Systems and methods for determining parameters of patterned structures from optical data

Abstract

A control system and method for use in optical measurement of a patterned sample is presented. The control system includes a computer system configured for data communication with a measurement data provider, and includes a data processor configured and operable to receive and process first and second types of raw measurement data simultaneously collected from a patterned sample being measured, the first type and the second type of measurement data each comprise scatter measurement data characterized by a first relatively high signal-to-noise ratio and a predetermined first relatively low spatial resolution, and interferometric measurement data characterized by a second relatively low signal-to-noise ratio and a predetermined second relatively high spatial resolution, the data processor is configured to process the measurement data to determine a pattern parameter along a patterned sample characterized by a first signal-to-noise ratio and a second spatial resolution.

Description

[0001] Technical Field and Background

[0002] The present disclosure is generally in the field of optical metrology technology and relates to systems and methods for determining parameters of a patterned sample and is particularly useful in the semiconductor industry for automated measurements on semiconductor wafers.

[0003] Modern semiconductor technology requires patterned structures with very small features (i.e., nanoscale features). Note that the fabrication of semiconductor devices is typically performed on a production line while utilizing automated measurement and inspection of the fabricated structures, and automated measurement and inspection techniques should be able to operate with correspondingly high resolution and high signal-to-noise ratio features.

[0004] Optical critical dimension (OCD) metrology is considered a highly suitable technology in semiconductor manufacturing processes due to its extreme sensitivity, accuracy, flexibility and speed. The structural parameters of patterned samples that are usually measured by OCD include critical dimensions, height, sidewall angle, thickness, material properties, etc.

[0005] OCD is based on the use of scatterometry data (also referred to herein as "spectral data"), which is typically acquired as reflected optical radiation indicative of characteristics of a pattern at a wafer location. Optical models are typically applied to OCD metrology to determine whether the pattern at a wafer location is manufactured to the correct specifications. Hereinafter, the more general term "OCD model" refers to both physical models developed from principles of optics and machine learning models known in the art.

[0006] Semiconductor wafers hold multiple "dies", each of which is ultimately intended to become a functional chip. Measurements are usually made at dedicated areas between the dies, known as "test locations". It is highly preferred to perform metrology at locations within the die, guaranteeing the best correlation between metrology results and the characteristics of the functional structures inside the die. However, die design is often such that there is no uniform area large enough for optical metrology to measure, preventing the application of this metrology technique.

[0007] Exemplary scatterometry tools for measuring (acquiring) scatterometry data (e.g., spectrograms) may include spectroscopic ellipsometers (SE), spectroscopic reflectometers (SR), polarization spectroscopic reflectometers, and other spectral critical dimension (OCD) metrology tools. These tools are incorporated into currently available OCD metrology systems. Such OCD metrology systems are NOVA Advanced OCD metrology tools, commercially available from Nova Measurement Instruments Ltd. of Rehovot, Israel, measure pattern parameters at specified wafer locations, i.e., "in-die". Additional methods for measuring critical dimensions (CD) may include interferometry, X-ray Raman spectroscopy (XRS), X-ray diffraction (XRD), and other tools such as pump-probe tools. Some examples of these tools are disclosed in U.S. Patents US10,161,885, US10,054,423, US9,184,102, and US10,119,925, and in international pending patent applications published as WO2018 / 211505, all of which are assigned to the assignee of the present application and are incorporated herein by reference in their entirety.

[0008] The extraction of various information about the patterned sample can utilize the detection and analysis of the spectral phase of the light scattered from the sample, i.e., the relative phase between the incident electromagnetic wave and the reflected electromagnetic wave is usually different for different wavelengths, angles of incidence\azimuths and polarizations. The spectral phase can be measured using interference effects. One method of measuring the spectral phase utilizes so-called white light interferometry (WLI), which can be advantageously used for surface height measurements on 3-dimensional structures with surface profiles varying between nanometers and several centimeters.

