrates a grid of pixels 165 associated with image 162.
• FIG. 7 depicts different pixel locations selected for model training and measurement in accordance with method the methods described herein .
• FIG. 8 depicts a vector 176 of measured intensity values sampled at the pixel locations illustrated in FIG. 7.
• FIG . 9 is a flowchart illustrative of a method 220 of training a measurement signal synthesis model as
• FIG. 10 is a flowchart illustrative of a method 230 of performing measurements of a structure using the
• FIG. 1 illustrates a system. 100 for measuring characteristics of a specimen in accordance with the exemplary methods presented herein.
• the system 100 may be used to perform imaging and non ⁇ imaging measurements of one or more structures formed on a specimen 107.
• the system 100 may be used to perform imaging and non ⁇ imaging measurements of one or more structures formed on a specimen 107.
• the system 100 may be used to perform imaging and non ⁇ imaging measurements of one or more structures formed on a specimen 107.
• the system 100 may be used to perform imaging and non ⁇ imaging measurements of one or more structures formed on a specimen 107.
• the system 100 may be used to perform imaging and non ⁇ imaging measurements of one or more structures formed on a specimen 107.
• the system 100 may be used to perform imaging and non ⁇ imaging measurements of one or more structures formed on a specimen 107.
• the system 100 may be used to perform imaging and non ⁇ imaging measurements of one or more structures formed on a specimen 107.
• the system 100 may be used to perform imaging and non ⁇
• system 100 configured as a beam profile reflectometer (BPR) , a field imaging system, and a spectroscopic reflectometer (SR) .
• system 100 may be configured as a BPR, a spectroscopic ellipsometer (SE) , and a field imaging system.
• System 100 includes a high numerical aperture (NA) objective lens (e.g., NA > 0.9) and at least one collection beam splitter 110 to generate an optical path to the pupil detector 117 and another optical path to the field detector 113 or 114.
• NA numerical aperture
• the field detector and pupil detector acquire field signals 121 or 122 and pupil signals 123,
• Field images or pupil images are processed to estimate one or more structural or process parameter values.
• system 100 includes an illumination source 101 that generates an amount of illumination light 119.
• illumination source 101 is a broadband illumination source such as a xenon lamp, a laser driven light source, a multiple
• illumination source 101 includes a narrowband light source such as a single 'wavelength laser, a tunable narrowband laser, etc. In some embodiments, illumination source 101 includes a combination of broadband and narrowband illumination sources. In some embodiments, optical filters are included to select one or more
• illumination wavelength ( s ) and corresponding wavelength range ( s ) are provided.
• illumination light 119 passes through illumination optics 102.
• Illumination optics 102 focus and collimate the illumination light.
• Illumination optics 102 include lens components, mirror components, or a combination of both. Illumination light passes through one or more selectable illumination
• the selectable illumination apertures 104 include a set of illumination field stops and a set of illumination pupil stops.
• the illumination field stops are configured to select the illumination spot size projected onto specimen 107.
• the illumination pupil stops are configured to select the illumination pupil projected onto specimen 107.
• the illumination field stops and pupil stops operate in con unction with other illumination optics components (e.g., illumination optics 102 and objective 106) to achieve an illumination NA tuned for optimal light throughput, illumination field of view, and pupil on the surface of specimen 107.
• the aperture (s) of the selectable illumination apertures 104 may be formed by any suitable device including, but not limited to a mechanical pin-hole, a spatial light modulator (SLM) , an apodizer, and any other beam forming and controlling component or sub-system.
• SLM spatial light modulator
• apodizer any other beam forming and controlling component or sub-system.
• Illumination beam splitter 105 directs a portion of the collimated illumination light to objective 106 and directs another portion of the collimated illumination light to intensity monitor 108.
• intensity monitor 108 is communicatively coupled to
• computing system 130 and provides an indication of the overall illumination intensity, the illumination intensity profile, or both, to computing system 130.
• Objective 106 directs illumination light to the surface of specimen 107 over a broad range of angles of incidence.
• Illumination beam splitter 105 and collection beam, splitter 110 may include any suitable beam, splitting element including, but not limited to, a cubic beam splitter, a metallic coating plate, a dic roic optical coating plate, or other beam splitting mechani sm .
• the field detection path includes a selectable field collection aperture 111, focusing field optics 112, and at least one field detector.
• the selectable field collection aperture 111 includes a set of field stops to select signals for projection onto field signal detectors 113 or 114. In some examples, higher order field signals are selected for projection onto field signal detectors 113 or 114.
• the aperture (s) of the selectable field collection aperture 111 may be formed by any suitable device including, but not limited to a
• SLM spatial light modulator
• apodizer any other beam forming and controlling component or sub-system.
• system 100 includes a field imaging detector 114 and a spectroscopic field detector 113.
• a flip-in mirror mechanism 120 is selectively located in the field detection path based on a command signal (not shown) received from computing system 130.
