Combination of multi-wavelength raman and spectral ellipsometry to measure film stacks

Abstract

The thickness of the film stack of the workpiece and the composition of the film stack are determined using x-ray technology, spectroscopic ellipsometry (SE) and / or spectroscopic reflectometry (SR) and multi-wavelength Raman spectroscopy. The measurements are combined to form combined measured data. The combining includes regressing the second measurement and the third measurement. The combined measured data is then used to determine the thickness of the film stack and the composition of the film stack.

Description

[0001] Cross Reference to Related Applications

[0002] This application claims priority to provisional patent application filed on June 16, 2023, and assigned U.S. Application No. 63 / 521,555, the disclosure of which is hereby incorporated by reference. TECHNICAL FIELD

[0003] The present disclosure relates to measuring film stacks on workpieces, such as semiconductor wafers. BACKGROUND

[0004] The evolution of the semiconductor manufacturing industry has placed greater demands on yield management and, in particular, on metrology and inspection systems. Critical dimensions continue to shrink, but the industry needs to reduce the time to market high-yield, high-value production. Minimizing the total time from detecting a yield problem to fixing the problem maximizes the return on investment for semiconductor manufacturers.

[0005] Manufacturing semiconductor devices, such as logic and memory devices, typically includes processing semiconductor wafers using a large number of manufacturing processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor manufacturing process that involves transferring a pattern from a photomask to a photoresist disposed on a semiconductor wafer. Additional examples of semiconductor manufacturing processes include, but are not limited to, chemical-mechanical polishing (CMP), etching, deposition, and ion implantation. The arrangement of multiple semiconductor devices manufactured on a single semiconductor wafer can be separated into individual semiconductor devices.

[0006] Inspection processes are used at various steps during semiconductor manufacturing to detect defects on the workpieces to facilitate higher yields in the manufacturing process and thus higher profits. Inspection has always been an important part of manufacturing semiconductor devices, such as integrated circuits (ICs). However, as the dimensions of semiconductor devices decrease, inspection becomes even more important to the successful manufacture of acceptable semiconductor devices because smaller defects can cause device failure. For example, as the dimensions of semiconductor devices decrease, detection of defects with decreasing size has become necessary because even relatively smaller defects can cause unwanted aberrations in the semiconductor devices.

[0007] Metrology processes are also used at various steps during semiconductor fabrication to monitor and control the process. Metrology processes differ from inspection processes in that, unlike inspection processes in which defects are detected on a workpiece, metrology processes are used to measure one or more characteristics of the workpiece that cannot be determined using existing inspection tools. Metrology processes can be used to measure one or more characteristics of a workpiece such that performance of the process can be determined from the one or more characteristics. For example, metrology processes can measure dimensions of features formed on a workpiece during a process (e.g., line width, thickness, etc.). Additionally, if one or more characteristics of a workpiece are unacceptable (e.g., outside of a predetermined range for the characteristic), measurement of the one or more characteristics of the workpiece can be used to alter one or more parameters of the process such that additional workpieces manufactured by the process have acceptable characteristics.

[0008] In-line optical metrology techniques can include spectroscopic ellipsometry (SE) and spectroscopic reflectometry (SR). SE is an optical measurement technique that measures changes in polarization of outgoing light reflected from a sample (e.g., a workpiece). SE can measure parameters such as thickness, optical constants (e.g., refractive index and extinction coefficient (n and k)), material properties (e.g., crystallinity, surface roughness, alloy composition, anisotropy), or other parameters. SE can be a robust technique for measuring critical parameters of thin films or multiple film stacks, but SE is less sensitive in measuring strain.

[0009] Raman scattering is based on the inelastic scattering of incident light by optical phonons or molecular vibrations of a sample (e.g., a workpiece). It measures changes in frequency of outgoing light relative to the frequency of incoming or illuminating light, which is caused by the scattering. It can quantify material properties such as material species, chemical properties of a material, crystallinity, stress / strain, concentration, or other parameters. Raman peak position and full width at half maximum (FWHM) of Raman peaks can be used to estimate concentration and thickness. Also, changes or shifts in Raman scattering energy can reveal stress / strain of a sample.

[0010] Manufacturers are reaching physical limits as transistors are scaled down, so manufacturers are adding SiGe alloys, SiC, GaN, or other materials to increase strain of Si channels, which improves and allows selective control of mobility of carriers. Scaling and enhancing performance by introducing novel materials and structures will increase complexity. There is a need for fast and non-destructive in-line monitoring techniques that are capable of monitoring sub-micron spot size. The probe volume depends on spot size and penetration depth, which in turn depends on wavelength of the laser. This can be performed using high resolution multi-wavelength (MWL) Raman.