[0009] WO2015 / 155779 assigned to the assignee of the present application describes a phase measurement technique. According to this technique, a measurement system comprises a broadband light source, an interferometric measurement system and a detection unit, and provides measurement data indicating the amplitude and phase of the reflected spectrum. The measurement data undergoes model-based processing, enabling the determination of parameters of the pattern in the sample. Summary of the invention

[0010] There is a need in the art for new methods for optical measurement of patterned samples that enable high resolution and high signal-to-noise ratio measurements of various pattern parameters in the sample. In particular, considering patterned samples such as semiconductor wafers, there is a need for measurements of small areas located inside functional dies, however, current optical metrology (OCD metrology) solutions are limited in their ability to achieve these measurements due to their too large measurement spots ("tens" or "tens" of microns). On the other hand, white light interferometry (WLI) cannot be used for optical metrology because the measurement quality of WLI is not sufficient to allow interpretation of underlying structural features.

[0011] The disclosed technique proposes an efficient combined OCD measurement and WLI measurement, thus extracting pattern parameters with high spatial resolution and spatial sampling (of the WLI technique) and high signal quality (of the OCD technique).

[0012] It should be noted that the quality of the measurement is usually defined by various factors including signal-to-noise ratio (SND), wavelength resolution, calibration accuracy, etc. Since the relatively low SNR of WLI (compared to the SNR of OCD) is the most critical disadvantage of WLI, the relatively low measurement signal quality of WLI is referred to as "relatively low SNR" compared to "relatively high SNR of OCD".

[0013] A typical setup for white light interferometry is schematically shown in Figure 1. In this setup, a Michelson interferometer utilizes a broadband light source and a camera used as a detector at the output of the interferometer. The sample to be measured is placed in one arm of the interferometer and a reference mirror is placed on the other arm. Both the sample and the mirror are imaged onto the camera through converging optics. During the measurement, the reference mirror or the sample is moved in the longitudinal direction (Z axis). The light reflected from the sample surface and the reference mirror is superimposed and interferes on the detector. The full interference pattern (reflectivity versus mirror position z) is acquired for each pixel on the camera, together comprising the white light image.

[0014] The key sources of error in white light interferometry are the random arrival of photons at the detector ("Poisson" or "shot" noise), variations in the speed of the mirror motion (positioning noise), mirror orientation, and the discretization of the measured analog signal (discretization noise). Shot noise caused by the random arrival of photons at the detector is unique among the noise sources mentioned here. As a fundamental property of the quantum nature of light, shot noise cannot be reduced. Shot noise imposes a fundamental limit on the maximum signal-to-noise ratio (SNR) that can be achieved using WLI techniques.

[0015] WLI provides a signal with a signal-to-noise ratio that is typically too low, making it potentially inadequate for measurements on high-end semiconductor structures. In particular, WLI cannot by itself successfully address the applications of patterned structures (i.e., providing dimensional and material parameters) provided by modern OCD solutions (e.g., scatterometry, etc.).

[0016] On the other hand, WLI utilizes a matrix of pixels for light detection and can therefore provide relatively high spatial resolution of measurements compared to that of OCD, in addition to its ability to provide parallel characterization of multiple locations over the imaging area.

[0017] The present disclosure proposes a new technique that overcomes the challenges of resolution and signal quality / signal-to-noise ratio by appropriately combining OCD measurement techniques and WLI measurement techniques, so that the resulting solution is superior to the solution that can be provided by each of them on its own. The inventors have found that by performing OCD measurements at dedicated pads / points of the sample (using standard OCD techniques) and solving for application parameters (using model-based processing and adaptation techniques), performing WLI measurements at the same points (and preferably simultaneously with the OCD measurements) and obtaining a spectrum with greater noise than using standard OCD, a better final result (accurate measurement of pattern parameters) can be obtained. This is achieved by using the OCD solution to optimize the applied parameters and obtain a nominal solution at the measurement location, and using the WLI signal, by using the difference between the WLI data and the OCD channel to extract a high spatial resolution map of the parameter distribution within the measurement area.

