Non-destructive three-dimensional detection and characterization of samples

Abstract

A system for non-destructive characterization of a sample is disclosed herein. The system includes an electron beam (e-beam) source for projecting an electron beam onto a sample with one or more electron beam landing energies; an X-ray detector for sensing X-rays emitted from the sample, thereby obtaining measurement data; and processing circuitry. The processing circuitry is configured to: (i) extract key features specified by a vector # imgabs0 # from the measurement data; and (ii) determining values # imgabs3 # imgabs4 # of one or more structural parameters characterizing the sample based on # imgabs1 # and a set # imgabs2 # of vectors simulating the key features, each of # imgabs3 # imgabs4 # being a product of a computer simulation of emitting X-rays from a respective simulation sample as a result of incidence with an electron beam on the respective simulation sample at each of one or more landing energies.

Description

Technical Field

[0001] The present disclosure generally relates to non-destructive three-dimensional probing and characterization of samples based on X-ray measurements and computer simulations. Background Art

[0002] "Three-dimensional" structures are increasingly being used in the semiconductor industry, particularly for the manufacture of logic and memory components. Therefore, as part of quality control, it is often necessary to obtain "three-dimensional" data of the structure within a sample. Currently, most techniques for dissecting samples that include three-dimensional internal structures are destructive and may involve extracting a thin slice or chipping off a section from the sample and subsequently inspecting the thin slice or section using, for example, transmission electron microscopy (TEM). The challenge remains to develop non-destructive techniques for dissecting samples incorporating three-dimensional internal structures that allow high volume manufacturing (HVM). Summary of the invention

[0003] According to some embodiments of the present disclosure, aspects of the present disclosure relate to non-destructive three-dimensional detection and characterization of samples based on X-ray measurements and computer simulations. More specifically, but not exclusively, according to some embodiments of the present disclosure, aspects of the present disclosure relate to non-destructive three-dimensional detection and characterization of samples based on characteristic X-ray measurements and modeling (by extrapolation from computer simulations), which does not require ground truth data.

[0004] Therefore, according to an aspect of some embodiments, a system for non-destructive characterization of a sample is provided. The system includes: an electron beam (e-beam) source, the electron beam source being used to project an electron beam at one or more electron beam landing energies onto a sample being tested; an X-ray detector, the X-ray detector being used to sense X-rays emitted from the sample being tested (i.e., the sample being tested); and a processing circuit system. The processing circuit system is configured to: (i) receive X-ray measurement data related to one or more electron beam landing energies from the X-ray detector; (ii) extract a vector of values ​​specifying key features of the X-ray measurement data from the X-ray measurement data; and (iii) extract a vector of values ​​specifying key features of the X-ray measurement data from the X-ray measurement data. and (iii) based on and a set of vectors that simulate key features To determine the value assumed by one or more structural parameters related to the structure under test For each 1≤n≤N, specifies the values ​​assumed by one or more structural parameters associated with the nth simulation sample, and is the product of a computer simulation of the emission of X-rays from the nth simulated sample due to incident upon the nth simulated sample with an electron beam at each of one or more landing energies.

[0005] According to some embodiments of the system, the electron beam source and the X-ray detector form part of a scanning electron microscope.

[0006] According to some embodiments of the system, Minimize the loss function, which depends at least on and key features from Extrapolated vector-valued function According to some such embodiments, in addition to depending on and In addition to the first term of , the loss function also includes at least one regularization term, which is function.

[0007] According to some embodiments of the system, the processing circuit system is configured to execute an optimization algorithm to minimize the loss function and thereby determine

[0008] According to some embodiments of the system, the processing circuit system is configured to calculate and The minimum (mathematical) distance between The key feature is Extrapolated vector-valued function.

[0009] According to some embodiments of the system, the processing circuit system is configured to calculate and The (mathematical) distance between

[0010] According to some embodiments of the system, for each 1≤n≤N, Specifies nominal values ​​for one or more structural parameters. For a K-dimensional vector space defined by one or more structural parameters A hypervolume centered at K is sampled, where K is the number of one or more structural parameters. The size and boundaries of the hypervolume are selected so as to encompass the expected variations in the one or more structural parameters.

[0011] According to some embodiments of the system, the one or more structural parameters include any geometrical and / or compositional parameters characterizing a sample, modification of which affects at least some of the values ​​of the key features.

[0012] According to some embodiments of the system, the one or more structural parameters include one or more of the following: the total concentration of at least one material (i.e., substance) included in the tested sample; and optionally, when the tested sample includes a structure embedded in or on the tested sample, the width of the embedded structure.

[0013] According to some embodiments of the system, the sample under test comprises a plurality of layers.

[0014] According to some embodiments of the system, the one or more structural parameters include one or more of the following: (i) at least one thickness of at least one of these layers; (ii) the combined thickness of at least two or more of these layers; (iii) at least one mass density of at least one of these layers; and (vi) at least one relative concentration of at least one material in one or more of these layers.

[0015] According to some embodiments of the system, one or more electron beam landing energies cause the emission of X-rays regarding one or more characteristic X-ray lines, and the one or more characteristic X-ray lines are respectively related to one or more target substances (materials) included in the tested sample.

[0016] According to some embodiments of the system, the one or more electron beams land with an energy such that emission of X-rays originating from each of at least two of the plurality of layers is induced.

[0017] According to some embodiments of the system, the X-ray detector is configured to respectively sense at least one measured spectrum of respectively emitted X-rays within at least one photon energy range, the at least one photon energy range including at least one of the characteristic X-ray lines. The X-ray measurement data includes the measured spectrum.

[0018] According to some embodiments of the system, the key feature is or comprises, or is a function of, the intensity of the characteristic X-ray line and / or the intensity of the background radiation.

[0019] According to some embodiments of the system, the key feature is or comprises the intensity of the characteristic X-ray lines, each characteristic X-ray line normalized by a mean value of the background intensity with respect to the corresponding characteristic X-ray line.

[0020] According to some embodiments of the system, The double vertical bars indicate the vector norm.

[0021] According to some embodiments of the system, is one or more regularization terms. The double vertical bars indicate the vector norm.

[0022] According to some embodiments of the system, in Specify nominal values ​​for one or more structural parameters, Specify the deviation from the nominal value, is the key feature and is a vector of corresponding values, and A is a matrix.

[0023] According to some embodiments of the system, is the product of a computer simulation of the emission of X-rays from a simulated sample due to an electron beam incident on the simulated sample at each of one or more landing energies, the simulated sample being Characterization. The matrix A is equal to where the double vertical bars indicate the matrix norm, and for every 1≤n≤N,

[0024] According to some embodiments of the system, obtaining and the matrix A as where the double vertical bars indicate the matrix norm, and for every 1≤n≤N,

[0025] According to some embodiments of the system, the processing circuit system is configured to determine part of right Apply the k-nearest neighbor (k-NN) regression algorithm to determine the closest k

[0026] According to some embodiments of the system, is closest to k (ie, closest to k ) corresponding to The average, optionally weighted average, or median of the values.

[0027] According to some embodiments of the system, the processing circuit system is configured to calculate and The minimum (mathematical) distance between and the processing circuit system is further configured to The set of N′>N vectors with respect to the key features is subjected to a (k=N)-NN classifier to obtain The set of N′>N vectors of key features includes And the other N′-N vectors of key features are obtained by applying computer simulation to N′-N additional simulation samples.

[0028] According to some embodiments of the system, in order to derive the intensity of the characteristic X-ray line, the processing circuit system is configured to fit a free curve to each interval of the measured spectrum, each interval being approximately (i.e., substantially) centered on the corresponding characteristic X-ray line and consisting of a nearby portion of the characteristic X-ray line, thereby obtaining a corresponding optimized curve.

[0029] According to some embodiments of the system, the free curve is proportional to a convex shape function, or wherein the free curve is a sum of functions including a convex shape function. The processing circuit system is configured to fit a convex shape function to a peak around a characteristic X-ray line of a corresponding measured spectrum as part of fitting the free curve.

[0030] According to some embodiments of the system, the sum of functions comprises a second function (ie, other than the convex function), the second function being a polynomial. In fitting the freeform curve, the processing circuitry fits the second function to account for a background intensity component of the corresponding measured spectrum.

[0031] According to some embodiments of the system, the processing circuitry is configured to derive, by computer simulation, the emission of X-rays from the corresponding simulated sample as a result of impinging the corresponding simulated sample with the electron beam at each of the one or more landing energies.

[0032] According to some embodiments of the system, the processing circuit system is further configured to: Extrapolation

[0033] According to some embodiments of the system, the X-ray detector is an energy dispersive X-ray spectrometer or a wavelength dispersive X-ray spectrometer.

[0034] According to some embodiments of the system, optionally, the sample being tested is a patterned wafer or a part of a patterned wafer in one of the manufacturing stages of the patterned wafer.

[0035] According to an aspect of some embodiments, a method for non-destructive characterization of a sample is provided. The method includes a measurement operation and a measurement data analysis operation. The measurement operation includes, for each of one or more landing energies, a sub-operation of projecting an electron beam onto a sample under test, and obtaining (X-ray) measurement data by measuring the intensity of X-rays emitted from the sample under test due to the electron beam penetrating the sample under test. The measurement data analysis operation includes a sub-operation of extracting a vector of values ​​of a specified key feature from the measurement data. And based on and a set of vectors that simulate key features To determine the value assumed by one or more structural parameters related to the structure under test For each 1≤n≤N, specifies the values ​​assumed by one or more structural parameters associated with the nth simulation sample, and Each of is obtained by computer simulation of the emission of X-rays from the nth simulated sample due to the incidence of the nth simulated sample with an electron beam at each of the one or more landing energies.