[0011] Transistor performance will be improved by reducing size, using novel device structures such as gate-all-around FETs (GAA FETs) or complementary FETs (CFETs), and using channel stress to perform electrostatic control. The current technology for monitoring GAA FET devices or films is based on multi-angle broadband SE combined reflectometry. SE can be useful in characterizing single-layer or multi-layer compositions with high material contrast. The measured spectrum is fitted to a theoretical optical model over a broadband wavelength range, from which film parameters such as n, k, and the thickness of a single layer or a composite multilayer stack can be determined. This fit can be complex for multilayer film stacks and may not be able to characterize defects, strain, and interface diffusion. Previous technologies are limited when measuring multilayer stacks with small material contrast and gradient layers. This situation may require an accurate model to describe the layer. Deeper layers may have higher uncertainties when measuring film parameters.

[0012] Other thickness and material measurement analysis techniques may be used, such as x-ray reflectometry (XRR), x-ray fluorescence (XRF), x-ray diffraction (XRD), scanning electron microscopy (SEM), or transmission electron microscopy (TEM). While these are reliable techniques and can measure thickness, stress, and composition, they are not suitable for in-line measurements because each measurement is slow and / or destructive.

[0013] TEM and other electron diffraction-based techniques can be used for strain and thickness measurement and can detect spots up to a few nm. These techniques are time-consuming, destructive and require sample preparation, which can affect strain properties. Non-destructive techniques (such as XRD or XRR) are slow and have large spot sizes of up to several mm, which are not suitable for online process monitoring. Another technique, secondary ion mass spectrometry (SIMS) can be used for depth profiling, but is inherently destructive, time-consuming and has a large spot size (e.g., approximately 100 μm). Optical SE can accurately measure the total thickness or individual thickness of a multilayer thin film stack. However, SE alone cannot determine the thickness of a multilayer stack and may require an accurate model to characterize the underlying film stack. In addition, SE or laser-driven spectroscopic reflectometer (LDSR) cannot provide strain in the stack.

[0014] Therefore, new techniques and systems are needed. Summary of the Invention

[0015] A method is provided in a first embodiment. The method includes measuring a thickness of a film stack of a workpiece and a composition of the film stack using x-ray techniques, resulting in a first measurement. The x-ray techniques are x-ray diffraction (XRD), x-ray reflectometry (XRR), or soft x-ray techniques. A thickness of the film stack and a composition of the film stack are measured using spectroscopic ellipsometry (SE) and / or spectroscopic reflectometry (SR), resulting in a second measurement. A thickness of the film stack and a composition of the film stack are also measured using multi-wavelength Raman spectroscopy, resulting in a third measurement. The second measurement and the third measurement are combined to form combined measured data. The combining includes regressing the second measurement and the third measurement. The thickness of the film stack and the composition of the film stack are determined using the combined measured data.

[0016] The method can include measuring a strain of the film stack using x-ray techniques.

[0017] The method can include adjusting an accuracy of SE and / or SR using a thickness and a composition of the film stack measured using x-ray techniques.

[0018] The method can include adjusting an accuracy of multi-wavelength Raman spectroscopy using a thickness and a composition of the film stack measured using x-ray techniques.

[0019] The combining can include combining the first measurement with the second measurement and the third measurement.

[0020] The combining and the determining can use a physical model. The combining and the determining can also use a machine learning algorithm.

[0021] The film stack can be a Si / SiGe film stack, a Si / SiC film stack, or a Si / GaN film stack.

[0022] The workpiece can include a GAA FET, a FinFET, a ForkFET, a CFET, or a 2D structure.

[0023] The second measurement and the third measurement can further include strain, stress, and / or defects.

[0024] The second measurement and the third measurement can be performed at least partially simultaneously. For example, the first measurement is performed at least partially simultaneously with the second measurement and the third measurement.

[0025] A non-transitory computer-readable storage medium is provided in a second embodiment. The non-transitory computer-readable storage medium includes one or more programs for execution on one or more computing devices. A first measurement is received that includes a thickness of a film stack of a workpiece and a composition of the film stack measured using x-ray techniques. The x-ray techniques are XRD, XRR, or soft x-ray techniques. A second measurement is received that includes the thickness of the film stack and the composition of the film stack measured using SE and / or SR. A third measurement is received that includes the thickness of the film stack and the composition of the film stack measured using multi-wavelength Raman spectroscopy. The second measurement and the third measurement are combined to form combined measured data, where the combining includes regressing the second measurement and the third measurement. The thickness of the film stack and the composition of the film stack are determined using the combined measured data.