• flip-in mirror mechanism 120 is located in the field detection path and the collected light is directed to field imaging detector 114.
• flip-in mirror mechanism 120 is located outside the field detection path and the collected light is directed toward spectroscopic fie1d detector 113.
• system. 100 is configured to perform, either image- based or spectroscopic based field measurements.
• field imaging detector 114 images a portion of the wafer surface illuminated by the illumination source onto the detector.
• Field imaging detector 114 may be CCD camera, CMOS camera, array detector, etc.
• the pupil detection path includes a selectable pupil collection aperture 118, a selectable narrow band pass filter 115, and pupil relay optics 116 that direct the collected light to pupil detector 117. In some
• the selectable pupil collection, aperture 118 includes a set of field stops to select signals for
• higher order pupil signals are selected for projection onto pupil signal detector 117.
• the aperture (s) of the selectable pupil collection aperture 118 may be formed by any suitable device including, but not limited to a mechanical pin-hole, a spatial light modulator (SLM) , an apodizer, and any other beam forming and controlling component or sub-system..
• SLM spatial light modulator
• apodizer any other beam forming and controlling component or sub-system.
• pupil detector 117 is an imaging detector. However, in some other embodiments, pupil detector 117 is a spectroscopic detector. In
• the pupil detection, path may include one or more pupil detectors configured to collect, pupil data
• the pupil images detected by pupil imaging detector 117 or the field images detected by field imaging detector 114 may be used to measurement parameters of interest directly based on an image based SRM model, or indirectly, based on a measurement signal
• spectroscopic field detector 113 is a spectrometer.
• the detected spectra may also be used for measurement of parameters of interest.
• Exemplary parameters of interest include any of a critical dimension.
• CD compact disc
• overlay param.et.er an overlay param.et.er, a focus parameter, a dose parameter, a structure asymmetry parameter, a structure roughness parameter, a directed self assembly (DSA) pattern uniformity parameter, a pitch walk parameter, etc .
• system 100 includes a polarizer 103 in the illumination path and an analyzer 109 in the collection path. Depending on whether polarizer 103 is rotating or not, system 100 may be
• SR spectroscopic reflectometry
• SE spectroscopic ellipsometry
• system. 100 may be
• system 100 includes a measurement device (e.g., encoder 125) configured to measure the position of specimen 107 relative to the optical system, in. the direction perpendicular to the surface of specimen 107 (i.e., z-direction depicted in coordinate frame 126) .
• encoder 125 provides an indication of the focus position of specimen 107 relative to the optical system.
• Pupil signals 123 and field signals 121 or 122 can be collected along with an indication of focus position 124 for analysis by computing system 130.
• computing system. 130 communicates command signals to either a wafer positioning system (not shown) or an optical positioning system (not shown) to adjust the focus position of specimen 107 relative to the optical system. In this manner, the focus position of specimen 107 is monitored and adjusted during image
• image data is
• model based CD measurement involves a CD measurement model including a parameterization of a metrology target in terms of the CD parameter of interest.
• the measurement model includes a
• Machine parameters are parameters used to characterize the metrology tool itself.
• Exemplary machine parameters include angle of incidence (AOI), analyzer angle
• AO polarizer angle
• MA numerical aperture
• P spe cimen Specimen parameters
• the floating parameters and the specimen parameters of the measurement model, or a subset of specimen parameters, are treated, as unknown, floating parameters .
• the floating parameters are resolved by a fitting process (e.g., regression, library matching, etc.) that produces the best fit between
• the high information content present in measured images is transformed into estimated values of structural parameters of interest.
• An image-based signal response metrology (SRM) model is trained based on SRM
• measured, image-based training data e.g. , images collected from a Design of Experiments (DOE) wafer
• the trained, image-based measurement model is then used to calculate values of one or more parameters of interest directly from measured image data collected from other wafers.
• the trained, image-based SRM models described herein receive image data directly as input and provide estimates of values of one or more parameters of interest as output. By streamlining the measurement process, the predictive results are improved along with a reduction in computation and user time.
• measurement model is trained based on image data collected from, a particular metrology system and used to perform measurements based on images collected from the same metro1ogy system .
• the image-based SRM model can be created in less than an hour.
• measurement time is reduced compared to existing image based metrology methods. Additional modeling details are described in U.S. Patent Publication No. 2014/0297211 and U.S. Patent Publication No. 2014/0316730, the .subject matter of each are incorporated herein by reference in their entirety.
• each pixel is considered as an individual signal containing information about (or sensitive to) structural parameters, process parameters, dispersion parameters, etc.
• FIG . 2 illustrates a method 200 suitable for implementation by a measurement system such as measurement system. 100 illustrated in FIG. 1 of the present invention.