[0036] According to some embodiments of the method, Minimize the loss function, which is at least and key features from Extrapolated vector-valued function According to some such embodiments, in addition to depending on and In addition to the first term of , the loss function also includes one or more regularization terms.

[0037] According to some embodiments of the method, the loss function is minimized by executing an optimization algorithm configured to minimize the loss function.

[0038] According to some embodiments of the method, by calculating and The minimum (mathematical) distance between The key feature is Extrapolated vector-valued function.

[0039] According to some embodiments of the method, by calculating and The (mathematical) distance between

[0040] According to some embodiments of the method, for each 1≤n≤N, Specify nominal values ​​for one or more structural parameters. To deal with the K-dimensional vector space defined by one or more structural parameters A hypervolume centered at K is sampled, where K is the number of one or more structural parameters. The size and boundaries of the hypervolume are selected so as to encompass the expected variations in the one or more structural parameters.

[0041] According to some embodiments of the method, the one or more structural parameters include any geometrical and / or compositional parameters characterizing the sample, the modification of which affects at least some of the values ​​of the key features.

[0042] According to some embodiments of the method, the one or more structural parameters include one or more of the following: the total concentration of at least one material (i.e., substance) included in the tested sample; and optionally, when the tested sample includes a structure embedded in or on the tested sample, the width of the embedded structure.

[0043] According to some embodiments of the method, the sample under test comprises a plurality of layers.

[0044] According to some embodiments of the method, the one or more structural parameters include one or more of the following: (i) at least one thickness of at least one of these layers; (ii) the combined thickness of at least two or more of these layers; (iii) at least one mass density of at least one of these layers; and (vi) at least one relative concentration of at least one material in one or more of these layers.

[0045] According to some embodiments of the method, one or more landing energies are selected so as to cause the emission of X-rays regarding one or more characteristic X-ray lines, which are respectively related to one or more target substances (materials) included in the tested sample.

[0046] According to some embodiments of the method, one or more landing energies are selected so as to cause emission of X-rays from each of at least two of the plurality of slices.

[0047] According to some embodiments of the method, in each implementation of the sub-operations of projecting an electron beam and obtaining measurement data, obtained is at least one measured spectrum of respectively emitted X-rays in at least one photon energy range, the at least one photon energy range including at least one of the characteristic X-ray lines. The measurement data includes the measured spectrum.

[0048] According to some embodiments of the method, the key feature is or comprises or is obtained from the intensity of the characteristic X-ray line and / or the intensity of the background radiation.

[0049] According to some embodiments of the method, the key feature is or comprises the intensity of the characteristic X-ray lines, each characteristic X-ray line being normalized by a mean value of the background intensity with respect to the corresponding characteristic X-ray line.

[0050] According to some embodiments of the method, The double vertical bars indicate the vector norm.

[0051] According to some embodiments of the method, is one or more regularization terms. The double vertical bars indicate the vector norm.

[0052] According to some embodiments of the method, in Specify nominal values ​​for one or more structural parameters, Specify the deviation from the nominal value, is the key feature and is a vector of corresponding values, and A is a matrix.

[0053] According to some embodiments of the method, is obtained by computer simulation of the emission of X-rays from a simulated sample due to incident electron beams on the simulated sample at each of one or more landing energies, the simulated sample being Characterization. The matrix A is equal to where the double vertical bars indicate the matrix norm, and for every 1≤n≤N,

[0054] According to some embodiments of the method, obtaining and the matrix A as where the double vertical bars indicate the matrix norm, and for every 1≤n≤N,

[0055] According to some embodiments of the method, when determining In the sub-operation of right Apply the k-nearest neighbor (k-NN) regression algorithm to determine the closest k

[0056] According to some embodiments of the method, is calculated by (ie, closest to k ) corresponding to The average, optionally weighted average, or median of the data.

[0057] According to some embodiments of the method, the measurement data analysis operation further comprises determining Previous sub-operation: By using The set of N′>N vectors with respect to the key features is subjected to a (k=N)-NN classifier to obtain The set of N′>N vectors of key features includes And the other N′-N vectors of key features are obtained by applying computer simulation to N′-N additional simulation samples.

[0058] According to some embodiments of the method, in order to derive the intensity of the characteristic X-ray line, a free curve is fitted to each interval of the measured spectrum, each interval being approximately (i.e., substantially) centered on the corresponding characteristic X-ray line and consisting of the nearby portion of the characteristic X-ray line, thereby obtaining a corresponding optimized curve.

[0059] According to some embodiments of the method, the free curve is proportional to a convex shape function, or wherein the free curve is a sum of functions including a convex shape function. In fitting the free curve, a convex shape function is fitted to a peak around a characteristic X-ray line of the corresponding measured spectrum.

[0060] According to some embodiments of the method, the sum of functions comprises a second function (ie in addition to the convex shape function), the second function being a polynomial. In fitting the free curve, the second function is fitted so as to take into account a background intensity component of the corresponding measured spectrum.

[0061] According to some embodiments of the method, the method further comprises an initial operation of deriving, by computer simulation, an X-ray emission from a corresponding simulated sample due to incident electron beams on the corresponding simulated sample at each of the at least one landing energy.

[0062] According to some embodiments of the method, wherein by calculating and The minimum (mathematical) distance between The method further comprises, after the initial operation, Extrapolation

[0063] According to some embodiments of the method, optionally, the sample under test is the patterned wafer or a part of the patterned wafer in one of the manufacturing stages of the patterned wafer.

[0064] According to some embodiments of the method, the measuring operation is performed using a scanning electron microscope.

[0065] According to an aspect of some embodiments, a non-transitory computer-readable storage medium is provided, wherein the storage medium stores instructions that cause a system for non-destructive characterization of a sample, such as the above system, to implement the above method with respect to a (tested) sample.

[0066] According to an aspect of some embodiments, a non-transitory computer-readable storage medium is provided. The storage medium stores instructions that cause one or more processors to: (i) extract a vector of values ​​of a specified key feature from X-ray measurement data related to a sample under test; and (ii) based on and a set of vectors simulating key features To determine the value assumed by one or more structural parameters related to the structure under test For each 1≤n≤N, specifies the values ​​assumed by one or more structural parameters associated with the nth simulation sample, and Each of is a product of a computer simulation of the emission of X-rays from the nth simulated sample due to incident on the nth simulated sample with an electron beam at each of one or more landing energies. For each of the one or more landing energies, X-ray measurement data is obtained by projecting an electron beam onto the tested sample and by measuring the intensity of X-rays emitted from the tested sample due to penetration of the electron beam into the tested sample.

[0067] Certain embodiments of the present disclosure may include some, all, or none of the above advantages. One or more other technical advantages will be apparent to those skilled in the art from the drawings, descriptions, and claims included herein. In addition, although specific advantages have been listed above, various embodiments may include all, some, or none of the listed advantages.

[0068] Unless otherwise defined, all technical and / or scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure belongs. In the event of a conflict, the patent specification including definitions shall prevail. As used herein, the indefinite articles "a" and "an" mean "at least one" or "one or more", unless the context clearly indicates otherwise.

[0069] Unless otherwise specifically noted, as will be apparent from the present disclosure, it should be understood that, according to some embodiments, terms such as "process," "compute," "calculate," "determine," "estimate," "evaluate," "measure," and the like may refer to the actions and / or processing of a computer or computing system or similar electronic computing device that manipulates and / or transforms data represented as physical (e.g., electronic) quantities within registers and / or memories of the computing system into other data similarly represented as physical quantities within the computing system's memories, registers, or other such information storage, transmission, or display devices.

[0070] Embodiments of the present disclosure may include devices for performing the operations described herein. These devices may be specifically constructed for the desired purpose, or may include one or more general-purpose computers selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer-readable storage medium, such as, but not limited to, any type of disk, including a floppy disk, an optical disk, a CD-ROM, a magneto-optical disk, a read-only memory (ROM), a random access memory (RAM), an electrically programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic card or an optical card, a flash memory, a solid-state drive (SSD), or any other type of medium suitable for storing electronic instructions and capable of being coupled to a computer system bus.

[0071] The processes and displays presented herein are not inherently related to any particular computer or other device. Various general purpose systems can be used together with programs according to the teachings herein, or it may be convenient to construct more specialized devices to perform one or more methods of expectation. The following description illustrates one or more structures of various these system expectations. In addition, the embodiments of the present disclosure are not described with reference to any particular programming language. It should be understood that various programming languages ​​can be used to implement the teachings of the present disclosure described herein.

[0072] Aspects of the present disclosure may be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. In general, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The disclosed embodiments may also be practiced in a distributed computing environment in which tasks are performed by remote processing devices linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including memory storage devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Some embodiments of the present disclosure are described herein with reference to the accompanying drawings. The description together with the accompanying drawings makes it clear to one of ordinary skill in the art how to practice some embodiments. The accompanying drawings are used for illustrative purposes and are not intended to show the structural details of the embodiments in more detail than is necessary for a basic understanding of the present disclosure. For clarity, some objects shown in the drawings are not drawn to scale. In addition, two different objects in the same figure may be drawn at different scales. In particular, the scale of some objects may be greatly enlarged compared to other objects in the same figure.

