SYSTEM AND METHOD FOR DETERMINING PARAMETERS OF PATTERNED STRUCTURES FROM OPTICAL DATA

Abstract

A control system and method are presented for use in optical measurements on patterned samples. The control system comprises a computer system configured for data communication with a measured data provider and comprising a data processor configured and operable to receive and process raw measured data of first and second types concurrently collected from the patterned sample being measured. said first and second types of the measured data comprising, respectively. scatterometry measured data. characterized by first relatively high signal-to-noise and predetermined first relatively low spatial resolution, and interferometric measured data characterized by second relatively low signal-to-noise and predetermined second relatively high spatial resolution, said data processor being configured to process the measured data to determine pattern parameters along said patterned sample characterized by said first signal to-noise and said second spatial resolution.

Claims

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

2. The control system according to claim 1, wherein the data processor comprises: a data processing utility configured and operable to process the scatterometry measured data utilizing at least one of model-based or machine learning processing of said scatterometry measured data and extract an average value of at least one pattern parameter of the sample for each measured spot on the sample; an optimizer utility configured and operable to process the interferometric measured data to transform each pixel-related interferogram of the interferometric measured data into a pixel-related spectrum, and utilize the scatterometry measured data to extract, from said each pixel-related spectrum, a spectral part matching the scatterometry data, and generate respective first and second matching spectra; and a spectral analyzer utility configured and operable to analyze the first and second matching spectra and extract, from a spectral difference between the first and second matching spectra, distribution of pixel-related measured values of said at least one parameter in a pixel matrix of a size of the measured spot, and generate output data indicative of said distribution.

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

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

5. The system according to claim 4, configured and operable to selectively activate the interferometric assembly to collect said second type measured data and disactivate the interferometric assembly to collect the scatterometric measured data.

6. A data analysis method for use in optical measurements on patterned samples to determine at least one pattern parameter of interest, the method comprising: providing raw measured data of first and second types concurrently collected from the patterned sample being measured, said first and second types of the measured data comprising, respectively, scatterometry measured data characterized by first relatively high signal-to-noise and first relatively low spatial resolution, and interferometric measured data characterized by second relatively low signal-to-noise and second relatively high spatial resolution, processing said first and second types of the measured data to determine each of said at least one parameter of interest with said first signal to-noise and said second spatial resolution.

7. The method according to claim 6, wherein said processing comprises: applying at least one of model-based and machine learning processing to the scatterometry measured data to extract an average value of at least one pattern parameter of the sample for each measured spot on the sample; processing the interferometric measured data to transform each pixel-related interferogram of the interferometric measured data into a pixel-related spectrum, utilizing the scatterometry measured data to extract, from said each pixel-related spectrum, a spectral part matching the scatterometry data, and generating respective first and second matching spectra; and analyzing the first and second matching spectra and extracting from a spectral difference between the first and second matching spectra, distribution of pixel-related measured values of said at least one parameter in a pixel matrix of a size of the measured spot and generating output data indicative of said distribution.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to better understand the subject matter that is disclosed herein and to exemplify 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:

[0032] FIG. 1 is a general art WLI setup;

[0033] FIG. 2A illustrates the configuration and operation of a control system according to the present disclosure for processing combined optical data measured data collected from a sample by a combined OCD and WLI measurement system;

[0034] FIGS. 2B and 2C illustrate two examples of the combined measurement system suitable to be used in the technique of the present disclosure;

[0035] FIGS. 2D and 2E illustrate flow diagrams of two examples of the operational steps of the control system according to the present disclosure;

[0036] FIGS. 3A and 3B exemplify one of the data processing steps aimed at extracting spectral data from the WLI raw measured data;

[0037] FIG. 4 exemplifies the spectral analysis step of the data processing (spectral matching step between the OCD and WLI data);

[0038] FIG. 5A exemplifies the sample's parameter values extracted from the OCD data measured data for an array/matrix of the measured spots; and

[0039] FIG. 5B exemplifies the distribution (map) of pixel-related values of the same parameter within each of the measured spots, as extracted from combined OCD and WLI measurements.

DETAILED DESCRIPTION OF EMBODIMENTS

[0040] FIG. 1 illustrates a typical WLI measurement scheme, which can be used as part of a measurement system suitable to provide WLI measured data.

[0041] Reference is made to FIG. 2A illustrating a control system 10 of the present disclosure for processing combined measured data CMD including OCD measured data MD-OCD and WLI measured data MD-WLI. The combined measured data is obtained from a measured data provider MDP, which may be a combined measurement system 20 performing such combined/hybrid measurements on a sample (on-line operational mode of the control system 10) or a storage device in which the previously collected combined measured data is kept (off-line operational mode of the control system 10). It should be noted that the control system 10 may for example be integral with the measurement system 20.

