Measurement method, apparatus and computer program

Abstract

Disclosed is a metrology method, the metrology method comprising: obtaining metrology data associated with one or more targets on a substrate, the metrology data comprising intensity metric data associated with one or more intensity metrics and phase difference metric data associated with a phase difference metric; determining intensity asymmetry metric data from the intensity metric data, the intensity asymmetry metric data describing, for each of the intensity metrics, asymmetry of the intensity metric data between diffraction orders in a complementary diffraction order pair after diffraction by the one or more targets; one or more pre-calibrated asymmetry coefficients are obtained; and determining a value of a parameter of interest from the phase difference metric data and the intensity asymmetry metric data weighted by the pre-calibrated asymmetry coefficient.

Description

Cross-reference to related applications

[0001] This application claims priority to EP application 23170059.2, filed on April 26, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0002] The present invention relates to, for example, measurement methods and apparatus that can be used to manufacture devices using photolithography, and methods for manufacturing devices using photolithography. Background Technology

[0003] A photolithography apparatus is a machine that applies a desired pattern onto a substrate, typically onto a target portion of the substrate. For example, a photolithography apparatus can be used in the fabrication of integrated circuits (ICs). In this case, a patterning device (also called a mask or photomask) can be used to generate a circuit pattern to be formed on a separate layer of the IC. This pattern can then be transferred onto a target portion (e.g., a portion comprising one or more dies) on a substrate (e.g., a silicon wafer). The transfer of the pattern is typically via imaging onto a radiation-sensitive material (resist) layer provided on the substrate. Typically, a single substrate will contain a network of continuously patterned adjacent target portions. In photolithography processes, it is often necessary to measure the created structure, for example, for process control and verification. Various tools are known for performing such measurements, including scanning electron microscopes, which are commonly used to measure critical dimensions (CD), and specialized tools for measuring overlay, a measure of the alignment accuracy between two layers in the apparatus. Overlay can be described based on the degree of misalignment between two layers; for example, a measurement of overlay with a reference of 1 nm can describe a misalignment of 1 nm between two layers.

[0004] Recently, various forms of scatterometers have been developed for use in photolithography. These devices guide a radiation beam onto a target and measure one or more characteristics of the scattered radiation, such as the intensity at a single reflection angle as a function of wavelength; the intensity at one or more wavelengths as a function of the reflection angle; or the polarization as a function of the reflection angle—to obtain a “spectrum” from which the characteristics of interest of the target can be determined. The determination of the characteristics of interest can be performed using various techniques: for example, reconstructing the target using iterative methods such as rigorous coupled-wave analysis or the finite element method; library retrieval; and principal component analysis.

[0005] Traditional scatterometers use relatively large targets, such as 40 μm by 40 μm, where the grating and measurement beam generate points smaller than the grating (i.e., underfilled grating). This simplifies the mathematical reconstruction of the target, as it can be considered infinite. However, to reduce the target size to 10 μm by 10 μm or smaller, for example, to allow them to be positioned between product features rather than in scribing lines, measurement methods have been proposed where the grating size is smaller than the measurement point (i.e., overfilled grating). Typically, such targets are measured using dark-field scattering measurements, where zero-order diffraction (corresponding to specular reflection) is blocked, and only higher-order diffraction is processed. Examples of dark-field measurements can be found in international patent applications WO 2009 / 078708 and WO 2009 / 106279, the entire contents of which are incorporated herein by reference. Further developments of this technique are described in patent publications US20110027704A, US20110043791A, and US20120242940A. The contents of all these applications are also incorporated herein by reference. Diffraction-based overlay uses diffraction-order dark-field detection to perform overlay measurements on small targets. These targets can be smaller than the illumination point and can be surrounded by product structures on the wafer. The targets can include multiple gratings that can be measured in a single image.

[0006] In known metrology techniques, overlay measurements are obtained by measuring the target twice under specific conditions, while simultaneously rotating the target or changing the illumination or imaging mode to acquire the -1 and +1 diffraction order intensities, respectively. For a given target, the intensity asymmetry (i.e., the comparison of these diffraction order intensities) provides a measurement of target asymmetry (i.e., the asymmetry within the target). This asymmetry within the target can be used as an indicator of overlay (undesirable misalignment between two layers). Another known method measures the phase difference between dark-field images of two different types of sub-targets.