[0074] In the attached picture:

[0075] Figure 1A flow chart of a method for non-destructive three-dimensional probing and characterization of a sample based on X-ray measurements and computer simulations according to some embodiments is presented;

[0076] FIG. 2A to FIG. 2D Schematically depicts a method according to some embodiments Figure 1 The method of deeply probing a sample as part of characterization of the sample;

[0077] Figure 3 Presented based on Figure 1 Flow charts of the measurement data analysis operations of some specific embodiments of the measurement data analysis operations of the method;

[0078] Figure 4A Presented based on Figure 1 Some embodiments of the measurement operation of the method of obtaining an X-ray emission spectrum of the sample by performing the measurement operation;

[0079] Figure 4B Presented based on Figure 1 A specific embodiment of the method of measuring data analysis operation is fitted to Figure 4A Optimization curve on the X-ray emission spectrum;

[0080] Figure 4C Presents a superposition Figure 4A The X-ray emission spectrum of Figure 4B The optimization curve of

[0081] Figure 4D Presented are some embodiments of the Figure 4B The fitting Gaussian function in the optimization curve of ;

[0082] Figure 4E Presented are some embodiments of the Figure 4B a fitting polynomial in the optimization curve of , wherein the fitting polynomial takes into account bremsstrahlung; and

[0083] Figure 5 Schematically depicted is a flow chart of a system for non-destructive three-dimensional probing and characterization of a sample based on X-ray measurements and computer simulations, according to some embodiments. DETAILED DESCRIPTION

[0084] The principles, uses and implementations of the teachings herein may be better understood with reference to the attached description and drawings. After careful reading of the description and drawings herein, those skilled in the art will be able to implement the teachings herein without undue effort or experimentation. In the drawings, the same reference numerals always represent the same parts.

[0085] According to some embodiments of the present application, the present application relates to methods and systems for non-destructive three-dimensional detection and characterization of samples (e.g., semiconductor samples). According to some embodiments, an electron beam at each of a plurality of (electron beam) landing energies is projected onto a sample to be deeply profiled. Each electron beam penetrates the sample and stimulates the emission of characteristic X-rays from the sample (and the accompanying bremsstrahlung, i.e., background radiation). The greater the electron beam landing energy, the greater the depth of penetration of the electron beam into the sample.

[0086] The spectrum of the emitted X-rays depends on the internal geometry of the sample and the material composition of the sample, in particular the distribution of each material (i.e., substance) that makes up the sample. As the electron beam passes through the sample, it "probes" the different regions through which the electron beam passes. The contribution of each region passed through to the spectrum of the emitted X-rays depends not only on the concentration of each material included in the region passed through, but also on the energy of the electron beam entering the region, which in turn decreases with depth.

[0087] The present application teaches how to characterize the internal structural features of a sample based on the values ​​of key features extracted from the measured spectrum of the sample. The measured spectrum can be analyzed using computer simulations based on the sample and optimization tools for the measurement settings used to obtain the measured spectrum. Advantageously, the disclosed methods and systems do not require the acquisition of expensive and time-consuming benchmark real data. In particular, the time between the design of semiconductor devices and the testing and mass production of semiconductor devices may therefore be reduced.

[0088] As used herein, "e-beam" stands for "electron beam." The term "characteristic X-ray limit" refers to the photon energy range within the X-ray spectrum (ie, the energy range or equivalently the frequency range of photons) that includes the characteristic X-ray line.

[0089] As used herein, the terms "bremsstrahlung" and "background radiation" are interchangeable.

[0090] method

[0091] According to an aspect of some embodiments, a computerized method for non-destructive (three-dimensional) characterization of a sample (eg, a semiconductor structure) based on characteristic X-ray measurements and computer simulations is provided. Figure 1 A flow chart of such a method (method 100) according to some embodiments is presented. Method 100 includes:

[0092] - a measuring operation 110, which comprises, for each of the electron beam landing energy(s) (ie, the landing energy of the electron beam), performing:

[0093] ■ Sub-operation 110a, in which an electron beam is projected onto a sample (also referred to as a "tested sample").

[0094] ■ Sub-operation 110b, in which the X emitted from the sample under test due to the penetration of the electron beam is measured.

[0095] The intensity of the rays is used to obtain measurement data.

[0096] - Data analysis operation 120, the data analysis operation 120 includes:

[0097] ■ Sub-operation 120a, in which the vector Designated key characteristics.

[0098] ■ Sub-operation 120b, in which based on and a set of vectors that simulate key features To determine the value assumed by one or more structural parameters associated with the sample being tested is obtained by computer simulation of the emission of X-rays from N simulated samples respectively due to the incidence of electron beams on the N simulated samples at each of the (electron beam) landing energies.

[0099] A system such as the one described below can be used Figure 5 The method 100 may be implemented by using a system described in the description of ) or a system similar to the system. The term "extrapolation" is used in an expanded manner herein and generally refers to deriving a continuous function from multiple data points.

[0100] According to some embodiments, and as follows in Figure 3 As described in detail in the description, make The loss function and key features from Extrapolated vector-valued function According to some embodiments, except for and In addition to the first term of the two, the loss function may also include one or more regularization terms, for example to stabilize the solution or as one or more constraints reflecting some prior knowledge about one or more structural parameters. According to some embodiments, the first term is related to and According to some embodiments, standard local and / or global optimization tools can be used to minimize the loss function and thereby determine Alternatively, according to some embodiments in which the optimization problem defined by minimization over a loss function admits of a known analytical solution, it is possible to compute directly from the analytical solution (the function defining the analytical solution)

[0101] According to some embodiments, and as expanded upon below, by calculating from arrive The distance to determine

[0102] According to some embodiments, the sample includes two or more layers. According to some such embodiments, one or more structural parameters characterize each of at least some of the two or more layers. According to some embodiments, optionally, in one of the manufacturing stages of the patterned wafer, the tested sample is a patterned wafer, a portion of a patterned wafer, or a semiconductor device embedded in or on a patterned wafer. According to some embodiments, the tested sample is or includes a structure comprising one or more semiconductor materials. According to some embodiments, the structure may be constructed as part of a manufacturing process of a semiconductor device and / or a component of a semiconductor device. According to some embodiments, the structure may be an auxiliary structure, and the auxiliary structure may be constructed as part of a manufacturing process of a semiconductor device and / or a component of a semiconductor device. According to some embodiments, optionally, in one of the manufacturing stages of the tested sample, the tested sample may be or include one or more logic components (e.g., fin field effect transistors (FinFETs) and / or all-around gate (GAA) FETs) and / or memory components (e.g., dynamic RAM and / or vertical NAND (V-NAND)).

[0103] According to some embodiments, the N simulated samples may be selected to reflect expected variations in one or more structural parameters between samples (of the same intended design) (e.g., due to manufacturing defects). In particular, To define K in a p In the vector space The hypervolume centered at is “sampled”. Nominal values ​​of one or more structural parameters may be specified. p is the number of structural parameters. The size and boundaries of the hypervolume may be selected so as to encompass the expected variation of one or more structural parameters. According to some embodiments, the number of simulation samples N is greater than 2K p (or at least strictly greater than K p As a non-limiting example, according to some embodiments, one may select The deviation from the nominal value In terms of Includes 2K p A set of vectors For each vector in . For each 1≤k≤K p , Approximately equal to σ k is the expected standard deviation of the kth structural parameter, and It refers to thep K is limited by structural parameters p The unit vector of the k-th axis of the dimensional vector space.

[0104] Parameters characterizing the electron beam, in particular the electron beam landing energy, are selected so as to cause the emission of characteristic X-rays in sub-operation 110a by particles (specifically, particles of at least one target substance) in a detection region centered at a corresponding depth, the depth depending on the electron beam landing energy. More precisely, each detection region can correspond to a corresponding volume of the sample under test, wherein electrons from the corresponding electron beam can cause the ejection of electrons in the inner shells of atoms (in the detection region), causing each of these atoms to have an inner shell vacancy. The inner shell vacancies can be filled by relaxing the outer shell electrons to the inner shell. The relaxation can be accompanied by the emission of photons (the energy of the photons is equal to the energy lost by the electrons in the transition from the outer shell to the inner shell).

[0105] According to some embodiments, the number of electron beam landing energies and the minimum and maximum electron beam landing energies may be selected to ensure detection of the tested sample over a range of depths. According to some such embodiments, the number of electron beam landing energies and the minimum and maximum electron beam landing energies may be selected to ensure detection of the tested sample along a depth dimension of the sample.

[0106] According to some embodiments, in each implementation of sub-operation 110b, at least one measured spectrum is obtained. Each of the measured spectra may correspond to a photon energy range or a corresponding photon energy range, each of which includes at least one characteristic X-ray line associated with at least one target substance. The measurement data includes (e.g., consists of) the measured spectra. According to some embodiments, is or includes a measured spectrum of a characteristic X-ray line, or is obtained from (i.e. is a function of) a measured spectrum of a characteristic X-ray line. More specifically, according to some embodiments, the set of extracted parameters characterizing the shape of a peak around the corresponding characteristic X-ray line may be derived. According to some embodiments, the key feature is or includes the intensity of the characteristic X-ray line and / or the intensity of the background radiation, or is a function of the intensity of the characteristic X-ray line and / or the intensity of the background radiation. According to some embodiments, the key feature includes a so-called "energy signal" (e.g., consists of a so-called "energy signal"). According to some embodiments, each component of the energy signal may correspond to the absolute intensity, normalized intensity or relative intensity of the corresponding characteristic X-ray line. Each possibility corresponds to a separate embodiment. According to some embodiments, each component of the energy signal may correspond to the intensity of the corresponding characteristic X-ray line normalized by the average background intensity with respect to the characteristic X-ray line. The following is FIG. 4A to FIG. 4EVarious ways in which an energy signal can be derived from this are described in the description of .