• data processing blocks of method 200 may be carried out via a pre-programmed algorithm executed by one or more processors of computing system 130, or any other general purpose computing system.. It is recognized herein that the particular structural aspects of measurement system 100 do not represent
• DOE Design Of Experiments
• Each. DOE measurement site includes an instance of at least one structure characterized by at least one parameter of interest.
• the structure can be a dedicated metrology target, device structure, grating structure, etc.
• the parameters of interest include one or more process parameters, structural parameters, dispersion parameters, or layout parameters.
• Each of the measurement sites includes the same nominal structures at the same nominal locations within, each of the measurement sites.
• a measurement site encompasses a field area of a semiconductor wafer that is repeatedly constructed across the wafer surface.
• a measurement site encompasses a die area that is repeatedly constructed across the wafer surface.
• each measurement site nominally includes the same structures, in reality, and for purposes of model training, each measurement site includes variations of various parameters (e.g., CD, sidewall angle, height, overlay, etc.) .
• DOE Experiments
• the DOE pattern is a focus exposure matrix (FEM) pattern.
• FEM focus exposure matrix
• a DOE 'wafer exhibiting an FEM pattern includes a grid pattern of measurement sites .
• the focus is varied while the exposure is held constant.
• the orthogonal grid direction e.g., the y- direction
• the exposure is varied while the focus is held constant.
• image data collected from the DOE wafer includes data associated with variations in focus and exposure.
• FIG. 4 depicts a DOE wafer 160 having a grid of measurement sites (e.g., measurement site 161) including structures that exhibit variations in the parameter (s) of interest (e.g. , focus and exposure) .
• the focus varies as a function of location on the DOE wafer 160 in the x- direction.
• the exposure varies as a function of location on the DOE wafer 160 in the y-direction.
• the images include device areas. Each pixel of a particular image of a measurement site represents the intensity of the collected light under specific illumination and collection conditions,
• FIG. 5 depicts images 162- 164 of different measurement sites of wafer 160. Each image represents an aerial view of the device structures within a measurement site. The measurement site is
• the images include specific targets designed to facilitate image-based
• a specia11y designed target may be employed to improve device
• the image data is associated with a DOE wafer processed with variations in focus and exposure (i.e., dose) .
• image data associated with any variation, of process is associated with any variation, of process
• the images of the DOE wafer should exhibit ranges of the parameter (s) of interest.
• field imaging detector 114 depicted, in FIG. 1 detects light imaged from the surface of wafer 107 at each DOE measurement site. In.
• pupil imaging detector 117 detects light imaged from the pupil of objective 106 at each DOE
• an. image of each of the plurality of DOE measurement sites is generated.
• field imaging detector 114 generates an image of each of the DOE measurement sites and communicates signals 122 indicative of each generated image to computing system 130.
• pupil imaging detector 117 generates a pupil image of each of the DOE measurement sites and communicates signals indicative of each generated pupil image to computing system 130.
• each image of each measurement site includes a single measurement signal value associated with each image pixel.
• the single measurement value is a reflectance at the location of each pixel measured by an imaging reflectometer at a particular set of measurement system settings (e.g., wavelength, polarization, angle of incidence, azimuth angle, etc . ) .
• each of the images of each measurement site includes a single measurement signal value associated with each pixel.
• multiple measurement signal values are measured for each pixel.
• each of the images of each measurement site is measured either by the same measurement system, at different, settings
• a different measurement technique e.g., wavelength, polarization, angle of incidence, azimuth angle, etc.
• a different measurement technique e.g., a different measurement technique, or a combination thereof.
• image data can be collected from any imaging based system such as an optical imaging system, a microscope, a scanning electron microscope, a tunneling electron microscope, or other image forming systems .
• a reference measurement value of the at least one parameter of interest is estimated at each of the plurality of DOE measurement sites by a trusted, reference metrology system. Reference measurements are performed by a reference measurement system, or combination of reference measurement systems based on any suitable metrology technique, or combination of metrology
• any of a scanning electron microscope, an optical based measurement system, an x-ray based measurement system., a tunneling electron microscopy system, and an atomic force microscopy system may be employed to perform reference measurements of DOE measurement sites.
• reference measurements 151 of parameters of interest at each DOE measurement site are communicated from a reference
• spectroscopic field detector 113 generates measurement signals 121 indicative of light collected from one or more structures characterized by each parameter of interest within each measurement site.
• the spectroscopic field detector 113 generates measurement signals 121 indicative of light collected from one or more structures characterized by each parameter of interest within each measurement site.
• measurement signals are spectroscopic scatterometry
• Computing system 130 performs a model based measurement (e.g., optical critical dimension measurement) to estimate the value of each parameter of interest at each measurement site based on the detected measurement signals 121.
• a model based measurement e.g., optical critical dimension measurement
• each of the images is aligned with a. common reference location of each
• FIG. 6 illustrates a grid of pixels 165
• image alignment is optional.