[0007] While known dark-field image-based overlay measurements are fast and computationally simple (once calibrated), they may rely on the assumption that layer misalignment (i.e., overlay errors and / or intentional deviations, if applicable) is the sole cause of measurement intensity asymmetry or phase difference. Any other contribution to the measured intensity asymmetry or phase difference (such as any process effects within one or both overlay gratings) will also contribute to the measurement asymmetry. Overlay-independent intensity asymmetry / phase difference contributions caused by process effects will obviously interfere with overlay measurements, leading to inaccuracies. Similar problems arise in alignment measurements due to asymmetry in the aligned target or marker being measured. Asymmetry in the bottommost or bottom grating of the target is a common form of process effect. It can originate from wafer processing steps such as chemical mechanical polishing (CMP) performed after the initial formation of the bottom grating.

[0008] Therefore, it is desirable to improve the accuracy of overlay and / or alignment measurements. Summary of the Invention

[0009] In a first aspect of the invention, a measurement method is provided, the method comprising: acquiring measurement data associated with one or more targets on a substrate, the measurement data including intensity measurement data associated with one or more intensity measures and phase difference measurement data associated with a phase difference measure; determining intensity asymmetry measurement data based on the intensity measurement data, the intensity asymmetry measurement data describing, for each of the intensity measures, an asymmetry between the intensity measures in a complementary diffraction order pair after diffraction by the one or more targets; acquiring one or more pre-calibrated asymmetry coefficients; and determining a value of a parameter of interest based on the phase difference measurement data and the intensity asymmetry measurement data weighted by the pre-calibrated asymmetry coefficients.

[0010] In a second aspect of the invention, a method for calibrating a plurality of asymmetry coefficients, each of which is associated with a corresponding intensity metric, is provided. The method includes: acquiring calibration measurement data associated with a plurality of targets on at least one calibration substrate, the calibration measurement data including uncorrected parameter of interest data and intensity metric data associated with the intensity metric; determining calibration intensity asymmetry metric data based on the intensity metric data, the calibration intensity asymmetry metric data describing, for each of the intensity metric data, the asymmetry between the intensity metric data in a complementary diffraction order pair after diffraction by the target; acquiring reference data associated with the parameter of interest; and optimizing the asymmetry coefficients to minimize the difference between the reference data and the uncorrected parameter of interest data when calibration is performed using a combination of the calibration intensity asymmetry metric data and the asymmetry coefficients.

[0011] On the other hand, a computer program comprising processor-readable instructions and a computer program carrier comprising such a computer program, which, when executed on a suitable processor-controlled device, causes the device to perform the method of the first aspect or the second aspect. The processor-controlled device may include a measuring device or a lithography device or its processor.

[0012] Other features and advantages of the invention, as well as the structure and operation of various embodiments thereof, are described in detail below with reference to the accompanying drawings. It should be noted that the invention is not limited to the specific embodiments described herein. Such embodiments are presented herein for illustrative purposes only. Other embodiments will be apparent to those skilled in the art based on the teachings contained herein. Attached Figure Description

[0013] Embodiments of the invention will now be described by way of example only with reference to the accompanying drawings, wherein: Figure 1 A photolithography apparatus according to an embodiment of the present invention is described; Figure 2 A photolithography unit or cluster is depicted according to an embodiment of the present invention; Figure 3 (a) A schematic diagram of a dark field scattering instrument for measuring a target using a first pair of illumination apertures according to an embodiment of the present invention; Figure 3 (b) shows the diffraction spectrum details of the target grating under a given illumination direction; Figure 3 (c) shows a second pair of illumination apertures that provide additional illumination modes when performing diffraction-based overlay measurements using a scatterometer; Figure 3 (d) shows a third pair of irradiation holes combining the first pair of holes and the second pair of holes; Figure 4 The profiles of a known type of multi-grating target and measurement points on a substrate were depicted; Figure 5 This is a flowchart of a calibration method for calibrating correction coefficients according to an embodiment; Figure 6 It shows that it can be obtained from Figure 4 Images and associated intensity signals acquired during target measurement; and Figure 7 This is a flowchart of the measurement method according to an embodiment. Detailed Implementation

[0014] Before describing the embodiments of the invention in detail, it is helpful to introduce example environments in which the embodiments of the invention may be implemented.

[0015] Figure 1 A lithography apparatus LA is schematically depicted. The apparatus includes: an illumination optics system (illuminator) IL configured to modulate a radiation beam B (e.g., UV radiation or DUV radiation); a patterning device support or support structure (e.g., mask stage) MT configured to support a patterning device (e.g., a mask) MA and connected to a first positioner PM configured to precisely position the patterning device according to certain parameters; a substrate stage (e.g., wafer stage) WT configured to hold a substrate (e.g., a resist-coated wafer) W and connected to a second positioner PW configured to precisely position the substrate according to certain parameters; and a projection optics system (e.g., a refractive projection lens system) PS configured to project a pattern imparted by the radiation beam B by the patterning device MA onto a target portion C (e.g., comprising one or more dies) of the substrate W.