[0107] It can be obtained through computer simulation The value of In a similar way, Each of comprises or is obtained from (i.e. is a function of) a corresponding simulated (rather than measured) spectral parameter characterizing a characteristic X-ray line associated with the nth simulated sample. In particular, and may share the same functional form (ie the same dependence on spectral parameters such as the energy signal and optionally the background radiation). More precisely, in order to obtain The computer simulation may simulate the impact of the electron beam at each of at least one landing energy on each of the N (simulated) samples, the penetration of the electron beam in the N samples, and the emission of the resulting X-rays. Specifically, the computer simulation may simulate each of the N implementations of the measurement operation 110 with respect to each of the N simulated samples. For each 1≤n≤N, the nth simulated sample is represented by the value of one or more structural parameters The computer simulation is configured to output values ​​of key features corresponding to the simulated samples, i.e., for (i.e., by Characterized by) the nth simulation sample

[0108] According to some embodiments, the one or more structural parameters may include one or more of the following: the total concentration of at least one material included by the tested sample; and optionally, when the tested sample includes a structure embedded in or on the tested sample, the width of the embedded structure (e.g., the width of a gate, fin, or depletion layer). Additionally or alternatively, according to some embodiments in which the tested sample includes one or more layers, the one or more structural parameters may include one or more of the following: (i) at least one thickness of at least one of these layers; (ii) the combined thickness of at least two or more of these layers; (iii) at least one mass density of at least one of these layers; and (iv) at least one relative concentration of at least one material in one or more of these layers. As a non-limiting example, with respect to item (iv), the one or more structural parameters may include a relative concentration of a first material in a subset of these layers, such as an adjacent layer or a layer of the first type (which may not be adjacent). The relative concentration of the first material in the subset (of these layers) may correspond to the total number of particles of the first material included in the subset divided by the total number of particles (of all materials (including the first material)) included in the subset.

[0109] More generally, as described above and in more detail below, the one or more structural parameters may include any geometric and / or compositional parameters of the tested sample, the changes of which affect the at least some of the components of the sample so as to allow estimation of the value of one or more structural parameters characterizing the sample being tested.

[0110] As used herein, the term "target substance" is used to refer to a substance (i.e., material) that is included in the sample being tested and whose spectrum, at least about one characteristic X-ray line of the substance, is measured as part of the measurement operation 110 (i.e., when the method 100 is applied to the sample being tested).

[0111] Note that in embodiments where one or more structural parameters consist of a single structural parameter, and is a one-dimensional vector (ie, a scalar). Non-limiting examples include embodiments where only the thickness of a single layer or the total mass density of a single target substance is determined.

[0112] According to some embodiments, and as expanded upon below, for example in FIG. 4A to FIG. 4E In the description of , for each of the at least one target substance and for each electron beam landing energy, in sub-operation 110b, an X-ray emission spectrum in a photon energy range including a characteristic X-ray line of the target substance is measured. According to some embodiments, sub-operation 110b may be implemented using an energy dispersive X-ray (EDX) spectrometer and / or a wavelength dispersive X-ray (WDX) spectrometer. According to some embodiments, and as elaborated in detail below in the description of sub-operation 120a, in order to derive A corresponding curve was fitted to each of the X-ray emission spectra.

[0113] According to some embodiments, the photon energy range of the measured X-ray emission spectrum can be narrow in the sense of being limited to a nearby portion (e.g., about three times, about five times, or even about ten times the width) of a characteristic X-ray line of the target substance or the immediate vicinity of the characteristic X-ray line. A related non-limiting example includes an embodiment in which a WDX spectrometer is used to obtain the measured spectrum. Alternatively, according to some embodiments, and as described in more detail below, an X-ray detector and an optical filter can be employed to measure the intensity of the emitted X-rays at or near the characteristic X-ray line of the target substance.

[0114] For ease of description, please refer to FIG. 2A to FIG. 2D . FIG. 2A to FIG. 2D An implementation of the measuring operation 110 according to some embodiments of the measuring operation 110 of the method 100 is schematically depicted. Figure 2AA cross-sectional view of a sample 20 probed by an electron beam according to a measurement operation 110 is shown. To make the description more specific, it is assumed that the sample 20 includes a plurality of lateral (i.e., horizontal) layers 22, wherein at least some of the layers 22 differ from each other in material composition (e.g., concentration of one or more of the target substances). According to some embodiments, at least some of the layers 22 may differ from each other in thickness.

[0115] As a non-limiting example, in FIG. 2A to FIG. 2D , the sample 20 is shown to include three layers disposed one on top of another: a first layer 22′, a second layer 22″, and a third layer 22′″. The first layer 22′ is disposed above the second layer 22″. The second layer 22″ is sandwiched between the first layer 22′ and the third layer 22′″. The top surface of the first layer 22′ constitutes the outer surface 24 of the sample 20. An electron beam source 202 and an electron beam 205 generated thereby are also shown so as to impinge (e.g., orthogonally impinge) on the outer surface 24. The electron beam source 202 can be configured to project an electron beam (one at a time) at each of a plurality of electron beam landing energies, thereby implementing sub-operation 110a.

[0116] The greater the landing energy of the electron beam 205, the greater the depth to which the electrons from the electron beam 205 will (on average) penetrate the sample 20. Furthermore, the greater the landing energy of the electron beam 205, the larger the volume within the sample can be in which the electrons from the electron beam 205 interact with matter in the sample 20, thereby causing the emission of characteristic X-rays. Figure 2A 2 is illustrated by three detection regions 26: a first detection region 26a corresponds to a volume in which approximately all (e.g., at least 80%, at least 90%, or at least 95%) of the characteristic X-ray (i.e., electromagnetic X-ray radiation) emission interactions will occur due to the electron beam penetrating the sample 20 at a first electron beam landing energy E1. A second detection region 26b corresponds to a volume in which approximately all of the characteristic X-ray emission interactions will occur due to the electron beam penetrating the sample 20 at a second electron beam landing energy E2. A third detection region 26c corresponds to a volume in which approximately all of the characteristic X-ray emission interactions will occur due to the electron beam penetrating the sample 20 at a third electron beam landing energy E3. The first detection region 26a is centered on a first point P1 at a depth u1, the second detection region 26b is centered on a second point P2 at a depth u2, and the third detection region 26c is centered on a third point P3 at a depth u3. E1<E2<E3. Therefore, u1<u2<u3. According to some embodiments, Figure 2A As shown, the size of the third detection area 26c is larger than the size of the second detection area 26b, and the size of the second detection area is larger than the size of the first detection area 26a.

[0117] Figure 2B A first electron beam 205a generated by the electron beam source 202 and having a first electron beam landing energy E1 is shown incident on the sample 20. A first detection region 26a is also depicted (where approximately all characteristic X-ray emission interactions caused by the first electron beam 205a occur). X-rays may be emitted in all directions, as exemplified by X-ray 215a. X-ray 215a′ represents an X-ray beam arriving at an X-ray detector 204 (such as Figure 5 X-rays (from X-ray 215a) of the X-ray detector).

[0118] Figure 2C A second electron beam 205b generated by the electron beam source 202 and having a second electron beam landing energy E2 is shown incident on the sample 20. A second detection region 26b is also depicted (where approximately all of the characteristic X-ray emission interactions caused by the second electron beam 205b occur). X-rays can be emitted in all directions, as shown by X-rays 215b. X-rays 215b' represent X-rays (from X-rays 215b) that reach the X-ray detector 204.

[0119] Figure 2D A third electron beam 205c generated by the electron beam source 202 and having a third electron beam landing energy E3 is shown incident on the sample 20. A third detection region 26c is also depicted (where approximately all characteristic X-ray emission interactions caused by the third electron beam 205c occur). X-rays can be emitted in all directions, as shown by X-rays 215c. X-rays 215c' represent X-rays that reach the X-ray detector 204 (from the emitted X-rays 215c).

[0120] Despite FIG. 2B to FIG. 2D 1 and 2. The layers 22 are depicted as differing from one another in their respective refractive indices (as evidenced by the refraction of X-rays as they transition from one layer to another), but it is understood that the method 100 is equally applicable without such differences.

[0121] For each of the electron beam landing energies (e.g., electron beam landing energies E1, E2, and E3), corresponding measurement data of the emitted X-rays may be obtained by the X-ray detector 204, thereby implementing sub-operation 110b. In particular, for each of the electron beam landing energies, the corresponding measurement data may include a corresponding X-ray emission spectrum within a photon energy range (the X-ray detector 204 is configured to measure the corresponding X-ray emission spectrum), and the photon energy range includes at least one characteristic X-ray line associated with a corresponding target substance of at least one target substance. The intensity of the characteristic X-ray line corresponding to the target substance represents the average concentration (i.e., particle density) of the target substance in the corresponding detection area. More specifically, each substance is characterized by a unique set of characteristic X-ray lines (i.e., spectral lines in the characteristic X-ray limit) corresponding to the energy difference between the orbits of the elements constituting the substance. The greater the concentration of the substance, the greater the measured intensity of each characteristic X-ray line associated with the concentration of the substance.

[0122] According to some embodiments, and as follows in Figure 3 As expanded from the description of , in sub-operation 120b, it is possible to calculate and (from Extrapolated) vector-valued function The minimum distance between Each of the components of the vector-valued function quantifies the dependence of the corresponding (extrapolated) key feature on (the value of) one or more structural parameters. The one or more structural parameters are Parameterization, where is a vector of free parameters (i.e., variables corresponding to structural parameters, respectively). As described in detail below, according to some embodiments, Minimize to obtain the minimum distance, where Each of the components of varies within a corresponding continuous range of values.