[0042] The measurement system 20 is preferably configured to concurrently perform OCD and WLI measurements applied to the same measurement sites (spots) on a sample, and includes an OCD measurement system 20A and an WLI measurements system 20B. The OCD and WLI measurement systems 20A and 20B concurrently perform respective first and second types measurements and provide first type measured data being scatterometry measured data MD-OCD and second type measured data being interferometric measured data MD-WLI. The scatterometry measured data is characterized by first relatively high signal-to-noise (quality) and predetermined first relatively low spatial resolution, and the interferometric measured data is characterized by second relatively low signal-to-noise (quality) and second relatively high spatial resolution.

[0043] The control system 10 is configured as a computer system including inter alia such main functional utilities as data input utility 10A, data output utility 10B, memory 10C and data processor 10D. It should be understood that the control system 10 is in data communication with the measured data provider 20 using any known suitable communication techniques, e.g., wireless communication of any known suitable type and communication protocols. These communication techniques are known per se and do not form part of the present invention and therefore need not be described.

[0044] The data processor 10D is configured and operable according to the present disclosure to process the combined measured data and extract values of one or more pattern parameters along the patterned sample characterized by the relatively high quality/signal to-noise (that of the OCD measurements) and relatively high spatial resolution (that of the WLI measurements).

[0045] The data processor 10D includes an OCD data processing utility 12 which is configured and operable to obtain the average sample characteristics. This can be implemented using model-based processing and/or machine learning techniques. In the description below, the model-based (fitting based) technique is exemplified, but it should be understood that the technique of the present disclosure is not limited to such model-based data processing.

[0046] Further provided in the data processor is an WLI data optimizer 14 configured and operable to extract from the WLI measured data a spectral part/signature [(WLI).sup.spect.sub.pixel).sub.match].sub.ij matching the OCD spectrum, and a spectral analyzer 16 configured and operable to find, for each pixel of the pixelated detector of the WLI measurement system, a spectral difference between the spectrum extracted from the WLI measured data at that pixel and the OCD spectrum, and using this spectral difference to determine the parameter difference from the average parameter (as obtained from the OCD). Specific examples of the data processing technique will be described further below.

[0047] FIGS. 2B and 2C show two specific not-limiting examples of the possible configurations of the combined measurement system. To facilitate understanding, the same reference numbers are used to identify the components common in all the examples.

[0048] As shown in these examples, the system 20 includes a broadband light source producing input light propagating along an illumination channel IL towards a sample S to be measured, a detection system 24 including an OCD detector 24A (spectrometer) and a pixelated detector (CCD) 24B, and an interferometric assembly 26. It should be noted that two different light sources could be usedone optimized for OCD and another for

[0049] WLI measurements. The interferometric assembly 26 includes a beam splitter/combiner 26A which splits the illumination channel IL into a sample arm SA where the sample S is and a reference arm RA where a movable reflector 28 is located and combines light returned from the sample and reflector to propagate towards a detection channel DC. Also provided in the system 20 is a beam splitter/combiner 26B located in the illumination and detection channels to direct incident light into the interferometric assembly and direct the combined light towards the detection system 24, and a beam splitter 26C which splits the detection channel into OCD detection path DP.sub.1 and camera detection path DP.sub.2. The system 20 may also include lenses (e.g., objective lens OL) as well as other light directing clement(s).

[0050] Thus, input light L.sub.in from the light source 22 is reflected by beam splitter/combiner 26B towards the interferometric assembly (e.g., via objective lens OL) and is then split by beam splitter/combiner 26A into sample incident component L.sup.(S).sub.in impinging onto an illumination spot on the sample and reference incident component L.sup.(R).sub.in interacting with the reflector 28. Corresponding sample and reference reflected components, L.sup.(S).sub.ref and L.sup.(R).sub.ref, are combined by the beam splitter/combiner 26A into a combined light beam L.sub.com to propagate to the beam splitter/combiner 26B which directs the combined light beam L.sub.com towards the beam splitter/combiner 26C which splits this combined light beam into first and second light components L.sup.(1).sub.com and L.sup.(2).sub.com propagating along the detection paths DP.sub.1 and DP.sub.2 to the OCD detector 24A and camera 24B. These detectors 24A and 24B generate corresponding types of measured data MD-OCD and MD-WLI forming together combined measured data to be processed by the control system 10.

[0051] It should be noted that collection of the WLI measured data requires collection of the combined light (from the sample and reference arms) and optical path difference OPD data while moving the reflector 28, while collection of OCD could not to operate with interference effect. Accordingly, the system 20 may include a shutter 27 located in front of the reflector 28 and shiftable between its open and closed states to, respectively, activate and disactivate the reference arm; or alternatively, the system operates to collect the OCD data in the time slots during which the reflector is outside the reference arm (or disregard the OCD data collected during the time intervals of reflector being active in the reference arm).