[0018] Therefore, according to a broad aspect of the present disclosure, a control system for use in optical measurement of a patterned sample is proposed, the control system comprising a computer system configured for data communication with a measurement data provider, and comprising a data processor configured and operable to receive and process first type and second type raw measurement data collected simultaneously from the patterned sample being measured, the first type and second type measurement data respectively comprising scattering measurement data characterized by a first relatively high signal-to-noise ratio and a predetermined first relatively low spatial resolution, and interferometry measurement data characterized by a second relatively low signal-to-noise ratio and a predetermined second relatively high spatial resolution, the data processor being configured to process the measurement data to determine pattern parameters along the patterned sample characterized by the first signal-to-noise ratio and the second spatial resolution.

[0019] In some embodiments, the data processor includes: a data processing tool, which is configured and operable to process the scatter measurement data using at least one of a model-based processing or a machine learning processing of the scatter measurement data, and for each measurement point on the sample, extract an average value of at least one pattern parameter in the sample (for example, obtained from a theoretical spectrum corresponding to a best fit condition with the scatter measurement data); an optimization tool, which is configured and operable to process the interferometry data to convert each pixel-related interference pattern of the interferometry data into a pixel-related spectrum, and use the scatter measurement data (for example, the scatter measurement data of the best fit condition) to extract a spectral portion that matches the scatter measurement data from the spectrum related to each pixel, and generate corresponding first matching spectrum and second matching spectrum; and a spectrum analyzer tool, which is configured and operable to analyze the first matching spectrum and the second matching spectrum, and extract the distribution of the pixel-related measurement values ​​of the at least one parameter in a pixel matrix of the size of the measurement point from the spectral difference between the first matching spectrum and the second matching spectrum, and generate output data indicating the distribution.

[0020] The output data may be in the form of a map of parameter values ​​for each of the at least one parameter.

[0021] According to another broad aspect of the present disclosure, a system for measuring parameters of a patterned sample is provided, comprising:

[0022] a measurement system configured to simultaneously perform scatterometry and spectral interferometry for each measurement point on the sample, the measurement system comprising: a broadband light source generating light propagating along an illumination channel toward the measurement point, an interferometry component, and a detection system comprising a spectrometer and a pixelated detector, the spectrometer and the pixelated detector respectively generating a first type of measurement data and a second type of measurement data collected simultaneously from the measurement point on the sample, the first type of measurement data being scatterometry data characterized by a first relatively high signal-to-noise ratio and a first relatively low spatial resolution, and the second type of measurement data being interferometry data characterized by a second relatively low signal-to-noise ratio and a second relatively high spatial resolution; and

[0023] The control system described above.

[0024] The measurement system may be configured and operable to selectively activate the interferometry component to collect the second type of measurement data and deactivate the interferometry component to collect scatterometry data.

[0025] The present disclosure also proposes a data analysis method for use in optical measurement of a patterned sample to determine at least one pattern parameter of interest, the method comprising:

[0026] providing first and second types of raw measurement data collected simultaneously from the patterned sample being measured, the first and second types of measurement data respectively comprising scatterometry data characterized by a first relatively high signal-to-noise ratio and a first relatively low spatial resolution, and interferometry data characterized by a second relatively low signal-to-noise ratio and a second relatively high spatial resolution,

[0027] The first type and the second type of measurement data are processed to determine each of at least one parameter of interest using the first signal-to-noise ratio and the second spatial resolution.