[0123] Alternatively, according to some embodiments, the right Apply the k-nearest neighbor (k-NN) regression algorithm (k<N) to obtain According to some such embodiments, include (i.e., a vector specifying nominal values ​​of one or more structural parameters, or in other words, a vector corresponding to simulated nominal samples characterized by the nominal values). Thus, Including when compared to the nominal value (ie, ) is realized by computer simulation. Note that the k-NN regression algorithm can be weighted or unweighted. That is, can be considered as equal to the k closest Corresponding Alternatively, can be considered equal to the closest k Corresponding In general, i.e. when one or more structural parameters include two or more structural parameters, the term "median" should be understood to refer to a multivariate extension of the (one-dimensional concept) median, such as the marginal median or the geometric median. More generally, can basically be the closest to k Corresponding Any function of .

[0124] According to still some other embodiments, is determined as the output of the neural network. The neural network is configured to receive a vector of key features (i.e., ) as input and output The neural network is trained using a training set consisting of N pairs of vectors such that for each 1≤n≤N, is used as the (training) input, and Used as the corresponding (training) output.

[0125] According to some embodiments, method 100 may further include an initial operation of exporting (i.e., set ). For each 1≤n≤N, it can be derived by computer simulation The computer simulation simulates, for each landing energy: (i) the electron beam (at a given landing energy) incident on the structure with the values ​​of one or more structural parameters (ii) penetration of the electron beam into the corresponding simulated sample and travel of the electron beam in the corresponding simulated sample; (iii) emission of X-rays generated from the corresponding simulated sample; and optionally, (iv) measurement of the emitted X-rays.

[0126] Figure 3 A flow chart of a measurement data analysis operation 300 is presented, which corresponds to a specific embodiment of the measurement data analysis operation 120 of the method 100. The measurement data analysis operation 300 includes:

[0127] - Sub-operation 310, in which the vector Designated key characteristics.

[0128] - Sub-operation 320, in which the As to make at least and A (numerical or analytical) solution that minimizes the loss function of . (That is, Minimize the loss function. ) It is through A vector-valued function of the key features obtained by extrapolation.

[0129] Sub-operations 310 and 320 correspond to specific embodiments of sub-operations 120a and 120b of method 100, respectively.

[0130] Each of the components of is a quantitative measure of the value of the corresponding key feature to one or more structural parameters is a function of the dependencies of (as specified by the model). Thus, for example, ( The jth component of the energy signal) is the quantization of the jth key feature (for example, the jth component of the energy signal) A function of the dependencies of (on each model).

[0131] According to some embodiments, in sub-operation 320, Indicates a pair of vectors and The mathematical distance between (the mathematical distance may be a norm or not) (thus yes and Non-limiting examples of distances include L 1 Norm and L 2 norm. (Note that in this case the norm is L 2 ,and exist In embodiments where is linear, the optimization problem admits of an analytical solution. ) According to some such embodiments, Double vertical bars indicate vector norms (e.g., the Euclidian norm: More generally, and as described in detail below, one may To determine Where M1 and M2 are matrices with appropriately chosen properties as specified below. In particular, each of M1 and M2 may be a positive definite matrix (optionally, a diagonally positive definite matrix) having a respective smallest eigenvalue greater than a respective pre-specified (positive) threshold.

[0132] According to some embodiments, one or more regularization terms may be added to the norm (Or more generally, or the first term of the loss function (depending on and ) to stabilize the solution or as one or more constraints reflecting some prior knowledge about one or more structural parameters.

[0133] According to some embodiments, the extrapolation is a linear function. That is, in is the sum of the values ​​of the key features and The corresponding vector. A is K f ×K p Matrix. K f is the number of key features, that is, (as well as and The dimension of each of K p yes (and ), i.e., the number of one or more structural parameters of the tested sample (and each of the simulated samples) to be determined. Specified with (i.e., by the nominal value The value of the key feature corresponding to the nominal sample (characterized).

[0134] According to some embodiments, it is possible to obtain The same way to get That is, the impact and penetration of the electron beam on the sample and the subsequent X-ray emission at each of at least one landing energy are simulated by computer simulation, the sample being According to some such embodiments,

[0135]

[0136] For each 1≤n≤N, B is for K f ×K p Matrix. The double vertical bars indicate the matrix norm (e.g., the Frobenius norm). Optionally, according to some embodiments, one or more regularization terms may be added to the matrix norm, in which case minimization is understood to be over the sum of the matrix norm and the one or more regularization terms.

[0137] Alternatively, according to some embodiments, similar to matrix A, by optimizing In particular, according to some such embodiments, obtaining and matrix A as , where the double vertical bars indicate the matrix norm (e.g., Frobenius norm). Optionally, according to some embodiments, one or more regularization terms may be added to the matrix norm, in which case minimization is understood to be over the sum of the matrix norm and the one or more regularization terms.

[0138] According to some embodiments, the extrapolation may be As a non-limiting example, according to some such embodiments, yes That is, in this embodiment, for each 1≤c≤K f , The cth component of will consist (in addition to the linear contribution and the constant) of The squared contribution given by Indicator K p ×K p Matrix T (c) The (a,b)th component of .

[0139] The optimization problems specified throughout this application may be solved using standard local and / or global optimization algorithms, such as gradient descent or quasi-Newton methods. According to some embodiments, where the specified optimization problem allows for a known analytical solution, the quantity sought to be optimized (e.g., Matrix A, ). As a non-limiting example, in which exist is linear (i.e., in is a matrix), and the norm is L 2 In the example, the optimization problem assumes the form Make The matrix yes As another example, in which In embodiments that allow for (known) analytical solutions, for example when the matrix norm is the Frobenius norm, this can be solved by replacing and Insert into the analytical solution (the function defining the analytical solution) to obtain A. More precisely, the analytical solution is given by A = (F-F0)Q, where Q is the column of the matrix composed of The matrix formed The inverse Moore-Penrose matrix F is the matrix whose columns are composed of The matrix is ​​composed of Finally, in which In embodiments that allow for (known) analytical solutions, for example when the matrix norm is the Frobenius norm, this can be solved by replacing and Inserted into the analytical solution (the function that defines the analytical solution) to obtain and A. By appropriate manipulation, it is possible to determine A in the same way as when only A is to be determined (i.e., when given The analytical solution is obtained in basically the same way.

[0140] Optionally, according to some embodiments, the measurement data analysis operation 300 may further include an (optional) sub-operation ( Figure 3 Not specified in ): By using A set of N′>N vectors relative to the key features Subjected to a (k=N)-NN classifier to obtain A set of N′>N vectors of key features Include (optionally, re-labeled) The additional N′-N vectors (i.e., beyond ) is obtained by applying computer simulation to N′-N additional simulation samples. The N′ may be selected as described above in the description of method 100. Complete set of .

[0141] Referring again to method 100, according to some embodiments, in sub-operation 120a (and thus also sub-operation 310), in order to derive A corresponding curve is fitted to each of the X-ray emission spectra (obtained for each of the electron beams projected in the measurement operation 110). According to some embodiments, this FIG. 4A to FIG. 4E By way of example, in this case, where is given by the energy signal associated with a single target substance and a single spectral line (ie, a single characteristic X-ray line) In the following, a more general case will be described, in which the energy signal is associated with a plurality of target substances and / or for at least some of the target substances a plurality of spectral lines of the target substances are considered.

[0142] refer to Figure 4A , Figure 4A A measured (X-ray emission) spectrum 400 is depicted, which is obtained by performing the measurement operation 110 on a sample under test (e.g., sample 20). FIG. 4B to FIG. 4E In each of the cases of , the horizontal axis corresponds to the photon energy ε (or equivalently, the frequency) of the emitted X-rays, and the vertical axis corresponds to the intensity I of the emitted X-rays. The scales on each of the horizontal and vertical axes are linearly spaced, with ε i <ε i+1 , and I i <I i+1 The peak 410 of the measured spectrum 400 is substantially centered on the characteristic X-ray line of the target substance, which is included in the tested sample and the energy signal of the target substance will be obtained. Figure 4BAn optimization curve 450 fitted to the measured spectrum 400 is depicted. Figure 4C An optimization curve 450 superimposed on the measured spectrum 400 is depicted.

[0143] According to some embodiments, the values ​​of one or more adjustable parameters involving an optimization curve (also referred to as a "free curve") are fitted to the measured spectrum 400, thereby obtaining an optimization curve 450. The values ​​of the one or more adjustable parameters are fixed by minimizing the distance between the free curve and the measured spectrum (over the one or more adjustable parameters).

[0144] The one or more adjustable parameters may include a (first) adjustable parameter, the value of the (first) adjustable parameter representing the intensity of the emitted X-rays about the characteristic X-ray line of the target material. According to some such embodiments, the adjustable parameter is a multiplication coefficient of a normalized hat function (e.g., a normalized Gaussian function), which normalized hat function may be centered on the characteristic X-ray line. According to some embodiments, the one or more adjustable parameters include a plurality of adjustable parameters, in addition to the first adjustable parameter, the plurality of adjustable parameters include additional bias parameters, at least one parameter controls the shape of the hat function (e.g., the width of the normalized Gaussian function), and / or a (characteristic X-ray) line shift parameter controls the center position of the hat function.