• each of the images is filtered by one or more image filters.
• Image filters may be employed for noise reduction, contrast enhancement, etc.
• image filters may be employed to reduce edge effects by detecting edges and removing or masking the edges and proximate regions. In this manner, subsequent image samples are taken from relatively homogenous device regions.
• the image filters employed may be selected by a user or by an automatic procedure. The number of different image filters and the parameters associated with each selected filter are chosen to improve the final measurement result without undue computational burden. Although, the use of image based filters may be advantageous, in general, it is not
• image filtering is optional.
• Each image of each measurement site may include millions of pixels, and only a small number of those pixels ave any correlation with the parameters of interest.
• FIG. 7 depicts two different groups of pixels at. different locations selected for model training and measurement.
• FIG. 8 depicts a vector 176 of measured intensity (e.g., reflectance) values sampled at the pixel locations illustrated in FIG. 7. This sampled image data is used for model training and measurement.
• i I ( H , JD is the intensity value associated with pixel group 170 of image 162, 2 I ( H , J I ; is the intensity value associated with pixel group 172 of image 163, and n I ⁇ H , JD is the intensity value associated with pixel group 174 of image 164.
• vector 176 includes intensity measurement signals from pixels groups at the same location of each imaged measurement site.
• pixels or groups of pixels are selected for their proximity to structures characterized by the parameters of interest .
• selected pixels are associated with an area around a structure of interest that is five to ten times as large as the
• pixel locations are selected randomly. In some other examples, the pixel locations are selected based on their measurement sensitivity. In one example, the variance of measurement signal values associated with each pixel location is calculated from the ensemble of images. The variance associated with each pixel location is a metric that characterizes the measurement sensitivity at each
• Pixel locations with relatively low variance offer lower
• a predetermined threshold value for variance is selected, and pixel locations with a variance that exceeds the predetermined threshold value are selected for model training and measurement. In this manner, only the most sensitive locations are sampled. In some examples, all of the pixels associated with each of the first plurality of images are selected for model training and measurement. In this sense, pixel selection is optional.
• a feature extraction model is determined based on the selected image data.
• the feature extraction model reduces a dimension of the image data.
• a feature extraction model maps the original signals to a new reduced set of signals.
• the transformation is determined based on the variations in the parameter (s) of interest in the selected images.
• Each pixel of each image is treated as an original signal that changes within the process range for different images.
• the feature extraction model may be applied to all of the image pixels, or a subset of image pixels.
• the pixe1 s subj ect to ana1ysi s by the feature extraction mode1 are chosen randomly.
• the pixels subject to analysis by the feature extraction model are chosen due to their relatively high sensitivity to changes in the parameter ( s ) of interest. For example, pixels that are not sensitive to changes in the parameter (s) of
• extraction model may a principal component analysis (PCA) model, a kernel PCA model, a non- linear PCA model, an independent component analysis (ICA) model or other
• FFT transform
• the locations on the wafer are programmed to have specific geometric and process parameter values (e.g., focus, dose, overlay, CD, sidewall angle, height etc.) .
• specific geometric and process parameter values e.g., focus, dose, overlay, CD, sidewall angle, height etc.
• components representation allows mapping one or more signal representations as a function of process parameters or geometric parameters over the entire wafer.
• the nature of the pattern captures the essential properties of the device, whether it includes isolated or dense features.
• an image based signal response metrology (SRM) model is trained based on the generated images, or features extracted from the generated images and the reference values of the at least one parameter of interest.
• the image-based SRM model is structured to receive image data generated by a metrology system at one or more measurement sites, and directly determine the parameter (s) of interest associated with each measurement target.
• the image-based measurement model is implemented as a neural network model .
• the number of nodes of the neural network is selected based on the features extracted from the image data.
• the image-based SRM model may be implemented as a linear model, a polynomial model, a response surface model, a support vector machines model, or other types of models.
• the image-based measurement model may be implemented as a combination of models.
• the selected model is trained based on the reduced set of signals determined from the feature extraction model and the measured reference values of the parameter ( s ) of interest. The model is trained such that its output fits the measured reference values of the parameter (s) of interest for all the images in the
• computing system 130 trains an image-based SRM model such that its output fits the reference values received from reference measurement source 150 or reference values calculated based on measurement signals 121 for each DOE image of each DOE measurement site received from field imaging detector 114 or pupil imaging detector 117.
• FIG. 3 illustrates a method 210 suitable for implementation by a metrology system such as metrology system 100 illustrated in FIG. 1 of the present invention.
• data processing blocks of method 210 may be carried out via a pre-programmed algorithm executed by one or more processors of computing system 130, or any other general purpose computing system. It is recognized herein that the particular structural aspects of metrology system 100 do not represent
• a measurement site is illuminated in accordance with the same image based metrology technique, or combination of image based metrology techniques employed to generate the images used to train the image based SRM model .