[0028] The measurement data can be processed as follows:

[0029] applying at least one of a model-based process and a machine learning process to the scatterometry data to extract a mean value of at least one pattern parameter of the sample for each measurement point on the sample;

[0030] processing the interferometry data to convert an interferogram associated with each pixel in the interferometry data into a pixel-associated spectrum,

[0031] Utilizing the scatterometry data, extracting a spectral portion that matches the scatterometry data from the spectrum associated with each pixel, and generating a corresponding first matching spectrum and a second matching spectrum; and

[0032] The first matching spectrum and the second matching spectrum are analyzed and a distribution of pixel-dependent measurement values ​​of at least one parameter in a pixel matrix of a size of the measurement point is extracted from a spectral difference between the first matching spectrum and the second matching spectrum, and output data indicative of the distribution is generated. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to better understand the subject matter disclosed herein and to demonstrate how it may be carried out in practice, embodiments will now be described, by way of non-limiting examples only, with reference to the accompanying drawings, in which:

[0034] Figure 1 is a general technology WLI setup;

[0035] Figure 2A shows configurations and operations for processing combined optical data measurement data collected from a sample by a combined OCD measurement and WLI measurement system in accordance with the present disclosure;

[0036] Figure 2B and Figure 2C Two examples of combined measurement systems suitable for use in the techniques of the present disclosure are shown;

[0037] Figure 2D and Figure 2E Flowcharts showing two examples of operating steps of a control system according to the present disclosure;

[0038] Figure 3A and Figure 3B One of the data processing steps for extracting spectral data from WLI raw measurement data is illustrated;

[0039] Figure 4 The spectral analysis step of data processing (spectral matching step between OCD data and WLI data) is illustrated;

[0040] Figure 5A illustrating parameter values ​​of samples extracted from OCD data measurement data for an array / matrix of measurement points; and

[0041] Figure 5B The distribution (map) of pixel-dependent values ​​of the same parameter within each measurement point extracted from the combined OCD measurement and WLI measurement is illustrated. DETAILED DESCRIPTION

[0042] FIG. 1 shows a typical WLI measurement scheme that may be used as part of a measurement system suitable for providing WLI measurement data.

[0043] refer to Figure 2A , Figure 2A A control system 10 of the present disclosure is shown for processing combined measurement data CMD comprising OCD measurement data MD-OCD and WLI measurement data MD-WLI. The combined measurement data are acquired from a measurement data provider MDP, which may be a combined measurement system 20 that performs these combined / mixed measurements on a sample (online operation mode of the control system 10) or a storage device in which previously collected combined measurement data are maintained (offline operation mode of the control system 10). It should be noted that, for example, the control system 10 may be integrated with the measurement system 20.

[0044] The measurement system 20 is preferably configured to simultaneously perform OCD measurement and WLI measurement applied to the same measurement position (point) on the sample, and includes an OCD measurement system 20A and a WLI measurement system 20B. The OCD measurement system 20A and the WLI measurement system 20B simultaneously perform respective first-type measurements and second-type measurements and provide first-type measurement data as scatterometry data MD-OCD and second-type measurement data as interferometry data MD-WLI. The scatterometry data is characterized by a first relatively high signal-to-noise ratio (quality) and a predetermined first relatively low spatial resolution, and the interferometry data is characterized by a second relatively low signal-to-noise ratio (quality) and a second relatively high spatial resolution.

[0045] The control system 10 is configured as a computer system, including but not limited to main functional tools such as data input means 10A, data output means 10B, memory 10C and data processor 10D. It should be understood that the control system 10 uses any known suitable communication technology (e.g., wireless communication of any known suitable type and communication protocol) to communicate data with the measurement data provider 20. These communication technologies are known per se and do not form part of the present application and therefore do not need to be described.

[0046] The data processor 10D is configured and operable in accordance with the present disclosure to process the combined measurement data and extract values ​​of one or more pattern parameters along the patterned sample characterized by a relatively high (OCD measured) quality / signal-to-noise ratio and a relatively high (WLI measured) spatial resolution.

[0047] The data processor 10D includes an OCD data processing tool 12 configured and operable to obtain average sample features. This can be achieved using model-based processing and / or machine learning techniques. In the following description, a model-based (fitting-based) technique is exemplified, but it should be understood that the technology of the present disclosure is not limited to such model-based data processing.