[0145] More generally, according to some embodiments, the free curve may be a sum of at least two adjustable functions: an adjustable hat function that may be centered on the characteristic X-ray line, and an adjustable second function that quantifies a (continuous) spectrum of a bremsstrahlung (i.e., background radiation) component of a corresponding measured X-ray emission spectrum (e.g., background radiation near the characteristic X-ray line). As a non-limiting example, at least one landing energy comprises N E Electron beam landing energy Make the measurement N E X-ray emission spectra: Here ε represents the photon energy of the emitted X-ray, and s i (ε) (the i-th measured X-ray emission spectrum) is obtained by i According to some embodiments, N E A set of free curves Fitting to the set of measured spectra According to some embodiments, for each 1≤i≤N E , c i (ε) = G i (ε)+b i (ε), where G i(ε) is an adjustable hat function, and b i (ε) is the second adjustable function. G i (ε) = a i ·g i (ε), where g i (ε) is the normalized hat function, and a i is the multiplication coefficient. According to some embodiments, g i (ε) can be a (normalized) Gaussian function, in which case g i The width and optionally the center of (ε) may be adjustable parameters (optimization may be performed on the adjustable parameters). According to some alternative embodiments, g i (ε) may be a (normalized) gamma distribution or a generalized Gaussian distribution. According to some embodiments, b i (ε) may be a polynomial with adjustable coefficients (e.g., a first-order polynomial or a second-order polynomial). Alternatively, according to some embodiments, b may be determined from Kramer's law: i (ε).

[0146] Due to g i (ε) is normalized, so a i Substantially equal to the intensity (or equivalently, the number of photons) of the X-rays emitted due to the transition corresponding to the characteristic X-ray line of the target material and collected (detected) by the X-ray measurement module.

[0147] Respectively by and Indicates g i (ε) and b i (ε) is an adjustable parameter, for each 1≤i≤N E , we can make D(c i (ε),s i (ε)) in a i , and Minimize the above to obtain the optimal value of the adjustable parameters and D(c i (ε),s i (ε)) is c i (ε) and s i More generally, according to some embodiments, the distance between the two can be determined by making i (ε) and s i (ε) is minimized to obtain the optimized value. As a non-limiting example, according to some embodiments, where g i (ε) is a Gaussian function, and b i (ε) is a second-order polynomial: (i) where g i,1 and g i,2 parameterize the width and center of the Gaussian function; and (ii) where b i,0 , b i,1 and b i,2 are the zeroth, first, and second order coefficients of the polynomial. In particular, According to some embodiments, D(c i (ε), s i (ε))=∫dε|c i (ε)-s i (ε)| 2 (or a discrete equivalent expression). According to some embodiments, a regularization term may be added to D(c i (ε),s i (ε)) to take into account prior knowledge about any free parameters and / or to stabilize the solution (of the minimization algorithm).

[0148] According to some alternative embodiments, where there is prior knowledge that relates at least some of the free parameters to each other, by jointly optimizing all adjustable parameters, i.e. Subject to the constraints imposed by the above prior knowledge, we obtain the complete set of optimized values, namely (or equivalently in and Indicated by and More specifically, in this embodiment,

[0149]

[0150] in YesN c A set of constraints. (That is, Q l Each of is an equation or inequality that relates at least some of the free parameters to each other. )

[0151] As a non-limiting example, according to FIG. 4B to FIG. 4E In some embodiments depicted in FIG. 1 , the free curve is the sum of three adjustable functions. In addition to g being a Gaussian function i (ε) and is a second-order polynomial b i In addition to (ε), the sum also includes the Gaussian function Y i (ε). Reference Figure 4D , curve 460 and Corresponding. (also a Gaussian function) is obtained by optimizing Y i (ε) is obtained by taking the free parameters of Focuses on the characteristic X-ray line of the target substance. The characteristic X-ray line of the second substance (non-target) present in the sample being tested is centered. The characteristic line of the second substance is close to the characteristic line of the target substance, and therefore the characteristic line of the second substance is taken into account to improve the determination (and therefore ) accuracy. Figure 4E , curve 470 and Corresponding. Figure 4C Curve 470 is also plotted.

[0152] According to some embodiments, an X-ray emission spectrum of a single characteristic X-ray line of a single target substance (included in the sample under test) is used to determine The number of components of is equal to the number of electron beam landing energies. According to some embodiments, for each 1≤j≤J, equal (jth component of the energy signal). More generally, according to some embodiments, for each 1≤j≤J, in yes and That is, for each 1≤j≤J, the jth component of the energy signal is and According to some such embodiments, where q is As a non-limiting example, according to some embodiments, and The triangular brackets indicate that the The width of the interval The center of is averaged.

[0153] According to some embodiments, the key feature may be derived based on the dependence of the intensity of the emitted X-rays for each of a plurality of different characteristic X-ray lines on the electron beam landing energy. According to some such embodiments, wherein N L is the number of X-ray lines with different characteristics, the key feature is composed of components J = N E ×N L Component vector specified, where 1 ≤ n E ≤N E , and 1≤n L ≤N L (N E is the number of landing energies). The first index represents the electron beam landing energy, and the second index represents the characteristic X-ray line. That is, In such an embodiment, in the measurement operation 110, for each electron beam landing energy, an X-ray emission spectrum is measured in one or more photon energy ranges including a plurality of characteristic X-ray lines. ) The component of N L = 1. According to some embodiments, at least one target substance includes N sub (N sub ≤N L ) target substances, N L The characteristic X-ray lines include N sub A characteristic X-ray line corresponding to each of the target substances.

[0154] As described above, according to some embodiments, M1 and M2 are matrices with appropriately chosen properties (e.g., positive definiteness and symmetry as specified below), and express and According to some embodiments, one or more regularization terms may be added to To make the description more concrete by way of non-limiting example, an example in which an X-ray emission spectrum of a single characteristic X-ray line of a single target substance is used to determine So that for every 1≤j≤N E , equal and (Right now, The number of components is equal to 2N E ). That is, The first few N E The components are non-normalized energy signal components, and the last few N E The component is a pure bremsstrahlung (ie, background radiation) component. According to some such embodiments, (so that M1 is equal to the identity matrix, and M2=M). M is a diagonal matrix whose diagonal entries are in the order of 1≤j≤N E , are pairwise equal in the sense of , where T is a pre-specified (positive) threshold. That is, for each 1≤j≤N E , along the diagonal of M, the (N E +j) components are equal to the jth component along the diagonal. Therefore, for each 1≤j≤N E , by the same corresponding factor and The inclusion of M and minimization on it can illustrate the components and The corresponding components of the potential different scaling, thus, for at least some 1≤j≤N E , The scaling and Different from and According to some such embodiments, According to some embodiments, one or more regularization terms may be added to More generally, according to some embodiments, in is the loss function, which depends on and And the loss function is equal to (where M has the properties and symmetries specified above, i.e., a pairwise equality). It depends on and According to some embodiments, in is a regularization function including one or more regularization terms.

[0155] According to some embodiments, where a spectrometer is used to obtain the X-ray emission spectrum, the measurement data analysis operation 120 may include an initial pre-processing sub-operation, where the X-ray emission spectrum may be pre-processed to remove noise.

[0156] According to some embodiments of method 100, sub-operation 120a includes an initial sub-operation in which a difference spectrum is obtained by subtracting a measured (control) spectrum of a control sample from the measured spectrum of the tested sample obtained in measurement operation 110, respectively. The control spectrum may be of a gold standard sample, which is known or assumed to closely match the intended design of the tested sample in the sense that one or more structural parameters of the gold standard sample (optionally, also structural parameters not estimated by method 100) deviate by no more than 1%, 2%, or 5% from the nominal values ​​of the one or more structural parameters. Each possibility corresponds to a separate embodiment.

[0157] More specifically, the difference spectrum may be processed (in sub-operation 120a) to extract a vector of key features from the difference spectrum. Basically as above FIG. 4A to FIG. 4E In this embodiment, the computer simulation of sub-operation 120b is configured as a set of vectors based on N simulation key features. To receive As input and output For each 1≤n≤N, can be considered equal to When relative to the nth simulated sample (i.e., When the simulated sample of the characterization is realized, computer simulation is used to obtain When achieved relative to a control sample, the control sample is used to obtain For the purpose of computer simulations, the exact characteristics of the control samples were assumed to be Note that in embodiments that include extrapolation, The zero-order term in will be zero. Therefore, when extrapolated onto a linear function, the extrapolated function will have the form

[0158] According to some such embodiments, method 100 may further include obtaining a control spectrum, for example by performing measurement operation 110 relative to a control sample.

[0159] It should be understood that the applicability of method 100 is not limited to samples including nominally flat layers (e.g. FIG. 2A to FIG. 2D ), and more generally, stratified samples. Regions of different material composition from each other can in principle be of arbitrary shape. In particular, method 100 can be applied to structures comprising locally embedded (buried) features, such as nanowires, all-around gate nanosheets, and more generally, channels. Method 100 can also be applied to samples characterized by a density of a substance included in the sample that varies continuously as a function of a depth coordinate and / or in three dimensions as a function of a lateral coordinate. Furthermore, a person skilled in the art will readily appreciate that method 100 can be applied to samples comprising cavities and / or holes.

[0160] system

[0161] According to an aspect of some embodiments, there is provided a computerized system for non-destructive three-dimensional probing and characterization of samples, such as, for example, semiconductor structures included in a patterned wafer, based on X-ray measurements and subsequent analysis of the obtained measurement data using computer simulation. Figure 5 Such a system, namely a computerized system 500, is schematically depicted according to some embodiments. As will be apparent from the description of the system 500, the system 500 can be used to implement the method 100 (including specific embodiments of the method 100, which includes the measurement data analysis operation 300). In particular, the system 500 can be used to determine the value of one or more structural parameters that characterize the sample being tested. Non-limiting examples of structural parameters that can be estimated using the system 500 are listed above in the method section in the description of the method 100.