[0052] The OCD measured data portion MD-OCD generated by detector 24A is in the form of spectrum collected from the entire illuminated spot and thus has relatively low spatial resolution defined by the spot size. The WLI measured data portion MD-WLI generated by the detector 24B with respect to the same measurement session (i.e., for the same illumination spot) is interferometric data in the form of a matrix of interferograms (interferogram per camera pixel). Such data is actually in the form of pixels' gray scale collected during the reflector's movement e.g., 30 @15 sec) and is thus relatively noisy but characterized by higher spatial resolution (pixel size resolution) as compared to the OCD measured data. This combined measured data is received and processed by the control system 10, as will be described further below.

[0053] The measurement system may utilize polarizers. This is illustrated in the example of FIG. 2C.

[0054] It should be noted that in these specific but not limiting examples, the measurement system is exemplified as being configured for operation with the normal incidence mode and bright field measurement mode. It should, however, be understood that the invention is not limited to this configuration, and generally, measurement can be obtained in any (oblique or other) angle-of-incidence mode, as well as can utilize dark-field measurement mode or a combination of bright-and dark-field measurement modes.

[0055] Considering the normal incidence and bright-field detection modes exemplified herein, the beam splitter/combiners units 26A and 26B are located in both the illumination and detection channels. The light directing optics may optionally include a collimating lens in the illumination channel, being in the optical path of the input light, and/or a tube lens in the detection channel being in the optical path of collected light propagating to the detection system.

[0056] As exemplified in FIG. 2C the combined measurement system 20 is generally similar to the system of FIG. 2B but further includes polarizers 32 and 34 located, respectively, in illumination (IL) and detection (DC) channels. More specifically, input light L.sub.in on its way from the light source 22 passes through the polarizer 32 and a specifically polarized (e.g., linearly polarized) input light (L.sub.in).sub.pol is directed by beam splitter/combiner 26B towards beam splitter/combiner 26A (e.g. via objective lens OL) and is then split by beam splitter/combiner 26A into sample incident component L.sup.(S).sub.in impinging onto an illumination spot on the sample and reference incident component L.sup.(R).sub.in interacting with the reflector 28. Corresponding sample and reference reflected components, L.sup.(S).sub.ref and L.sup.(R).sub.ref, are combined by the beam splitter/combiner 26A into a combined light beam L.sub.com having said specific polarization, which passes through the objective OL and beam splitter/combiner 26B to the polarizer 34 which allows only light of said specific polarization to propagate to the detection system 24, specifically to the beam splitter/combiner 26C. This beam splitter/combiner 26C splits this combined light beam L.sub.com having said specific polarization into first and second light components L.sup.(1).sub.com and L.sup.(2).sub.com propagating along the detection paths DP.sub.1 and DP.sub.2 to the OCD detector 24A and camera 24B. These detectors 24A and 24B generate corresponding types of measured data MD-OCD and MD-WLI forming together combined measured data to be processed by the control system 10. It should be understood that using polarizers 32 and 34 accommodated and oriented as described above actually provides a cross-polarization scheme, which results in the dark-field measurement mode. It should also be understood that when the mirror 28 is not used (i.e., is moved out of the optical path of incident light or is inactivated by the use of an appropriate shutter, e.g., shutter 27), the system 30 can operate as a spectral reflectometer. Accordingly, the same system 30 may be shifted between two different operational modes, as a spectral interferometer and spectral reflectometer.

[0057] FIG. 2D exemplifies a flow diagram 100 of the operational steps of the data processor 10D.

[0058] The data processor 10D includes an OCD data processing utility 12 configured to e.g. utilize one or more models for interpreting OCD measured data MD-OCD and extract one or more parameters of the sample (average parameter value for the measurement spot), e.g. from the theoretical (modeled) OCD spectrum corresponding to the best fit condition with the measured OCD spectrum MD-OCD. Any patterned structure (an application) to be measured in order to determine its dimensional and material parameters (i.e., to solve the application) defines a parameter space .sub.OCD which may contain e.g., the values of TCD (top critical dimension), BCD (bottom critical dimension), SWA (sidewall angle), etc. Thus, initial vector of parameters, {right arrow over (P.sub.0)}, of an application (per site on wafer) in parameters' space .sub.OCD is guessed: {right arrow over (P.sub.0)}.sub.OCD (step 102). Due to the low SNR (signal-to-noise ratio) of the WLI technique, it cannot find the correct parameter values' vector from a general guess of {right arrow over (P.sub.0)}.sub.OCD, however the standard OCD technique can solve the application and find {right arrow over (P.sub.OCD)}, although with limited resolution. Therefore, in step 104, the OCD measured data (spectrum) is received being measured with OCD standard technique, e.g., scatterometry, and then using typical model-based processing best-match theoretical OCD spectrum is selected and vector of final parameters, {right arrow over (P.sub.OCD)}, is calculated in step 106. It should be understood that these are parameters that are to be optimized with WLI. .sub.WLI.Math..sub.OCD. This vector of parameters is much closer to the real solution than {right arrow over (P.sub.0)}, though may have e.g., a limited resolution, hence the WLI can be used to optimize it.