[0016] Irradiation optical systems may include various types of optical components, such as refractive, reflective, magnetic, electromagnetic, electrostatic, or other types of optical components, or any combination thereof, which are used to guide, shape, or control radiation.

[0017] Patterning apparatus supports hold the patterning apparatus in a manner dependent on the orientation of the patterning apparatus, the design of the lithography device, and other conditions, such as whether the patterning apparatus is held in a vacuum environment. The patterning apparatus supports can use mechanical, vacuum, electrostatic, or other clamping techniques to secure the patterning apparatus. The patterning apparatus supports can be, for example, frames or tables, which may be fixed or movable as needed. The patterning apparatus supports ensure that the patterning apparatus is, for example, in the desired position relative to the projection system. The term "mask" or "mask" as used herein can be considered synonymous with the more general term "patterning apparatus."

[0018] The term "patterning device" as used herein should be interpreted broadly as any device that can be used to impart a pattern to a radiation beam in its cross-section (such as creating a pattern in a target portion of a substrate). It should be noted that, for example, if the pattern includes phase-shifting features or so-called auxiliary features, the pattern applied to the radiation beam may not perfectly correspond to the desired pattern in the target portion of the substrate. Typically, the pattern applied to the radiation beam will correspond to a specific functional layer in a device (such as an integrated circuit) created in the target portion.

[0019] Patterning apparatus can be transmissive or reflective. Examples of patterning apparatus include masks, programmable mirror arrays, and programmable LCD panels. Masks are well-known in photolithography and include mask types such as binary, alternating phase-shift, and attenuation phase-shift masks, as well as various hybrid mask types. One example of a programmable mirror array employs a matrix arrangement of small mirrors, each of which can be individually tilted to reflect an incident radiation beam in different directions. The tilted mirrors create a pattern in the radiation beam, which is then reflected by the mirror matrix.

[0020] As shown in the figure, the device is transmissive (e.g., using a transmissive mask). Alternatively, the device can be reflective (e.g., using a programmable mirror array of the type described above, or using a reflective mask).

[0021] Photolithography apparatuses can also be of the type in which at least a portion of the substrate can be covered by a liquid (e.g., water) with a relatively high refractive index to fill the space between the projection system and the substrate. Immersion liquids can also be applied to other spaces within the photolithography apparatus, such as the space between the mask and the projection system. Immersion techniques are well known in the art for increasing the numerical aperture of a projection system. The term "immersion" as used herein does not imply that the structure (such as the substrate) must be submerged in the liquid, but simply that the liquid is located between the projection system and the substrate during the exposure process.

[0022] refer to Figure 1 The irradiator IL receives the radiation beam from the radiation source SO. The source and the lithography apparatus can be separate entities, for example, when the source is an excimer laser. In this case, the source is not considered part of the lithography apparatus, and the radiation beam is delivered from the source SO to the irradiator IL with the aid of a beam delivery system BD, which includes, for example, suitable guide mirrors and / or beam expanders. In other cases, the source can be a component of the lithography apparatus, for example, when the source is a mercury lamp. The source SO and the irradiator IL, along with the beam delivery system BD (if desired), can be referred to as the radiation system.

[0023] The irradiator IL may include an adjuster AD for adjusting the angular intensity distribution of the radiation beam. Typically, at least the outer and / or inner radial ranges of the intensity distribution in the pupil plane of the irradiator (typically referred to as outer σ and inner σ, respectively) can be adjusted. Furthermore, the irradiator IL may include various other components, such as an integrator In and a concentrator CO. The irradiator can be used to adjust the radiation beam to achieve a desired uniformity and intensity distribution in its cross-section.

[0024] A radiation beam B is incident on a patterning device (e.g., a mask) MA held on a patterning device support (e.g., a mask stage MT) and patterned by the patterning device. After passing through the patterning device (e.g., the mask) MA, the radiation beam B passes through a projection optics system PS, which focuses the beam onto a target portion C of a substrate W, thereby projecting an image of the pattern onto the target portion C. With the aid of a second positioner PW and a position sensor IF (e.g., an interferometer, a linear encoder, a 2-D encoder, or a capacitive sensor), the substrate stage WT can be moved precisely, for example, to position different target portions C within the path of the radiation beam B. Similarly, the first positioner PM and another position sensor ( Figure 1 (Not explicitly shown) can be used to precisely position a patterning device relative to the path of the radiation beam B, for example, after a mechanical retrieval from a mask library, or during scanning.