[0162] System 500 includes an electron beam source 502, an X-ray detector 504 (or more generally, an X-ray sensing assembly including two or more X-ray detectors), a processing circuit system 506, and a controller 508. According to some embodiments, system 500 may further include a workbench 520 (e.g., an xyz stage) configured to receive (be tested) sample 50. According to some embodiments, electron beam source 502, X-ray detector 504, and controller 508 form part of a scanning electron microscope. According to some embodiments, sample 50 may be a patterned wafer or a structure (e.g., a semiconductor structure) included in or on a patterned wafer. According to some such embodiments, sample 50 may be a preliminary structure in one of the manufacturing stages of the patterned wafer, or an auxiliary structure employed in one of the manufacturing stages of the patterned wafer. According to some embodiments, sample 50 may be or include one or more memory components and / or logic components (such as a gate stack, e.g., a high dielectric constant metal gate stack). Note that sample 50 does not form part of system 500.

[0163] Dashed lines between elements indicate a functional or communicative relationship between them.

[0164] The electron beam source 502 is configured to generate an electron beam at a plurality of electron beam landing energies. In particular, substantially as described above in the description of sub-operation 110a of method 100, the electron beam source 502 is configured to generate an electron beam at each of the plurality of landing energies to allow for respectively probing the sample 50 to a plurality of depths.

[0165] The greater the depth of the tested sample to be probed, the greater the maximum electron beam landing energy, and optionally the greater the number of electron beam landing energies. According to some embodiments, the plurality of electron beam landing energies may include landing energies up to about 5 keV, about 10 keV, about 15 keV, about 20 keV, or even about 30 keV. Each possibility corresponds to a different embodiment. In silicon, the depth to which an electron beam with a landing energy of about 15 keV can penetrate is about 3 μm.

[0166] The duration of the electron beam projection can be determined by An indication of the degree of accuracy required (ie, the value of one or more structural parameters characterizing the sample 50).

[0167] According to some embodiments, an electron beam 505 generated by an electron beam source 502 is shown incident on (the outer surface 54 of) a sample 50. X-rays, in particular characteristic X-rays, are generated as a result of the electron beam 505 incident on the sample 50 and the electron beam 505 penetrating the sample 50. A portion of these X-rays, consisting of X-rays 515, reaches the X-ray detector 504.

[0168] According to some embodiments, the X-ray detector 504 is sensitive to electromagnetic radiation in the X-ray photon energy range (at least in the characteristic X-ray limit or one or more sub-ranges of the characteristic X-ray limit). According to some embodiments, the X-ray detector 504 can be an EDX spectrometer or a WDX spectrometer. According to some embodiments, an X-ray detector assembly including both an EDX spectrometer and a WDX spectrometer can be used instead of a single X-ray detector. In such an embodiment, both an EDX spectrometer and a WDX spectrometer can be used to obtain an X-ray emission spectrum, wherein the WDX spectrometer is used to "zoom in" on the characteristic X-ray line. In particular, the larger resolution of the WDX spectrometer (which makes the WDX spectrometer slower) allows narrower peaks and valleys to be obtained compared to the EDX spectrometer. According to some embodiments, where the spectrometer is a WDX spectrometer, the X-ray detector 504 can be configured to allow scanning in an extended photon energy range (thereby allowing the X-ray emission spectrum to be obtained in an extended photon energy range). The X-ray detector 504 is configured to relay measurement data collected thereby (eg, a spectrum of X-rays incident on the X-ray detector) to the processing circuitry 506 (optionally via the controller 508 ).

[0169] According to some embodiments, the system 500 may also include a window (not shown) positioned between the X-ray detector 504 and the workbench 520, which may be configured to controllably and differentially attenuate the spectrum of emitted X-rays and / or protect X-ray sensitive surfaces of the spectrometer.

[0170] According to some alternative embodiments, the X-ray detector 504 is configured to measure the intensity of electromagnetic X-ray radiation (i.e., electromagnetic radiation within the X-ray photon energy range) at or near the characteristic X-ray line of the (target) material included in the sample 50, without additionally measuring the intensity of electromagnetic X-ray radiation within an extended photon energy range outside the immediate vicinity of the characteristic X-ray line. According to some such embodiments, the system 500 may also include an optical filter (not shown). The optical filter is configured to block electromagnetic radiation having photon energies outside the immediate vicinity of the characteristic X-ray line from reaching the X-ray detector 504.

[0171] According to some embodiments, the system 500 may include additional elements. The additional elements may include electron optics (not shown; for example, one or more electrostatic lenses and one or more magnetic deflectors) that may be used to direct and manipulate the electron beam generated by the electron beam source 502. Additionally or alternatively, the additional elements may include collection optics configured to direct electromagnetic radiation generated by the electron beam incident on the sample 50 and the electron beam penetrating the sample onto the X-ray detector 504. According to some embodiments, the additional elements may include filters configured to block electromagnetic radiation outside the characteristic X-ray limit and / or one or more sub-ranges of the characteristic X-ray limit.

[0172] According to some embodiments, at least the electron beam source 502 and the workbench 520 may be housed within the vacuum chamber 530. Figure 5 The X-ray detector 504 is shown as being positioned within the vacuum chamber 530 , but according to some alternative embodiments, the X-ray detector 504 may be positioned outside the vacuum chamber 530 .

[0173] The controller 508 may be functionally associated with the electron beam source 502, and optionally with the stage 520. More specifically, the controller 508 is configured to control and synchronize the operation and functions of the instruments and components listed above during probing of the sample under test (e.g., instructing the electron beam source to vary the electron beam landing energy).

[0174] The processing circuit system 506 may include one or more processors and optionally include RAM and / or non-volatile memory components (not shown). The one or more processors are configured to execute software instructions stored, for example, in the non-volatile memory components. By executing the software instructions, substantially as described above in Figure 1 and Figure 3 As described in the description of , the measurement data (eg, obtained by the X-ray detector 504) of the tested sample (eg, sample 50) is processed to determine

[0175] More specifically, the processing circuitry 506 is configured to process the measurement data to determine values ​​of one or more structural parameters that characterize the sample under test (described by To this end, as detailed above in the description of sub-operation 120b of method 100, processing circuit system 506 is configured to extract from the measurement data a vector specifying the value of the key feature obtained for the sample being tested. In particular, according to some embodiments, the key feature comprises (e.g. consists of) a so-called “energy signal”. According to some embodiments, each component of the energy signal may correspond to an absolute intensity, a normalized intensity or a relative intensity of a corresponding characteristic X-ray line.

[0176] According to some embodiments, where the measured data is an X-ray emission spectrum, the processing circuit system 506 may be configured to fit a corresponding (free) curve to each of the X-ray emission spectra to obtain an optimized curve. As described above in the description of sub-operation 120a of method 100, the optimized curve may then be extracted from the optimized curve. According to some such embodiments, processing circuitry 506 may be configured to execute one or more optimization algorithms (e.g., to address the above FIG. 4A to FIG. 4E ). Examples of relevant optimization algorithms include standard iterative optimization algorithms, such as gradient descent or Newton's method. According to some embodiments, a customized iterative optimization algorithm may be used, which is obtained by "tweaking" the standard iterative optimization algorithm to solve constraints and / or ensure that a global minimization is achieved.

[0177] More specifically, in order to determine The processing circuitry 506 is configured to additionally consider the set of vectors simulating the key features For each 1≤n≤N, Specifying values ​​of one or more structural parameters characterizing the nth simulated sample. As described above in the methods section, by computer simulation of the emission of X-rays from the corresponding simulated sample due to incident electron beams on the corresponding simulated sample one at a time at each of one or more landing energies, it is possible to obtain According to some embodiments, and as expanded upon above in the description of method 100, For K defined by one or more structural parameters p In the vector space The hypervolume centered on K is sampled, where K p is the number of one or more structure parameters.

[0178] According to some embodiments, the processing circuit system 506 may be further configured to obtain That is, by simulating one or more electron beams (one at a time) striking each of N simulated samples, one or more electron beams penetrating the simulated samples, and the resulting X-ray emission

[0179] According to some embodiments, processing circuitry 506 may be configured to calculate Among them, k (k<N) marks the closest k And the triangle brackets indicate k To this end, according to some embodiments, the processing circuit system 506 may be configured to be relative to right A k-nearest neighbor (k-NN) regression algorithm is applied. According to some embodiments, the processing circuit system 506 may be configured to calculate the k nearest neighbor. Corresponding The median value of

[0180] According to some alternative embodiments, Minimize the loss function, which is at least and key features from Extrapolated vector-valued function (Optionally, except for and In addition to the first term, the loss function may also include one or more regularization terms. ) Therefore, the processing circuit system 506 may be configured to: (i) execute an optimization algorithm to make the loss function Minimize (for example, and and (ii) in embodiments where the minimization of the loss function has a known analytical solution, additionally or alternatively, determining directly from the analytical solution (a function defining the analytical solution) According to some such embodiments, processing circuitry 506 may be configured to solve (numerically or analytically) the optimization problem To determine Or more generally, where M is a positive definite matrix (e.g., a diagonally positive definite matrix with pairwise equal diagonal entries as specified above) whose smallest eigenvalue is greater than a pre-specified (positive) threshold, and even more generally, where M1 and M2 are appropriately chosen matrices, and yes and The mathematical distance between . is the key feature (from extrapolated) vector-valued function and models the dependence of key features on the values ​​of one or more structural parameters.

[0181] According to some embodiments, the processing circuitry 506 may be further configured to perform the extrapolation (and thereby obtain ). According to some such embodiments, the extrapolation may be a linear function, i.e., Represents the nominal value of one or more structural parameters deviation. is the key feature and As detailed above in the description of method 300, A is a matrix that can be determined by optimization, which takes into account

[0182] According to some embodiments, processing circuitry 506 may be configured to Relative to Subject to (k=N)-NN classifier to extract Select from (N′>N) As used herein, the terms "fitted" and "optimized" are interchangeable when used in the context of curve fitting.