[0048] Further provided in the data processor is a WLI data optimizer 14 and a spectrum analyzer 16, the WLI data optimizer 14 being configured and operable to extract spectral portions / features matching the OCD spectrum from the WLI measurement data [(WLI) spectr pixel ) match ] ij , and the spectrum analyzer 16 is configured and operable to find, for each pixel in the pixelated detector of the WLI measurement system, a spectral difference between a spectrum extracted from the WLI measurement data at that pixel and the OCD spectrum, and to determine a parameter difference from an average parameter (obtained from the OCD) using the spectral difference. Specific examples of data processing techniques are described further below.

[0049] Figure 2B and Figure 2CTwo specific, non-limiting examples of possible configurations of the combined measurement system are shown. For ease of understanding, the same reference numerals are used to identify components that are common in all examples. As shown in these examples, the system 20 includes a broadband light source that generates light propagating along the illumination channel IL toward the sample S to be measured, a detection system 24 including an OCD detector 24A (spectrometer) and a pixelated detector (CCD) 24B, and an interferometric measurement assembly 26. It should be noted that two different light sources can be used - one optimized for OCD and the other for WLI measurement. The interferometric measurement assembly 26 includes a beam splitter / combiner 26A that divides the illumination channel IL into a sample arm SA where the sample S is located and a reference arm RA where the movable reflector 28 is located, and combines light returned from the sample and reflector to propagate toward the detection channel DC. Also provided in the system 20 are beam splitters / combiners 26B and 26C, where the beam splitter / combiner 26B is located in the illumination and detection channels to direct the incident light into the interferometric measurement assembly and direct the combined light to the detection system 24, and the beam splitter 26C divides the detection channel into an OCD detection path DP1 and a camera detection path DP2. The system 20 may also include lenses (e.g., an objective lens OL) and other light directing elements.

[0050] Therefore, the input light L from the light source 22 in The light is reflected by the beam splitter / combiner 26B toward the interferometric assembly (eg, through the objective lens OL), and then split by the beam splitter / combiner 26A into a sample incident component L that impinges on the illumination point on the sample. (S) and the reference incident component L interacting with the reflector 28 (R) The corresponding sample and reference reflection components, L (S) ref and L (R) ref , combined into a combined light beam L by the beam splitter / combiner 26A com The combined light beam L com Directed toward beam splitter / combiner 26C, which splits this combined light beam into a first light component L that propagates along detection paths DP1 and DP2 to OCD detector 24A and camera 24B. (1) com and the second light component L (2) com These detectors 24A and 24B generate corresponding types of measurement data MD-OCD and MD-WLI which together form combined measurement data which are processed by the control system 10 .

[0051] It should be noted that the collection of WLI measurement data requires the collection of combined beam and optical path difference OPD data (from the sample arm and the reference arm) while moving the reflector 28, and the collection of OCD cannot operate using interference effects. Therefore, the system 20 may include a shutter 27 located in front of the reflector 28 and displaceable between an open state and a closed state to activate and deactivate the reference arm, respectively; or alternatively, the system operates to collect OCD data at a point in time during which the reflector is outside the reference arm (or ignores OCD data collected during the time interval when the reflector is activated in the reference arm).

[0052] The OCD measurement data portion MD-OCD generated by detector 24A is in the form of a spectrum collected from the entire illumination point and therefore has a relatively low spatial resolution defined by the point size. The WLI measurement data portion MD-WLI generated by detector 24B relative to the same measurement phase (i.e., for the same illumination point) is interferometric measurement data in the form of an interferogram matrix (interferogram per camera pixel). These data are actually in the form of pixel gray levels collected during the movement of the reflector (e.g., about 30μ @ 15 seconds) and are therefore relatively noisy, but are characterized by a higher spatial resolution (pixel size resolution) compared to the OCD measurement data. As will be further described below, the combined measurement data is received and processed by the control system 10.

[0053] The data can be measured using a polarizing filter. Figure 2C is shown in the example.

[0054] It should be noted that in these specific but non-limiting examples, measurement systems configured to operate in normal incidence mode and bright field measurement mode are illustrated. However, it should be understood that the invention is not limited to these configurations, and in general, measurements may be acquired in any (oblique or other) incidence angle mode, and dark field measurement mode or a combination of bright field and dark field measurement modes may also be utilized.