[0026] The combining can further include combining the first measurement with the second measurement and the third measurement.

[0027] The combining and determining can use a physical model. The combining and the determining can also use a machine learning algorithm.

[0028] The second measurement and the third measurement can further include strain, stress, and / or defects.

[0029] A system is provided in a third embodiment. The system includes an x-ray measurement unit configured to measure a thickness of a film stack of a workpiece and a composition of the film stack on a stage, resulting in a first measurement. The x-ray measurement unit uses XRD, XRR, or soft x-ray techniques. An SE / SR measurement unit configured to measure the thickness of the film stack and the composition of the film stack, resulting in a second measurement. A multi-wavelength Raman spectroscopy unit configured to measure the thickness of the film stack and the composition of the film stack, resulting in a third measurement. A processor in electronic communication with the x-ray measurement unit, the SE / SR measurement unit, and the multi-wavelength Raman spectroscopy unit. The processor is configured to combine the second measurement with the third measurement to form combined measured data, and determine the thickness of the film stack and the composition of the film stack using the combined measured data. The combining includes regressing the second measurement and the third measurement.

[0030] The x-ray measurement unit can be further configured to measure strain of a film stack.

[0031] The processor can be further configured to use the thickness and composition of the film stack in the first measurement to adjust accuracy of the SE / SR measurement unit and / or the multi-wavelength Raman spectroscopy unit.

[0032] The processor can be further configured to combine the first measurement with the second measurement and the third measurement.

[0033] The combining and determining can use a physical model and / or a machine learning algorithm.

[0034] The second measurement and the third measurement can further include strain, stress, and / or defects.

[0035] The second measurement and the third measurement can be performed at least partially simultaneously. For example, the first measurement is performed at least partially simultaneously with the second measurement and the third measurement. BRIEF DESCRIPTION OF DRAWINGS

[0036] For a more complete understanding of the nature and scope of the disclosure, reference should be made to the following detailed description taken in conjunction with the accompanying drawing wherein:

[0037] Figure 1 is a graph showing an exemplary layout of Raman spectroscopy;

[0038] Figure 2 illustrates exemplary laser excitation probing different wavelengths up to different film stacks;

[0039] Figure 3 illustrates Figure 2 corresponding spectral forms;

[0040] Figure 4 is an exemplary measured spectrum x% as a function of a dispersion model 拉曼 and ε 拉曼 ≈ x% r and ε r and x% SE and t SE ≈ x% r and t r ;

[0041] Figure 5 is a graph showing an exemplary layout of spectroscopic ellipsometry;

[0042] Figure 6 is a flowchart of a method according to the present disclosure;

[0043] Figure 7 is another flowchart of a method according to the present disclosure; and

[0044] Figure 8 is an exemplary system according to the present disclosure. DETAILED DESCRIPTION

[0045] Although the claimed subject matter will be described in terms of certain embodiments, other embodiments (including embodiments that do not provide all of the benefits and features set forth herein) are also within the scope of the present disclosure. Various structural, logical, process steps, and electronic changes can be made without departing from the scope of the present disclosure. Accordingly, the scope of the present disclosure is defined only by the appended claims, along with the full scope of equivalents to which such claims are entitled.

[0046] Embodiments disclosed herein use a variety of non-destructive optical spectroscopy and metrology techniques, such as multi-wavelength (MWL) Raman, SE, spectroscopic reflectometry (SR), and / or angle resolved spectroreflectometry (i.e., beam profile reflectometry (BPR)). Embodiments disclosed herein include combining these techniques to characterize key parameters of unpatterned and patterned multilayer structures. This includes not only film applications, but also in-die device structures as well as scribe lane and device-like critical dimension (CD) structures in the device structures. These optical techniques can characterize film stacks in terms of composition and strain, as well as perform depth profiling of single and multilayer film stacks. SE and SR tend to have lower sensitivity to buried structures and can benefit from independent information (e.g., composition and strain) obtained from MWL to improve SE models.

[0047] Each technique can measure some or all of the film properties (e.g., composition, strain, thickness) individually, but multiple techniques can complement each other and can improve the accuracy and precision of the measured components when combined. Additionally, by using measured spectra to train a machine learning (ML) model through deep learning followed by neural network analysis can help measure film parameters efficiently.