[0025] Patterning devices (e.g., masks) MA and substrates W can be aligned using mask alignment marks M1, M2 and substrate alignment marks P1, P2. Although the substrate alignment marks shown occupy dedicated target portions, they can be located in the space between target portions (these are called scribe line alignment marks). Similarly, where the patterning device (e.g., mask) MA provides more than one die, mask alignment marks can be located between the dies. Small alignment marks can also be included within device features within the die, in which case it is desirable that the marks be as small as possible and that different imaging or process conditions than adjacent features are not required. Alignment systems for detecting alignment marks will be described further below.

[0026] The lithography apparatus LA in this example is a so-called two-stage type, featuring two substrate stages WTa and WTb and two stations—an exposure station and a measurement station—between which the substrate stages can be exchanged. While one substrate on one stage is exposed at the exposure station, another substrate can be loaded onto the other stage at the measurement station, and various preparation steps are performed. Preparation steps may include using a leveling sensor LS to map surface control of the substrate and using an alignment sensor AS to measure the position of alignment marks on the substrate. This can significantly increase the apparatus's throughput.

[0027] The depicted apparatus can be used in various modes, including, for example, stepping mode or scanning mode. The construction and operation of the lithography apparatus are well known to those skilled in the art and do not require further description for understanding the present invention.

[0028] like Figure 2 As shown, the lithography unit LA forms part of the lithography system and is referred to as a lithography cell LC or lithography unit or cluster. The lithography cell LC may also include devices for performing pre-exposure and post-exposure processes on the substrate. Traditionally, these include a spin coater SC for depositing a resist layer, a developer DE for developing the exposed resist, a cooling plate CH, and a baking plate BK. A substrate processor or robot RO picks up substrates from input / output ports I / O1, I / O2, moves them between different processing devices, and then delivers them to the loading area LB of the lithography unit. These devices (often collectively referred to as tracks) are controlled by a track control unit TCU, which in turn is controlled by a monitoring system SCS, which in turn controls the lithography unit via the lithography control unit LACU. Therefore, different devices can be operated to maximize throughput and processing efficiency.

[0029] Measurement devices applicable to embodiments of the present invention, such as Figure 3 As shown in (A). Figure 3(b) describes the target T and the measurement radiation diffraction rays used to illuminate the target in more detail. The measurement device shown is a type known as a dark-field measurement device. The measurement device can be a stand-alone device or incorporated into a lithography device LA, for example at a measurement station, or included in a lithography unit LC. The optical axis with multiple branches throughout the device is indicated by the dashed line O. In this device, light emitted by the light source 11 (e.g., a xenon lamp) is guided onto the substrate W by an optical system including lenses 12, 14 and objective lens 16 via a beam splitter 15. These lenses are arranged in a double sequence of 4F arrangement. Different lens arrangements can be used, provided that it still provides a substrate image to the detector while allowing access to the intermediate pupil plane for spatial frequency filtering. Thus, the range of angles at which radiation is incident on the substrate can be selected by defining the spatial intensity distribution in the plane that presents the spatial spectrum of the substrate plane (here referred to as the (conjugate) pupil plane). In particular, this can be achieved by inserting a suitable aperture plate 13 between lenses 12 and 14 in the plane of the back-projected image, which serves as the objective pupil plane. In the example shown, the aperture plate 13 has different forms, labeled 13N and 13S, to allow for the selection of different illumination modes. The illumination system in this example forms an off-axis illumination mode. In the first illumination mode, aperture plate 13N provides off-axis illumination in a direction designated "north" for illustrative purposes only. In the second illumination mode, aperture plate 13S is used to provide similar illumination, but in the opposite direction, labeled "south". Other illumination modes can be achieved by using different apertures, such as those capable of simultaneous illumination and detection from two opposite directions, and combined with optical wedges to separate the resulting image. The remainder of the pupil plane is preferably dark, as any unwanted light outside the desired illumination mode will interfere with the desired measurement signal.

[0030] like Figure 3 As shown in (b), the target T is positioned such that the substrate W is perpendicular to the optical axis O of the objective lens 16. The substrate W may be supported by a support (not shown). The ray of measurement radiation I incident on the target T from an angle offset from axis O produces a zero-order ray (solid line 0) and two first-order rays (dotted line +1 and double-dotted line -1). It should be remembered that for an overfilled small target, these rays are just one of many parallel rays covering the substrate area (including the measurement target T and other features). Due to the limited width of the aperture in plate 13 (to allow for a useful amount of light), the incident ray I will actually occupy an angular range, and the diffracted rays 0 and +1 / -1 will be slightly diffused. Depending on the point spread function of the small target, each order of +1 and -1 will be further diffused within an angular range, rather than a single ideal ray as shown. Note that the grating pitch and illumination angle of the target can be designed or adjusted so that the first-order rays entering the objective lens are closely aligned with the central optical axis. Figure 3 (a) and Figure 3 The light rays shown in (b) are slightly off-axis, purely to make them easily distinguishable in the diagram.