[0055] Considering the normal incidence and bright field detection modes exemplified herein, beam splitter / combiner unit 26A and beam splitter / combiner unit 26B are both located in the illumination and detection channels. Light directing optics may optionally include a collimating lens in the illumination channel, which is in the optical path of the input light, and / or a tube lens in the detection channel, which is in the optical path of the collected light that is propagated to the detection system.

[0056] As in Figure 2C The combined measurement system 20 exemplified in FIG. Figure 2BThe combined measurement system 20 is similar to the system of FIG. 1 , but the combined measurement system 20 further includes a polarizer 32 and a polarizer 34 located in the illumination (IL) channel and the detection (DC) channel, respectively. More specifically, the input light L from the light source 22 in The input light (L) passes through the polarizer 32 on its way and is polarized in a specific manner (e.g., linearly polarized). in)pol The incident light is directed by the beam splitter / combiner 26B toward the beam splitter / combiner 26A (eg, through the objective lens OL), and then split by the beam splitter / combiner 26A into a sample incident component L that impinges on the illumination point on the sample. (S) in and the reference incident component L interacting with the reflector 28 (R) in The corresponding sample reflection component L (S) ref and the reference reflection component L (R) ref , is combined by the beam splitter / combiner 26A into a combined light beam L having the specific polarization com , the combined beam L com The light beam L passes through the objective lens OL and the beam splitter / combiner 26B to the polarizer 34, which allows only the light of the specific polarization to propagate to the detection system 24, specifically to the beam splitter / combiner 26C. The beam splitter / combiner 26C converts this combined light beam L having the specific polarization into com The light component L is divided into the first light component L and propagates along the detection path DP1 and the detection path DP2 to the OCD detector 24A and the camera 24B. (1) com and the second light component L (2) com . These detectors 24A and detectors 24B generate corresponding types of measurement data MD-OCD and MD-WLI, which together form combined measurement data processed by the control system 10. It should be understood that the use of polarizers 32 and 34 housed and oriented as described above actually provides a cross-polarization scheme that results in a dark field measurement mode. It should also be understood that when the mirror 28 is not used (i.e., moved out of the optical path of the incident light or deactivated by using a suitable shutter (e.g., shutter 27)), the system 30 can be operated as a spectroscopic reflectometer. Therefore, like a spectroscopic interferometer and a spectroscopic reflectometer, the same system 30 can be shifted between two different operating modes.

[0057] Figure 2D Flowchart 100 illustrates the operating steps of data processor 10D.

[0058] The data processor 10D comprises an OCD data processing tool 12 configured to interpret the OCD measurement data MD-OCD using, for example, one or more modes and to extract, for example, one or more parameters (average parameter values ​​for the measurement points) of the sample from a theoretical (modeled) OCD spectrum corresponding to the best fit conditions to the measured OCD spectrum MD-OCD. Any patterned structure (application) measured in order to determine its dimensions and material parameters (i.e., to solve the application) defines a parameter space P OCD , parameter space P OCD It may include values ​​such as TCD (top critical dimension), BCD (bottom critical dimension), SWA (sidewall angle), etc. Therefore, assuming that in the parameter space P OCD The initial vector of parameters for the application (at each location on the wafer), (Step 102). Due to the low SNR (signal to noise ratio) of WLI technology, it is not possible to However, although with limited resolution, standard OCD techniques can solve the application and find the correct vector of parameter values. Thus, in step 104, OCD measurement data (spectra) are received using OCD standard techniques (e.g., scatterometry), and then in step 106, a typical model-based process is used to select the best matching theoretical OCD spectrum and calculate a vector of final parameters, It should be understood that these are to utilize WLI, The optimized parameters. The vector of this parameter is is closer to the true solution, but may have, for example, limited resolution, so WLI can be used to optimize it.