[0048] To overcome the limitations of current techniques and improve the accuracy of the measurements, sample spectra can be compared to reference technique results and tested with reference technique results. Reference x-ray techniques (e.g., x-ray diffraction (XRD), x-ray reflectometry (XRR), or soft x-ray (SXR) techniques) can be used to measure the thickness, composition, and optionally strain of sample film stacks. The derived thickness, composition, and strain can be used in SE and Raman to improve their accuracy and precision. Although specifically disclosed with x-ray techniques, other techniques that provide relevant reference data can be used.

[0049] Raman spectroscopy is sensitive to the composition and stress of Raman-active materials. Figure 1is a typical layout of a Raman spectroscopy system 100, which includes multiple laser excitation sources (in light source 101), polarization assembly 102, analyzer rotation assembly 103, and objective 105, and laser line filter 106. Stress or strain of sample 104 (e.g., a workpiece, which can be a semiconductor wafer) can change well-defined Raman bands, enhance degeneracy, and shift band positions. This can change band shape and distort the symmetry of the band. Spectrometer 107 and detector 108 are used with controller 109 (which can include a processor) to determine measurements. Materials on or in sample 104 have different absorption coefficients at different wavelengths and can interact in the absorbing medium up to a certain depth. Shorter wavelengths can probe shallower layers, while changing to longer wavelengths can enable selective probing of certain layers, which is depicted in Figure 2 . This can result in different Raman profiles for different wavelengths, as shown in Figure 3 . The Raman signal from each excitation is from an interaction volume in the film layer and its profile has characteristic features of the layer. Depending on the wavelength of excitation, the Raman signal I R (1) has different peak positions and profiles, which can provide the composition (x) and strain (e) of the layer. Also, I R (l) can vary depending on material properties (such as refractive index (n), extinction coefficient (k), thickness, strain, or other parameters), and the response can be different for different wavelength excitations.

[0050] Similarly, SE and SR spectra can depend on the dispersion of the material over the wavelength range. Figure 4 is a typical SE spectrum, where the intensity varies depending on different film parameters. Broadband spectroscopic ellipsometry (BBSE) can encompass a single angle of incidence (AOI) (e.g., 65 degrees), or multiple AOIs in a range of 0 to 90 degrees. Ellipsometry encompasses all types of ellipsometry, such as rotating analyzer / polarizer ellipsometry, rotating compensator and compensator, or a combination of rotating analyzer / polarizer / compensator. BBSE signals include, but are not limited to, tanΨ, cosΔ, harmonics, Mueller matrix, or Stokes vector. SE and SR models can be optimized and tested based on reference t and x%, such that the model fits the measured spectra with the values of reference thickness and composition. A combination of BBSE and Raman techniques can be measured sequentially or simultaneously. For simultaneous measurement, BBSE can act as a modulation to the sample and can add additional sensitivity to the Raman signal. The experimental setup can have a combined modulation on and off, and the Raman response difference can be used to determine sample properties (such as strain, composition, and / or thickness of a multi-film stack).

[0051] Figure 5is a typical layout for an SE. The SE system 120 includes a light source 121 that directs a beam of light at the sample 104 through a polarizer 122. Light reflected from the sample 104 is received by an analyzer 123 and then an SE spectrometer 124. The distribution of light across the optical spectrum can be measured to show the interaction between the light and the sample 104.

[0052] Figure 6 An embodiment of the method 200 is shown. At 201, the thickness and / or composition of a film stack of a workpiece is measured using x-ray techniques, which results in a first measurement. The x-ray techniques can be x-ray diffraction (XRD), x-ray reflectometry (XRR), or soft x-ray techniques. The first measurement can be used as a reference. Optionally, the strain of the film stack can also be measured using x-ray techniques.

[0053] While x-ray techniques are described as resulting in a reference, other techniques can be used to result in a reference. For example, TEM, SEM, or XRF can be used to result in a reference. A user (e.g., a semiconductor manufacturer) can also provide reference information.

[0054] For example, the film stack can be a Si / SiGe film stack, a Si / SiC film stack, or a Si / GaN film stack. The workpiece can include, for example, a GAAFET, a FinFET, a ForkFET, a CFET, or a 2D structure. Thin films involving wafer bonding (e.g., Si / SiN) can also benefit from the embodiments disclosed herein. Other 2D FETs, 3D structures, patterned structures, or film stacks on a workpiece are possible and can be used with the method 200.