• the measurement site is a different measurement site than any of the DOE measurement sites .
• measurement site includes an instance of at least one structure characterized by the parameter (s) of interest.
• field imaging detector 114 depicted in FIG. 1 detects light imaged from the surface of wafer 107 at the measurement site.
• pupil imaging detector 117 detects light imaged from the pupil of objective 106 at the measurement site.
• an image of the measurement site is generated.
• field imaging detector 114 generates an image of the measurement site and communicates signals 122 indicative of the generated image to computing system 130.
• pupil imaging detector 117 generates a pupil image of the measurement site and
• the image data is subjected to the same alignment, filtering, sampling, and feature extraction steps described with reference to method 200. Although, the use of any, or all, of these steps may be advantageous, in general, it is not necessary. In this sense, these steps are optional.
• the value of at least one parameter of interest characterizing the instance of the structure at the measurement site is determined based on the trained image based SRM model and the image of the measurement site.
• the image of measurement site is processed by the image based SRM model to determine the value (s) of the parameter (s) of interest.
• the determined value (s) of the parameter (s) of interest are stored in a memory.
• the parameter values may be stored on-board the measurement system 100, for example, in memory 132, or may be communicated (e.g., via output signal 140) to an external memory device.
• measured images are transformed into synthetic non-imaging based measurement signals associated with a model-based measurement technique at one or more locations a field.
• the model-based measurement technique is employed to estimate values of structural parameters of interest based on the synthetic signals.
• a measurement signal synthesis model is trained based on measured, image-based training data (e.g., images collected from a Design of Experiments (DOE) wafer) and corresponding non-imaging measurement data.
• synthetic signals are generated for multiple structures in different locations in each imaged field.
• performing model based measurements based on the synthetic signals is significantly faster than acquiring actual measurement data at each different location.
• FIG, 9 illustrates a method 220 suitable for implementation by a measurement system such as measurement system 100 illustrated in FIG. 1 of the present invention.
• data processing blocks of method 200 may be carried out via a pre-programmed algorithm, executed by one or more processors of computing system 130, or any other general purpose computing system. It is recognized herein that the particular structural aspects of measurement system. 100 do not represent,
• a plurality of DOE measurement sites are illuminated by an illumination source.
• DOE wafers Each DOE
• measurement site includes an instance of at least one structure characterized by at least one parameter of interest.
• the structure can be a dedicated metrology target, device structure, grating structure, etc.
• the parameters of interest include one or more process
• Each of the measurement sites includes the same nominal structures at the same nominal locations within each of the measurement sites.
• a measurement site encompasses a field area of a semiconductor wafer that is repeatedly constructed across the wafer surface.
• a measurement site encompasses a die area that is repeatedly constructed across the wafer surface.
• each measurement site nominally includes the same structures, in reality, and for purposes of model training, each measurement site includes variations of various parameters (e.g., CD, sidewall angle, height, overlay, etc. ) .
• variations of the parameter (s) of interest are organized in a Design of Experiments (DOE) pattern on the surface of a semiconductor wafer (e.g., DOE wafer) as described with reference to method 200.
• DOE Design of Experiments
• field imaging detector 114 depicted in FIG. 1 detects light imaged from the surface of wafer 107 at each DOE measurement site.
• pupil imaging detector 117 detects light imaged from, the pupil of objective 106 at each DOE
• an image of each of the plurality of DOE measurement sites is generated.
• field imaging detector 114 generates an image of each of the DOE measurement sites and communicates signals 122 indicative of each generated image to computing system 130.
• pupil imaging detector 117 generates a pupil image of each of the DOE measurement sites and communicates signals indicative of each generated pupil image to computing system 130.
• each image of each measurement site includes a single measurement signal value associated 'with each image pixel.
• the single measurement value is a reflectance at the location of each pixel measured by an imaging reflectometer at a particular set of measurement system settings (e.g. , wavelength, polarization, angle of incidence, azimuth angle, etc. ) .
• multiple images of each measurement site are generated, as described with reference to method 200.
• the image data is subjected to the same alignment, filtering, sampling, and feature extraction steps described with reference to method 200. Although, the use of any, or all, of these steps may be advantageous , in general, it is not necessary. In this sense, these steps are optiona1.
• spectroscopic field detector 113 detects light collected from one or more structures characterized by each parameter of interest within each measurement site.
• one or more measurement signals indicative of the detected light at each of the plurality of DOE measurement sites is generated.
• spectroscopic fie1d detector 113 generates measurement signals 121 indicative of light collected from one or more structures characterized by each parameter of interest within each measurement site.
• the measurement signals are spectroscopic scatterometry signals .
• a measurement signal synthesis model is trained.
• the measurement signal synthesis model relates the images of each of the plurality of DOE measurement sites to the one or more sets of measurement signals associated with each non-imaging measurement of each parameterized structure at each of the plurality of DOE measurement sites.