[0059] The WLI measured data is provided/received at the data processor (step 110), which as indicated above is preferably measured concurrently with the OCD data using the common optical scheme. As shown in FIG. 3A, such WLI measured data is in the form of interferogram for each camera pixel.

[0060] The data processor 10D includes the WLI data optimizer 14, which is configured and operable to apply Fourier transform processing to each ij-th pixel-related interferogram ((MD-WLI).sub.pixel).sub.ij (FIG. 3A) in the pixel matrix of the detector 24B to transform it to spectral data ((WLI).sup.spectr.sub.pixel).sub.ij (step 107) as exemplified in FIG. 3B. The optimizer further operates to utilize the OCD spectral data to extract, from the WLI pixel-related spectral data ((WLI).sup.spectr.sub.pixel).sub.ij, a spectral part/signature [(WLI).sup.spectr.sub.pixel).sub.match].sub.ij satisfying a best match with the OCD spectrum and thus optimize the WLI data on .sub.WLI parameters, starting from {right arrow over (P.sub.OCD)} (step 112). This is exemplified in FIG. 4. Keeping in mind that the OCD spectrum corresponding to best fit condition with the theoretical OCD data describes the parameter values' distribution with high accuracy but low spatial resolution, the corresponding matching WLI spectral data relates to the same pattern parameter(s) but with higher spatial resolution.

[0061] Further provided in the data processor 10D is the spectral analyzer utility 16.

[0062] which compares between the OCD spectrum and its matching WLI spectral part to determine the spectral difference between the two spectra and extract therefrom the corresponding parameter values difference (for each parameter) at the pixel resolution within the illuminated spot (step 114). To this end, the spectral analyzer 16 utilizes a known correlation between the OCD spectral variation and the corresponding parameter variation. The output data may be in the form of a map of the parameters' change found per pixel (or number of pixels) (step 116).

[0063] Reference is made to FIG. 2E exemplifying by way of a flow diagram 200 the operational steps of the control system according to another embodiment of the present disclosure. The OCD measured data in a form of a spectrum S.sup.OCD() is provided (step 202), being measured with OCD standard technique, e.g., scatterometry. The OCD measured data is interpreted in step 206, obtaining vector of parameters {right arrow over (P.sub.OCD)} characterizing the average characteristics of the measured structure across the relatively large OCD spot (few dozens or several tens microns). The WLI measured data is provided (step 204) in a form of interferograms [I.sup.WLI(z)].sub.ijpixels, one for each pixel ij in the pixel matrix of the detector 24B. The spectral representation [S.sup.WLI()].sub.ijpixels of each pixel-related WLI data is obtained in step 208 (e.g., by applying Fourier transform processing to each ij-th pixel-related interferogram). In the next step (210) the spectral difference [S.sup.WLI()].sub.i,j between OCD and WLI spectra at each WLI pixel is obtained ([S.sup.WLI()].sub.i,j=[S.sup.WLI()].sub.ijS.sup.OCD(). In the last step (212) accurate, average parameter values obtained from OCD ({right arrow over (P.sub.OCD)} obtained in step 206) and the spectral differences [S.sup.WLI()].sub.i,j are used to interpret parameter values at each WLI pixel, thereby increasing measurement resolution while keeping high accuracy and signal-to-noise.

[0064] Reference is made to FIGS. 5A and 5B where FIG. 5A exemplifies the parameter values extracted from the OCD measured data for the matrix of measured/illuminated spots, and FIG. 5B exemplifies the resulting map of the parameter change (parameter value's distribution) within each pixel in the pixel matrix of the spot size for each spot in the corresponding matrix of spots. In this specific not-limiting example, the pattern parameter being measured is GATE_AB_FIN.

[0065] It should be noted that the application may include many parameters (dim(.sub.OCD)10), all changing across the sample (wafer) and die-to-die or wafer-to-wafer (W2W) and using the combined OCD and WLI measurements and the above-described data processing, measurement of each of the selected parameters can be optimized. The result of the optimization of the Gate Height parameter is shown in FIG. 3B and proves that the match WLI-OCD is very good.

[0066] Thus, combining the standard OCD measurement for an average across-pad/spot application solution with the WLI technique provides the WLI optimization a much better starting point, allowing even for the low SNR signal to solve the POI (point-of-interest) variations across the pad. Within pad, the demand for out of spot contamination is lower, and the lateral resolution becomes 7 .