[0055] At 202, the thickness and / or composition of the film stack is measured using SE and / or SR, which results in a second measurement. The particular configuration for the measurement depends on the application requirements, for example, selected from an angle-resolved ellipsometer and reflectometer, single-wavelength, multi-wavelength, or spectroscopic parameters.

[0056] At 203, the thickness and / or composition of the film stack is measured using MWL Raman spectroscopy, which results in a third measurement. The particular configuration for the measurement depends on the application requirements.

[0057] Prior to steps 202 and 203, or for future measurements, the thickness or composition of the film stack in the first measurement can be used to adjust the accuracy of the SE and / or SR measurements and / or the MWL Raman spectroscopy measurements.

[0058] At 204, at least the second measurement and the third measurement are combined, which forms combined measured data. The combining can include regressing the second measurement and the third measurement. The combining can also include combining the first measurement with the second measurement and the third measurement. At 205, the combined measured data is used to determine the thickness of the film stack and / or the composition of the film stack. The combining and determining can use a physical model or a machine learning algorithm.

[0059] In an embodiment, the second and third measurements can further include strain, stress, and / or defects. In an example, the second and third measurements include strain, stress, and defects. These measurements can be used in later steps. The method 200 can also be used to determine strain, stress, and / or defects of the film stack. X-ray techniques can be used to generate corresponding reference information for strain, stress, composition, and thickness. TEM can also be used to generate reference information about defects.

[0060] In an example, the reference information includes for each of the types of measurements in the second and third measurements. In another example, if the model includes corresponding information, the reference information only includes some of the types of measurements in the second and third measurements.

[0061] In an example, the second and third measurements are performed at least partially simultaneously. The first measurement can be performed separately or at least partially simultaneously with the second and third measurements.

[0062] The number of second and third measurements used can depend on the sensitivity of the measurements.

[0063] Theoretical models for Raman scattering and SE can use a theory of light propagation and scattering that can be described by using a transfer matrix method. For Raman scattering as well as SE and SR, light propagation in the film stack varies as a function of the same film parameters and can be described using a combined model. In a combined and optimized physical model of SE and Raman techniques, the measured data can be further regressed and fed back and forth to obtain the combined film stack parameters. Figure 7 A process flow diagram showing the steps. Composition and strain (x% r , ε r ) from reference techniques like (XRD, XRR, SXR, etc.) are generally similar to composition and strain (x% 拉曼 , ε 拉曼 ) from Raman. There can be an offset and slope in optimizing the Raman model. Similarly, thickness and composition (x% SE , t SE ) from SE and reference (x% r , t r ) tend to be the same. Typically, these are set as reference values when optimizing the SE and Raman models. For example, x-ray techniques can be used to provide the reference values.

[0064] In one embodiment, the system can be configured to use the second measurement and the third measurement to determine one or more properties of the sample by combining the measurements. In an example, combining the second measurement and the third measurement includes using all measurements with appropriate relative weights as constraints in a non-linear regression. In another example, combining the second measurement and the third measurement includes using one wavelength range from one measurement technique to first determine one parameter (e.g., thickness), and then using another wavelength range from another measurement technique to determine another or other parameters (e.g., refractive index). Additionally, general algorithms can be used to combine results from multiple measurement subsystems. Many different algorithms can be used individually or in combination to extract results from the data. In one embodiment, one or more algorithms can be used to determine one or more properties. The one or more algorithms can include general algorithms, non-linear regression algorithms, comparison algorithms (e.g., comparison to a database (or library) or pre-computed or pre-measured results), or combinations thereof. Many such algorithms are known in the art, and a processor can use any of these algorithms to determine one or more properties. Examples of general algorithms are described in U.S. Patent Nos. 5,953,446 and 6,532,076, the entire contents of which are incorporated by reference. In one embodiment, the first and second data can include scatterometry data. In this embodiment, it can be particularly advantageous to use one or more algorithms to determine one or more properties of the sample.

[0065] The combining and regression actions can be repeated for one or more cycles. The combining and regression actions can be repeated until a convergence condition is met.

[0066] Machine learning can be used in embodiments of the processing algorithms to determine the thickness and / or composition of the film stack. Machine learning algorithms can be trained with both measured (e.g., ellipsometry and Raman spectroscopy) data and / or theoretical models. Theoretical models referred to here are optical and EM models that provide theoretical predictions of the measured signals / spectra based on defined geometry and materials of the sample and parameters characterizing the device optical and electronic hardware. Figure 7 The flowchart in FIG. 1 includes both physics-based computational models and machine learning algorithms (i.e., a combination of regression plus machine learning (ML) models, deep learning, and / or neural networks). Physics-based computational models use mathematical formulas to determine variables (e.g., thickness or composition). Measured and computed results using both techniques can be used to create a library and can further simulate and train machine learning algorithms. A processor can learn from different data and can help estimate small variations in film properties.