[0059] The WLI measurement data provided / received at the data processor (step 110), as indicated above, is preferably measured simultaneously with the OCD data using a common optical scheme. Figure 3A As shown, these WLI measurement data are in the form of an interferogram for each camera pixel.

[0060] The data processor 10D includes a WLI data optimizer 14 configured and operable to apply a Fourier transform process to an interference pattern (MD-WLI) associated with each ijth pixel in the pixel matrix of the detector 24B. pixel ) ij ( Figure 3A ) to transform it into Figure 3B Spectral data for the example ((WLI) spectr pixel ) ij(Step 107). The optimizer further operates to utilize the OCD spectral data to obtain spectral data associated with the WLI pixels ((WLI) spectr pixel ) ik Extract the spectral part / feature that best matches the OCD spectrum [(WLI) spectr pixel ) match ] ij And therefore for P WLI Parameter optimization WLI data, from Start (step 112). Figure 4 Note that the OCD spectrum corresponding to the best fit condition with the theoretical OCD data describes the distribution of parameter values ​​with high accuracy but low spatial resolution, and the corresponding matching WLI spectral data involves the same pattern parameters but with higher spatial resolution.

[0061] Further provided in the data processor 10D is a spectrum analyzer tool 16 which compares between the OCD spectrum and its matching WLI spectrum portion to determine the spectral difference between the two spectra and extracts from the spectral difference the corresponding parameter value difference (for each parameter) at pixel resolution within the illumination point (step 114). To this end, the spectrum analyzer tool 16 exploits the known correlation between OCD spectrum changes and corresponding parameter changes. The output data may be in the form of a map of the changes in the parameter found for each pixel (or number of pixels) (step 116).

[0062] refer to Figure 2E , Figure 2E The operation steps of the control system according to another embodiment of the present disclosure are illustrated by means of a flowchart 200. OCD OCD measurement data in the form of (λ) is provided (in step 202) and measured using standard OCD techniques (e.g., scatterometry). The OCD measurement data is interpreted in step 206 to obtain a vector of parameters These parameters characterize the average characteristics of the structure measured over a relatively large OCD spot ("tens" or "several tens" of micrometers). WLI (Z)] ij∈pixels The WLI measurement data is provided in the form of an interference pattern for each pixel ij in the pixel matrix of the detector 24B (step 204). In step 208, a spectral representation of the WLI data associated with each pixel is obtained [S WLI (λ)] ij∈pixels(For example, by applying a Fourier transform process to the interference pattern associated with each ijth pixel). In the next step (210), the spectral difference [ΔS WLI (λ)] i,j , [ΔS WLI (λ)] i,j =[S WLI (λ)] ij -S OCD (λ). In the final step (212), from the OCD (obtained in step 206) ) to obtain accurate, average parameter values ​​and spectral differences [Δs WLI (λ)] i,j It is used to interpret the parameter value at each WLI pixel, thus increasing the measurement resolution while maintaining high accuracy and signal-to-noise ratio.

[0063] refer to Figure 5A and Figure 5B ,in, Figure 5A illustrates parameter values ​​extracted from OCD measurement data for a matrix of measurement / illumination points, and Figure 5B Resulting graphs illustrating parameter variations (distribution of parameter values) within each pixel in the pixel matrix for the dot size of each dot in the corresponding dot matrix. In this specific non-limiting example, the pattern parameter being measured is GATE_AB_FIN.

[0064] It should be noted that an application may include many parameters (dim(P OCD )~10), all parameters vary on sample (wafer) and die to die or wafer to wafer (W2W), and using combined OCD measurement and WLI measurement and the above-mentioned data processing, the measurement of each parameter of selected parameters can be optimized. Figure 3B The results of the optimization of the gate height parameters are shown and demonstrate that the matched WLI-OCD is very good.

[0065] Therefore, combining the standard OCD measurement for average cross-pad / point application solution with the WLI technique provides a better starting point for WLI optimization, allowing even for low SNR signals to resolve POI (point of interest) variations across the pad. Within the pad, the requirements for "out-of-point contamination" are lower and the lateral resolution becomes about 7μ.