[0183] According to some embodiments, processing circuitry 506 and controller 508 may be housed in a common housing, such as when implemented by a single computer.

[0184] In the description and claims of this application, the words "including" and "having" and forms thereof are not limited to the members of the lists with which these words can be associated.

[0185] As used herein, the term "approximately" may be used to specify a value of a quantity or parameter (e.g., the length of an element) as being within a continuous range of values ​​close to (and including) a given (specified) value. According to some embodiments, "approximately" may specify that the value of a parameter is between 80% and 120% of the given value. For example, the statement "the length of the element is approximately equal to 1 m" is equivalent to the statement "the length of the element is between 0.8 m and 1.2 m". According to some embodiments, "approximately" may specify that the value of a parameter is between 90% and 110% of the given value. According to some embodiments, "approximately" may specify that the value of a parameter is between 95% and 105% of the given value.

[0186] As used herein, the terms "substantially" and "approximately" are interchangeable according to some embodiments.

[0187] According to some embodiments, the estimated quantity or estimated parameter can be said to be "approximately optimized" or "approximately optimal" when it falls within 5%, 10% or even 20% of the optimal value of the estimated quantity or estimated parameter. Each possibility corresponds to a separate embodiment. In particular, the expressions "approximately optimized" and "approximately optimal" also cover the case where the estimated quantity or estimated parameter is equal to the optimal value of the quantity or parameter. In principle, mathematical optimization software can be used to obtain the optimal value. Therefore, for example, when the estimated value is not greater than 101%, 105%, 110% or 120% (or some other predefined threshold percentage) of the optimal value of the quantity, the estimated (e.g., estimated residual) can be referred to as "approximately minimized" or "approximately minimum / minimum value". Each possibility corresponds to a separate embodiment.

[0188] For ease of illustration, a three-dimensional Cartesian coordinate system (with orthogonal x-, y-, and z-axes) is introduced in some of the figures in the accompanying drawings. Note that the orientation of the coordinate system relative to the depicted objects may vary from one figure to another. In addition, the symbol ⊙ is used to indicate an axis pointing "out of the page", while the symbol Can be used to indicate an axis pointing "inside the page".

[0189] In the flow chart, optional operations and sub-operations are depicted by dotted lines. Similarly, in the block diagram, optional elements can be depicted by dotted lines. Further, (in the block diagram), dotted line connecting elements can be used to represent functional associations or at least one-way or two-way communication associations between connected elements.

[0190] It should be understood that certain features of the present disclosure described in the context of separate embodiments may also be provided in combination in a single embodiment for the sake of clarity. Conversely, various features of the present disclosure described in the context of a single embodiment may also be provided separately, or in any suitable subcombination, or suitably provided in any other described embodiment of the present disclosure for the sake of brevity. Features described in the context of an embodiment are not considered essential features of that embodiment unless expressly so specified.

[0191] Although the operation of the method according to some embodiments can be described in a specific order, the method of the present disclosure may include some or all of the described operations performed in different orders. In particular, it should be understood that, unless the context clearly indicates otherwise, the order of the operations and sub-operations of any described method can be reordered, for example, when the subsequent operation requires the output of the previous operation as input, or when the subsequent operation requires the product of the previous operation. The method of the present disclosure may include some of the described operations or all of the described operations. No specific operation in the disclosed method is considered to be the basic operation of this method unless explicitly specified in this way.

[0192] Although the present disclosure is described in conjunction with the specific embodiments of the present disclosure, it is apparent that there may be many substitutions, modifications and variations that are apparent to those skilled in the art. Therefore, the present disclosure includes all of these substitutions, modifications and variations that fall within the scope of the appended claims. It should be understood that the present disclosure is not necessarily limited to the details of the construction and arrangement of the components and / or methods set forth herein in its application. Other embodiments may be practiced, and the embodiments may be performed in various ways.

[0193] The wording and terminology used herein are for descriptive purposes and should not be considered as limiting. The citation or identification of any reference in this application should not be construed as an admission that such reference can be used as prior art for the present disclosure. The section headings used herein are for ease of understanding the specification and should not be construed as necessary limitations.

Claims

1. A system for non-destructive characterization of a sample, the system comprising: an electron beam (e-beam) source for projecting an electron beam at one or more electron beam landing energies onto a sample being tested; An X-ray detector, the X-ray detector being used to sense X-rays emitted from the tested sample; as well as processing circuitry configured to: receiving X-ray measurement data related to one or more electron beam landing energies from the X-ray detector; Extracting from the X-ray measurement data a vector of values ​​specifying key features of the X-ray measurement data as well as based on and a set of vectors that simulate key features To determine the value assumed by one or more structural parameters associated with the structure under test where, for every 1≤n≤N, specifying the values ​​assumed by the one or more structural parameters associated with the nth simulation sample, and is a product of a computer simulation of the emission of X-rays from the nth simulated sample due to the incident electron beam on the nth simulated sample at each of the one or more landing energies.

2. The method of claim 1, wherein Minimize the loss function, which is at least and the key features of Extrapolated vector-valued function function.

3. The system of claim 2, wherein the processing circuit system is further configured to calculate and The minimum distance between 4. The system of claim 1, wherein the processing circuit system is further configured to calculate With the The distance between 5. The system of claim 1 , wherein the one or more structural parameters include one or more of: a total concentration of at least one material included in the tested sample; and optionally, when the tested sample includes a structure embedded in or on the tested sample, a width of the embedded structure.

6. The system of claim 1, wherein the sample under test comprises a plurality of layers; and wherein the one or more structural parameters include one or more of the following: (i) at least one thickness of at least one of the layers; (ii) the combined thickness of at least two or more of the layers; (iii) at least one mass density of at least one of the layers; and (vi) at least one relative concentration of at least one material in one or more of the layers.

7. The system of claim 1, wherein the one or more electron beams have landing energies such as to cause emission of X-rays related to one or more characteristic X-ray lines, the one or more characteristic X-ray lines being respectively related to one or more target substances included in the tested sample; wherein the X-ray detector is configured to sense at least one measured spectrum of the respectively emitted X-rays within at least one photon energy range, the at least one photon energy range including at least one of the characteristic X-ray lines; and Wherein the X-ray measurement data comprises the measured spectrum.

8. The system of claim 6, wherein the one or more electron beams land at energies such as to cause emission of X-rays originating from at least two of the plurality of layers.

9. The system of claim 7, wherein the key feature is or includes the intensity of the characteristic X-ray line and / or the intensity of background radiation, or is a function of the intensity of the characteristic X-ray line and / or the intensity of background radiation.

10. The system of claim 2, wherein in specifying nominal values ​​of the one or more structural parameters, Specify the deviation from the stated nominal value, is the key feature of is a vector of corresponding values, and A is a matrix.

11. The system of claim 10, wherein is the product of a computer simulation of the emission of X-rays from a simulated sample due to an electron beam being incident on the simulated sample at each of the one or more landing energies, the simulated sample being characterization; and where the matrix A is equal to where the double vertical bars indicate the matrix norm, and for every 1≤n≤N, 12. The system of claim 10, wherein obtaining and the matrix A as where the double vertical bars indicate the matrix norm, and for every 1≤n≤N, 13. The system of claim 4, wherein the processing circuit system is further configured to determine part of right Apply the k-nearest neighbor (k-NN) regression algorithm to determine the closest k of the 14. The system of claim 13, wherein is closest to k The corresponding The average, optionally weighted average, or median of the values.

15. The system of claim 2, wherein the processing circuit system is further configured to The set of N′>N vectors with respect to the key features is subjected to a (k=N)-NN classifier to obtain The set of N′>N vectors of the key features includes the And the other N′-N vectors of the key features are obtained by applying the computer simulation to N′-N additional simulation samples.

16. The system of claim 9, wherein, to derive the intensity of the characteristic X-ray line, the processing circuit system is configured to fit a free curve to each interval of the measured spectrum, each interval being approximately centered around a corresponding characteristic X-ray line and consisting of a nearby portion of the characteristic X-ray line, thereby obtaining a corresponding optimized curve.

17. The system of claim 16, wherein the free curve is a sum of functions, the sum of functions comprising a convex function and a second function, the second function being a polynomial; and Wherein the processing circuit system is further configured to fit the convex function to the peak of the characteristic X-ray line around the corresponding measured spectrum as part of the fitting of the free curve, and to fit the second function so as to take into account the background intensity component of the corresponding measured spectrum.

18. The system of claim 1, wherein the X-ray detector is an energy dispersive X-ray spectrometer or a wavelength dispersive X-ray spectrometer.

19. The system of claim 1, wherein optionally, the tested sample is the patterned wafer, or is a part of the patterned wafer, in one of the manufacturing stages of the patterned wafer.

20. A method for non-destructive characterization of a sample, the method comprising: The measuring operation includes the following sub-operations for each of the one or more landing energies: Projecting an electron beam onto the sample being tested; as well as obtaining measurement data by measuring the intensity of X-rays emitted from the tested sample due to the electron beam penetrating the tested sample; as well as The measurement data analysis operation includes the following sub-operations: Extract a vector of values ​​of specified key features from the measurement data as well as based on and a set of vectors that simulate key features To determine the value assumed by one or more structural parameters associated with the structure under test where, for every 1≤n≤N, specifies the values ​​assumed by one or more structural parameters associated with the nth simulation sample, and the Each of the nth simulated samples is obtained by computer simulation of the emission of X-rays from the nth simulated sample due to the incident electron beam on the nth simulated sample at each of the one or more landing energies.

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