[0067] While other machine learning models are possible, in embodiments, a neural network or deep learning model is used for machine learning. Machine learning can be used with a theoretical model, or can be used as a standalone technique if sufficient reference data is provided. As Figure 7As shown in the middle, the machine learning algorithm can provide input to the processing algorithm.

[0068] The embodiments disclosed herein can use a combination of multiple techniques that utilize physics-based models (e.g., single-wavelength reflectometry and ellipsometry, angle- resolved reflectometry and ellipsometry, MWL Raman, SE, SR, and / or angle- resolved reflectance), and combined regression (which includes machine learning fed into the regression) can be used to solve for film parameters of superlattice structures (such as Si / SiGe, Si / SiC, or Si / GaN stacks), such as thickness, composition, strain, stress, defects, etc. For example, the defects can be lattice defects or another defect that affects the optical properties of the material.

[0069] The embodiments disclosed herein can use a wide tunable narrow linewidth to selectively probe different depths and increase the ability to perform depth profiling.

[0070] The embodiments disclosed herein can use the ability to directly measure both films and patterned structures on a workpiece, for example, for GAAFET, FinFET, ForkFET, CFET, or 2D material applications.

[0071] The embodiments disclosed herein are non-destructive and can monitor in-line semiconductor processes. In an example, each measurement can take from 1 second to 5 seconds.

[0072] Polarization dependent Raman can be sensitive to the anisotropy of films and device patterns. Adding SE and SR can increase accuracy because these measurement techniques are less sensitive to the anisotropy of films.

[0073] It will be understood that, while exemplary features of the method have been described, this arrangement should not be interpreted as limiting the disclosure to such features. The method can be implemented in software, firmware, hardware, or a combination thereof. In one mode, the method is implemented in software as an executable program, and is executed by one or more special or general purpose digital computers, such as a personal computer (PC; IBM compatible, Apple compatible, or other), personal digital assistant, quantum computer, workstation, minicomputer, or mainframe computer. Steps of the method can be implemented by a server or computer in which the software modules reside, or partially reside. The one or more computers can be part of a metrology tool or a standalone computer. The one or more computers can be online or offline. Processing requirements of the one or more computers can be based on tool throughput and solution time targets.

[0074] Generally, as will be appreciated in light of the disclosure, the computer will include a processor; a storage medium, such as volatile or non-volatile memory; and one or more input and / or output (I / O) devices (or interfaces to I / O devices) coupled via a local interface, which can be, for example but not limited to, one or more buses or other wired or wireless connections. The local interface can have additional elements, such as a controller, buffers (caches), drivers, repeaters, and receivers, to enable communications. Further, the local interface can include address, control, and / or data connections to enable appropriate communications among other computer components.

[0075] The processor (i.e., that of the control system) can be programmed to perform the functions of embodiments of the methods described herein. The processor is a hardware device that executes software, specifically software stored in memory. The processor can be any custom made or commercially available processor, a central processing unit (CPU), a co-processor, a semiconductor-based microprocessor (in the form of a microchip or chip set), a macroprocessor, or generally a device that processes software instructions.

[0076] The memory is associated with the processor and can include any one or combination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, and / or the like)) and nonvolatile memory elements (e.g., ROM, hard drive, tape, CD ROM, etc.). Moreover, the memory can incorporate electronic, magnetic, optical, and / or other types of storage media. The memory can have a distributed architecture, where various components are situated remote from one another, but can be accessed by the processor.

[0077] The software in the memory can include one or more separate programs. A separate program includes an ordered listing of executable instructions for implementing logical functions, to carry out embodiments of the module. In an example, the software in the memory includes one or more components of a method and can be executed on a suitable operating system (O / S).