• the measurement signal synthesis model is structured to receive image data generated by a
• the measurement signal synthesis model is implemented as a neural network model.
• the number of nodes of the neural network is selected based on the features extracted from the image data.
• the measurement signal synthesis model may be implemented as a linear model, a polynomial model, a response surface model, a support vector machines model, or other types of models.
• the measurement signal synthesis model may be implemented as a combination of models.
• the selected model is trained based on the reduced set of signals determined from the feature extraction model and the signals measured based on one or more non-imaging metrology techniques.
• the model is trained such that its output fits the measured signals for all the images in the parameter variation space defined by the DOE images.
• computing system 130 trains an measurement signal synthesis model such that its output fits the measured signals 121 associated each parameterized structure measured within each DOE measurement site for each image of each DOE measurement site received from field imaging detector 114 or pupil imaging detector 117.
• t.he t.rained measurement signal synthesis model is employed to transform measured images into synthetic non-imaging based measurement signals associated with one or more model-based measurement
• the one or more model-based measurement techniques are employed to estimate values of structural parameters of interest based on the synthetic signals.
• FIG . 10 illustrates a method 230 suitable for implementation by a metrology system such as metrology system 100 illustrated in FIG. 1 of the present invention.
• data processing blocks of method 230 may be carried out via a pre-programmed algorithm, executed by one or more processors of computing system 130, or any other general purpose computing system. It is recognized herein that the particular structural aspects of metrology system 100 do not represent
• a measurement site is illuminated in accordance with the same image based metrology technique, or combination of image based metrology techniques employed to generate the images used to train the measurement signal synthesis model .
• the measurement site is a different measurement site than any of the DOE measurement sites.
• the measurement site includes an instance of at least one structure characterized by the parameter (s) of interest.
• field imaging detector 114 depicted in FIG. 1 detects light imaged from the surface of wafer 107 at the measurement site.
• pupil imaging detector 117 detects light imaged from the pupil of objective 106 at the measurement site.
• an image of the measurement site is generated.
• field imaging detector 114 generates an image of the measurement site and communicates signals 122 indicative of the generated image to computing system 130.
• pupil imaging detector 117 generates a pupil image of the measurement site and
• the image data is subjected to the same alignment, filtering, sampling, and feature extraction steps described with reference to method 230. Although, the use of any, or all, of these steps may be advantageous, in general, it is not necessary. In this sense, these steps are optional .
• a set of synthetic measurement signals associated with the measurement site is generated based on the trained measurement signal synthesis model and the image of the measurement site.
• the synthetic measurement signals are associated with different instances of the same structure (s) characterized by each parameter of interest within each measurement site used for training of the measurement signal synthesis model .
• the synthetic measurement signals are associated with a
• a value of at least one parameter of interest characterizing the instance of the at least one structure at the measurement site is determined based on a fitting of the synthetic measurement signals to a model of a measurement of the measurement site in accordance with the non-imaging measurement technique.
• the set of synthetic signals are received by computing system 130.
• Computing system 130 performs a model based
• optical critical dimension measurement e.g., optical critical dimension measurement
• parameter (s) of interest are stored in a memory.
• the parameter values may be stored on-board the measurement system 100, for example, in memory 132, or may be communicated (e.g., via output signal 140) to an
• values of parameters of interest may be determined from images of on-device
• images of on-device structures are used to train an image-based SRM model or a measurement signal synthesis model as described herein.
• the trained models are then used to calculate values of one or more parameters of interest directly from images, or indirectly, via synthetic signals, of the same on-device structures collected from, other wafers.
• the use of specialized targets is avoided.
• metrology targets are used and the target size can be less than 10 microns by 10 microns.
• multiple targets can be measured from, single image and the metrology target can include one structure or more than one different structure .
• Exemplary structures characterized by parameters of interest measured in accordance with the methods and systems described herein include line-space grating
• any target that exhibits sensitivity to a parameter of interest when imaged by the available imaging system may be employed in accordance with the methods and systems described herein.
• it is advantageous to employ specialized targets in general, any target that exhibits sensitivity to a parameter of interest when imaged by the available imaging system may be employed in accordance with the methods and systems described herein.
• it is advantageous to employ specialized targets in accordance with the methods and systems described herein.
• measurement targets that exhibit high sensitivity to a parameter of interest when imaged by the available imaging system to enhance image-based measurement performance.
• measurement targets are located in the scribe lines of a semiconductor area. In other examples, the measurement targets are located within the device area.
• the methods and systems for training the image-based measurement model include an optimization algorithm, to automate any or all of the elements required to arrive at a trained model.
• an optimization algorithm is configured to maximize the performance of the measurement (defined by a cost func ion) by optimizing any o all of the following parameters: the list of image filters, the parameters of the filters, pixel sampling, the type of feature extraction model, the parameters of the selected feature extraction, model, the type of measurement model, the parameters of the selected measurement model .