Claims

1. A control system for use in optical measurement of a patterned sample, the control system comprising a computer system configured for data communication with a measurement data provider, and comprising a data processor configured and operable to receive and process first type and second type raw measurement data collected simultaneously from the patterned sample being measured, the first type and second type measurement data respectively comprising scattered measurement data characterized by a first relatively high signal-to-noise ratio and a predetermined first relatively low spatial resolution, and interferometric measurement data characterized by a second relatively low signal-to-noise ratio and a predetermined second relatively high spatial resolution, the data processor being configured to process the measurement data to determine pattern parameters along the patterned sample characterized by the first signal-to-noise ratio and the second spatial resolution.

2. The control system according to claim 1, wherein: The data processor comprises: a data processing tool configured and operable to process the scatterometry data using at least one of a model-based processing or a machine learning processing of the scatterometry data and, for each measurement point on the sample, extract a mean value of at least one pattern parameter of the sample; an optimization tool configured and operable to process the interferometry data to convert an interference pattern associated with each pixel in the interferometry data into a pixel-associated spectrum, and to utilize the scatterometry data to extract a spectral portion from each pixel-associated spectrum that matches the scatterometry data, and to generate corresponding first and second matched spectra; and A spectrum analyzer tool is configured and operable to analyze the first matching spectrum and the second matching spectrum, and to extract the distribution of pixel-related measurement values ​​of at least one parameter in a pixel matrix of the size of the measurement point from the spectral difference between the first matching spectrum and the second matching spectrum, and to generate output data indicative of the distribution.

3. The control system according to claim 2, wherein: The output data is in the form of a map of parameter values ​​for each of the at least one parameter.

4. A system for measuring parameters of a patterned sample, the system comprising: a measurement system configured to simultaneously perform scatterometry and spectral interferometry for each measurement point on a sample, the measurement system comprising: a broadband light source generating light propagating along an illumination channel toward the measurement point, an interferometry component, and a detection system comprising a spectrometer and a pixelated detector, the spectrometer and the pixelated detector respectively generating a first type of measurement data and a second type of measurement data collected simultaneously from the measurement point on the sample, the first type of measurement data being scatterometry data characterized by a first relatively high signal-to-noise ratio and a first relatively low spatial resolution, and the second type of measurement data being interferometry data characterized by a second relatively low signal-to-noise ratio and a second relatively high spatial resolution; and A control system according to claim 1.

5. The system of claim 4, configured and operable to selectively activate the interferometry component to collect the second type of measurement data and deactivate the interferometry component to collect the scatterometry data.

6. A method of data analysis for use in optical measurement of a patterned sample to determine at least one pattern parameter of interest, the method comprising: providing first and second types of raw measurement data collected simultaneously from the patterned sample being measured, the first and second types of measurement data respectively comprising scatterometry data characterized by a first relatively high signal-to-noise ratio and a first relatively low spatial resolution, and interferometry data characterized by a second relatively low signal-to-noise ratio and a second relatively high spatial resolution, The first and second types of measurement data are processed to determine each of at least one parameter of interest using a first signal-to-noise ratio and a second spatial resolution.

7. The method according to claim 6, wherein: The processing includes: applying at least one of a model-based process and a machine learning process to the scatterometry data to extract, for each measurement point on the sample, a mean value of at least one pattern parameter of the sample; processing the interferometry data to convert an interference pattern associated with each pixel in the interferometry data into a pixel-associated spectrum, extracting a spectral portion matching the scatterometry data from a spectrum associated with each pixel using the scatterometry data, and generating a corresponding first matching spectrum and a second matching spectrum; and The first matching spectrum and the second matching spectrum are analyzed, and a distribution of pixel-dependent measurement values ​​of at least one parameter in a pixel matrix of the size of the measurement point is extracted from a spectral difference between the first matching spectrum and the second matching spectrum, and output data indicating the distribution is generated.

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