[0078] The present disclosure can include programs that are provided as source programs, executable programs (object code), script files, or any other physical components that include sets of instructions that when executed by a computer, cause the computer to perform methods. In the case of source programs, the programs need to be translated via a compiler, assembler, interpreter, or the like, which can or can not be included within the memory, to generate O / S appropriate object code. Furthermore, the methodologies according to the teachings can be implemented in a distributed computing environment, where tasks are performed by local and remote processing

[0079] Additional embodiments relate to a non-transitory computer readable medium storing program instructions executable on a controller for performing a computer-implemented method for determining measurements of a workpiece as disclosed herein. In particular, the memory can contain a non-transitory computer readable medium including program instructions executable on a processor. The computer-implemented method can include any steps of any method described herein, including method 200. The steps can include receiving first measurements including thickness of a film stack of a workpiece measured using x-ray techniques (e.g., XRD, XRR, or soft x-ray techniques) and composition of the film stack, receiving second measurements including thickness of the film stack and composition of the film stack measured using SE and / or SR, receiving third measurements including thickness of the film stack and composition of the film stack measured using multi-wavelength Raman spectroscopy, combining the second and third measurements to form combined measured data, and determining thickness of the film stack and composition of the film stack using the combined measured data. The combining includes regressing the second and third measurements. A physical model and / or a machine learning algorithm can be used for the combining. The first measurements can also be combined with the second and third measurements. The second and third measurements can include strain, stress, and / or defects.

[0080] The system can be used to perform measurements and determine thickness, composition, or other information about a film stack of a workpiece. The system can include an x-ray measurement unit, an SE / SR measurement unit, and a multi-wavelength Raman spectroscopy unit. The system can include multiple independent measurement systems or can be a cluster tool with multiple measurement systems. The x-ray measurement unit is configured to measure thickness, composition, and / or strain of a film stack of a workpiece on a stage, resulting in first measurements. The x-ray measurement unit uses XRD, XRR, or soft x-ray techniques. The SE / SR measurement unit is configured to measure thickness or composition of the film stack, resulting in second measurements. The multi-wavelength Raman spectroscopy unit is configured to measure thickness or composition of the film stack, resulting in third measurements. Strain, stress, and / or defects can also be measured as part of the measurements.

[0081] The processor is in electronic communication with the x-ray measurement unit, the SE / SR measurement unit, and the multi-wavelength Raman spectroscopy unit. The processor is configured to combine the second and third measurements to form combined measured data, and then determine thickness of the film stack and / or composition of the film stack using the combined measured data. The combining includes regressing the second and third measurements. A physical model and / or a machine learning algorithm can be used for the combining. The first measurements can optionally be combined with the second and third measurements to determine thickness and / or composition.

[0082] Figure 8An example of a system 300 is shown. The system 300 includes an x-ray measurement unit 301 that measures the sample 104, an SE / SR measurement unit 302, and a multi- wavelength Raman spectroscopy unit 303. The x-ray measurement unit 301, the SE / SR measurement unit 302, and the multi-wavelength Raman spectroscopy unit 303 are in electronic communication with a processor 304.

[0083] In an example, the processor is configured to use the thickness and composition of the film stack in the first measurement to adjust the accuracy of the SE / SR measurement unit and / or the multi-wavelength Raman spectroscopy unit. For example, the processor can send instructions to adjust optical components or change measurement techniques to improve accuracy.

[0084] The second and third measurements can be performed at least partially simultaneously on the workpiece. The first measurement can also be performed at least partially simultaneously with the second and third measurements. Of course, the first, second, and third measurements can also be performed sequentially. Although described as first, second, and third, the measurements can be performed in a different order.

[0085] Each of the steps of the method can be performed as described herein. The method can also include any other steps that can be performed by the processors and / or computer subsystems or systems described herein. The steps can be performed by one or more computer systems, which can be configured according to any of the embodiments described herein. In addition, the methods described above can be performed by any of the system embodiments described herein.

[0086] While the present disclosure has been described with respect to one or more particular embodiments, it is to be understood that other embodiments of the present disclosure can be made without departing from the scope of the present disclosure. Therefore, the present disclosure is to be considered only as illustrative of the principles of the disclosure.

Claims

1. A method comprising: measuring the thickness of a film stack of the workpiece and the composition of the film stack using spectroscopic ellipsometry (SE), spectroscopic reflectometry (SR), and / or angle resolved reflectometry (ARR), thereby producing a first optical measurement; measuring the thickness of a film stack and the composition of the film stack using multi-wavelength Raman spectroscopy, thereby producing a second optical measurement; combining the first optical measurement and the second optical measurement to form combined measured data, wherein the combining comprises regressing the first optical measurement and the second optical measurement; and The combined measured data is used to determine the thickness of the film stack and the composition of the film stack. The method of claim 1 , further comprising measuring the strain of the film stack. 3 . The method of claim 1 , wherein the combining further comprises combining a reference measurement with the first optical measurement and the second optical measurement. The method of claim 1 , wherein the combining and the determining use a physical model. The method of claim 1 , wherein the combining and the determining use a machine learning algorithm. The method according to claim 1 , wherein the film stack is a Si / SiGe film stack, a Si / SiC film stack, or a Si / GaN film stack.