• the optimization algorithm can include user defined heuristics and can be combination of nested optimizations (e.g., combinatorial and continuous optimization) .
• image data including multiple, different targets is collected for model building
• training data tha includes images of multiple, different targets at one or more measurement sites enables more accurate estimation of values of parameters of interest.
• signals from multiple targets can be processed to reduce sensitivity to process variations and increase sensitivity to the parameters of interest.
• signals from images, or portions of images, of different targets are subtracted from one another.
• signals from, images, or portions of images, of different targets are fit to a model, and the residuals are used to build, train, and use the models as described herein.
• image signals from two different targets are subtracted to eliminate, or significantly reduce, the effect of process noise in each measurement result.
• measurement signals from multiple targets are subtracted to eliminate, o significantly reduce, the effect of under layers in each measurement result.
• the use of measurement data associated with multiple targets increases the sample and process information embedded in the model.
• various mathematical operations can be applied between the signals from different target images, or portions of target images to determine image signals with reduced sensitivity to process variations and increased sensitivity to the parameters of interest.
• measurement data derived from measurements performed by a combination of multiple, different measurement techniques is collected for model building, training, and measurement.
• measurement data associated with multiple, different measurement techniques increases the information content in the combined set of signals and reduces the correlation to process or other parameters variations.
• measurement sites may be measured by multiple, different measurement techniques to enhance the measurement information available for estimation of parameters of interest .
• measurement results at multiple wavelengths are combined for model training and measurement in accordance with the methods and systems described herein .
• any image based measurement technique, or combination of two or more image based measurement techniques may be contemplated within the scope of this patent document as the data processed by any of the feature extraction model, the image-based SRM model, and the measurement signal synthesis model is in vector form.
• each pixel of image data is treated independently.
• spectroscopic ellipsometry at one or more angles of illumination including Mueller matrix ellipsometry, spectroscopic reflectometry, angle resolve ref1ectometry, spectroscopic sca11erometry, sca11erometry overlay, beam profile reflectometry, both angle-resolved and polarization-resolved, beam profile ellipsometry, single or multiple discrete wavelength ellipsometry, single wavelength reflectometry, single wavelength ellipsometry, transmission small angle x-ray scatterom.et.er (TS.AXS), small angle x-ray scattering (SAXS), grazing incidence small angle x-ray scattering (GISAXS), wide angle x-ray scattering (WAXS) , x-ray reflectivity (XRR) , x-ray
• XRD XRD diffraction
• GIXRD grazing incidence x-ray diffraction
• HRXRD high resolution x-ray diffraction
• XPS x-ray photoelectron spectroscopy
• XRF x-ray fluorescence
• GIXRF grazing incide ce x-ray fluorescence
• microscopy tunneling electron microscopy
• atomic force microscopy Any of the aforementioned metrology techniques may be implemented separately as part of a stand-alone measurement system, or combined into an integrated
• measurement data collected by different measurement technologies and analyzed in accordance with the methods described herein may be
• signals measured by multiple metrologies can. be processed to reduce sensitivity to process variations and increase sensitivity to the parameters of interest.
• signals from images, or portions of images, of targets measured by different metrologies are subtracted from one another.
• signals from images, or portions of images, of targets measured by different metrologies are fit to a model, and the residuals are used to build, train, and use the image-based measurement model as described herein.
• image signals from a target measured by two different metrologies are subtracted to eliminate, or significantly reduce, the effect of process noise in each measurement result.
• various mathematical operations can be applied between the signals of target images, or portions of target images, measured by different metrologies to determine image signals with reduced sensiti ity to process variations and increased sensitivity to the parameters of interest.
• image signals from multiple targets each measured by multiple metrology techniques increases the information content in the combined set of signals and reduces the overlay correlation to process or other
• image data and non ⁇ imaging data may be collected from measurement targets such as dedicated nietrology targets, device structures, or proxy structures found within the fields or die areas on the wafer, or within scribe lines.
• the measurement methods described herein are implemented as an element of a SpectraShape® optical critical-dimension metrology system available from KLA-Tencor Corporation, Milpitas, California, USA.
• the measurement methods described herein are implemented off-line, for example, by a computing system implementing AcuShape ⁇ software
• the measurement results described herein can be used to provide active feedback to a process tool (e.g., lithography tool, etch tool,
• values of overlay error determined using the methods described herein can be communicated to a lithography tool to adjust the
• etch parameters e.g., etch time, diffusivity, etc.
• deposition parameters e.g., time, concentration, etc.
• the measurement model may describe one or more target structures, device structures, and measurement sites.
• an image utilized for model training and measurement as described herein is a result of a linear or non-linear transformation from multiple partial images of different locations in the field.
• an image utilized for model training and measurement as described herein is a result of a linear or non-linear transformation from multiple partial images of different locations in different fields .