7. The method of claim 1, wherein the film stack is used to fabricate one of a GAA FET, a FinFET, a ForkFET, a CFET, or a 2D structure.

8. The method of claim 1, wherein the first optical measurement and the second optical measurement further comprise strain, stress, and / or defects.

9. The method of claim 1, wherein the first optical measurement and the second optical measurement are performed at least partially simultaneously or sequentially.

10. The method of claim 1 , further comprising measuring the thickness of the film stack and the composition of the film stack using an x-ray technique, thereby producing a reference measurement, wherein the x-ray technique is x-ray diffraction (XRD), x-ray reflectometry (XRR), or a soft x-ray technique.

11. The method of claim 3, wherein the reference measurement uses an x-ray technique, wherein the x-ray technique is XRD, XRR, or a soft x-ray technique.

12. The method of claim 1, wherein the thickness of the film stack comprises the thickness of one or more layers in the film stack.

13. The method of claim 5, wherein the machine learning algorithm is trained on theoretical models and / or measured data.

14. A non-transitory computer-readable storage medium comprising one or more programs for performing the following steps on one or more computing devices: receiving a first optical measurement of a thickness of a film stack of a workpiece and a composition of the film stack including measurements using SE, SR, and / or ARR; receiving a second optical measurement comprising the thickness of the film stack and the composition of the film stack measured using multi-wavelength Raman spectroscopy; combining the first optical measurement and the second optical measurement to form combined measured data, wherein the combining comprises regressing the first optical measurement and the second optical measurement; and The combined measured data is used to determine the thickness of the film stack and the composition of the film stack.

15. The non-transitory computer-readable storage medium of claim 14, wherein the combining further comprises combining a reference measurement with the first optical measurement and the second optical measurement.

16. The non-transitory computer-readable storage medium of claim 14, wherein the combining and the determining use a physical model.

17. The non-transitory computer-readable storage medium of claim 14, wherein the combining and the determining use a machine learning algorithm.

18. The non-transitory computer-readable storage medium of claim 14, wherein the first optical measurement and the second optical measurement further comprise strain, stress, and / or defects.

19. The non-transitory computer-readable storage medium of claim 14, further comprising receiving reference measurements comprising the thickness of the film stack and the composition of the film stack measured using an x-ray technique, wherein the x-ray technique is XRD, XRR, or a soft x-ray technique.

20. The non-transitory computer-readable storage medium of claim 15, wherein the reference measurement uses an x-ray technique, wherein the x-ray technique is XRD, XRR, or a soft x-ray technique.

21. The non-transitory computer-readable storage medium of claim 14, wherein the thickness of the film stack comprises a thickness of one or more layers in the film stack.

22. The non-transitory computer-readable storage medium of claim 17, wherein the machine learning algorithm is trained on theoretical models and / or measured data.

23. A system comprising: a measurement unit configured to measure a thickness of a film stack of a workpiece and a composition of the film stack using SE, SR, and / or ARR, thereby generating a first optical measurement; a multi-wavelength Raman spectroscopy unit configured to measure the thickness of the film stack and the composition of the film stack, thereby producing a second optical measurement; and a processor in electronic communication with the measurement unit and the multi-wavelength Raman spectroscopy unit, wherein the processor is configured to: combining the first optical measurement and the second optical measurement to form combined measured data, wherein the combining comprises regressing the first optical measurement and the second optical measurement; and The thickness of the film stack and the composition of the film stack are determined using the combined measured data.

24. The system of claim 23, wherein the processor is further configured to combine a reference measurement with the first optical measurement and the second optical measurement.

25. The system of claim 23, wherein the combining and the determining use physical models and / or machine learning algorithms.

26. The system of claim 23, wherein the first optical measurement and the second optical measurement further comprise strain, stress, and / or defects.

27. The system of claim 23, wherein the first optical measurement and the second optical measurement are performed at least partially simultaneously or sequentially.

28. The system of claim 23, wherein the thickness of the film stack comprises a thickness of one or more layers in the film stack.

29. The system of claim 24, wherein the reference measurement uses an x-ray technique, wherein the x-ray technique is XRD, XRR, or a soft x-ray technique.

30. The system of claim 25, wherein the machine learning algorithm is trained on theoretical models and / or measured data.

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