• an image utilized for model training and measurement as described herein is a result of a linear or non-linear transformation from multiple partial images of different locations in a field and non-imaging measurement signals (e.g., scatterometry signals) used for model training are associated with different measurement locations in the field.
• non-imaging measurement signals e.g., scatterometry signals
• system 100 may include one or more computing systems 130 employed to perform,
• the one or more computing systems 130 may be communicatively coupled to the detectors of system 100.
• the one or more computing systems 130 are configured to receive measurement data associated with measurements of the structure of specimen 107.
• subsystems of the system 100 may include a computer system suitable for carrying out at least a portion of the steps described herein. Therefore, the aforementioned description should not be interpreted as a limitation on the present invention but merely an illustration. Further, the one or more computing systems 130 may be configured to perform any other step(s) of any of the method embodiments described herein.
• the computer system 130 may be any computer system 130.
• the one or more computing systems 130 may be coupled to computing systems associated with the detectors of system 100.
• the detectors may be controlled directly by a single computer system coupled to computer system 130.
• the computer system 130 of the metrology system 100 may be configured to receive and/or acquire data or
• the transmission medium may include wireline and/or wireless portions .
• the transmission medium may serve as a data link between the computer system 130 and other subsystems of the system 100.
• Computer system 130 of system 300 may be configured to receive and/or acquire data or information (e.g. , measurement results, modeling inputs, modeling results, etc.) from other systems by a transmission medium that may include wireline and/or wireless portions.
• the transmission medium may serve as a data link between the computer system 130 and other systems (e.g., memory on ⁇ board metrology system 100, external memory, or other external systems) .
• the computing system. 130 may be configured to receive measurement data from, a storage medium (i.e., memory 132 or an external memory) via a data link.
• spectral measurement results obtained using spectrometer 113 may be stored in a
• the spectral results may be imported from on-board memory or from an external memory system.
• the computer system 130 may send data to other systems via a transmission medium. For instance, a parameter value 140 determined by computer system 130 may be communicated and stored in an external memory. In this regard, measurement results may be
• Computing system 130 may include, but is not.
• the term "com.put.ing system.” may be broadly defined to encompass any device having one or more processors, which execute
• Program instructions 134 implementing methods such as those described herein may be transmitted over a
• program instructions 134 stored in memory 132 are
• exemplary computer-readable media include read-only memory, a random access memory, a
• critical dimension includes any critical dimension of a structure (e.g., bottom critical dimension, middle critical dimension, top critical dimension, sidewall angle, grating height, etc.), a critical dimension between any two or more structures
• Structures may include three dimensional structures, patterned
• critical dimension application or “critical dimension measurement
• metalology system includes any system employed at least in part to
• the metrology system 100 may be configured for measurement of patterned wafers and/or unpatterned wafers .
• the metrology system may be configured as a LED inspec ion tool, edge inspection tool, backside inspection tool, macro-inspection tool, or multi-mode inspection tool (involving data from one or more platforms simultaneously), and any other metrology or inspection tool- that benefits from the calibration of system parameters based on critical dimension data.
• a semiconductor processing system e.g., an inspection system or a lithography system
• a specimen is used herein to refer to a wafer, a reticle, or any other sample that may be processed
• wafer generally refers to substrates formed of a semiconductor or non- semiconductor material. Examples include, but are not limited to, monocrystalline silicon, gallium arsenide, and indium phosphide. Such substrates may be commonly found and/or processed in semiconductor fabrication facilities. In some cases, a wafer may include only the substrate
• a wafer may include one or more layers of different materials formed upon a
• One or more layers formed on a wafer may be "patterned” or “unpatterned . "
• a wafer may include a plurality of dies having repeatable pattern features .
• a "reticle” may be a reticle at any stage of a reticle fabrication process, or a completed reticle that may or may not be released for use in a semiconductor fabrication facility.
• a reticle, or a “mask, " is generally defined as a substantially transparent substrate having substantially opaque regions formed thereon and configured in a pattern.
• the substrate may include, for example, a glass material such as amorphous S1O 2 .
• a reticle may be disposed above a resist-covered wafer during an exposure step of a lithography process such that the pattern on the reticle may be transferred to the resist.
• One or more layers formed on a wafer may be
• a wafer may include a plurality of dies, each having repeatable pattern
• Com.puter-.reada.ble media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another.
• a storage media may be any available media that can be accessed by a. general purpose or special purpose computer.
• such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special- purpose processor.
• any connection is properly termed a computer-readable medium. For example, if the software is transmitted from. a.
• coaxial cable fiber optic cable, twisted pair, digital subscriber line (DSL) , or wireless technologies such as infrared, radio, and microwave
• coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave
• Disk and disc includes compact disc (CD) , laser disc, optical disc, digital versatile disc (DVD) , floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

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