[0031] At least the 0th and +1st orders of the target T diffraction on the substrate W are collected by objective lens 16 and guided back by beam splitter 15. Back Figure 3 (a) Both the first and second illumination modes are shown by specifying diameter-opposite apertures marked North (N) and South (S). When the incident ray I for measuring radiation comes from the north side of the optical axis, i.e., when the first illumination mode is applied using aperture plate 13N, the +1 diffracted ray marked +1 (N) enters the objective lens 16. Conversely, when the second illumination mode is applied using aperture plate 13S, the -1 diffracted ray (marked 1 (S)) is the ray entering the lens 16.

[0032] The second beam splitter 17 divides the diffracted beam into two measurement branches. In the first measurement branch, the optical system 18 uses the zeroth and first-order diffracted beams to form the diffraction spectrum (pupil plane image) of the target on the first sensor 19 (e.g., a CCD or CMOS sensor). Each diffraction order hits a different point on the sensor, so image processing can compare and contrast each order. The pupil plane image captured by the sensor 19 can be used for focusing measurement devices and / or for normalizing the intensity measurement of the first-order beam. The pupil plane image can also be used for many measurement purposes, such as reconstruction.

[0033] In the second measurement branch, optical systems 20 and 22 form an image of the target T on sensor 23 (e.g., a CCD or CMOS sensor). In this second measurement branch, aperture stop 21 is positioned in a plane conjugate to the pupil plane. Aperture stop 21 blocks the zero-order diffracted beam, ensuring that the target image formed on sensor 23 is formed only by -1 or +1 order beams. The image captured by sensors 19 and 23 is output to processor PU, which processes the image, the function of which will depend on the specific type of measurement being performed. Note that the term "image" used here is broad. If only one of the -1 or +1 orders is present, an image of the grating lines will not be formed.

[0034] Figure 3 The specific forms of the aperture plate 13 and field stop 21 shown are purely illustrative. In another embodiment of the invention, coaxial illumination of the target is used, and an aperture stop with an off-axis aperture is used to deliver essentially only a first-order diffracted beam to the sensor. In other embodiments, second-, third-, and higher-order beams can be used in the measurement. Figure 3 (not shown in the image) to replace or supplement a first-order beam.

[0035] To accommodate these different types of measurements, the orifice plate 13 can include multiple aperture patterns formed around a disk, which is rotated to position the desired pattern. Note that orifice plates 13N or 13S can only be used to measure gratings oriented in one direction (X or Y, depending on the setup). For measurements of orthogonal gratings, the target can be rotated 90° and 270°. Different orifice plates, such as... Figure 3 (c) and Figure 3 As shown in (d). The use of these devices, as well as many other variations and applications of the devices, are described in the aforementioned previously disclosed applications.

[0036] Figure 4 This is an example target on wafer W, which can be measured using a known diffraction-based overlay (DBO) measurement method. This measurement method may result in overfilling, causing the entire target to be trapped within the measurement point 31. In this DBO measurement method, the measured asymmetry signal can be a phase difference asymmetry from a pair of complementary sub-targets (a first type of sub-target or "M pad" and a second type of sub-target or "W pad"). In the example target shown, there are two pairs of such sub-targets: the first pair 32, 34 are oriented along a first direction along the substrate plane, and the second pair 33, 35 are oriented along a second direction along the substrate plane. Each sub-target includes an overlay periodic structure or grating in the corresponding layer where its overlay value is to be measured. Compared to the more common µDBO targets (where each layer has the same pitch), such... Figure 4 The sub-target shown has a grating with a different pitch in each of the two layers.

[0037] More specifically, such as Figure 4 The targets shown include arrangements of two different types of sub-targets (e.g., in each direction): "M pads" or "M sub-gratings" 34, 35, which include a bottom grating with a pitch of p 1 is less than the pitch of the top grating p 2; and “W pad” or “W sub-grating” 32, 33, which have an opposite grating (i.e., it can have the same pitch as the M pad, but a larger pitch in the top layer). p 2. Although strictly speaking, the two sub-targets do not need to have the same pitch. This is shown in the cross-sectional details of one of these pairs 32 and 34. In this way, the target bias varies continuously along each target. The overlay signal is encoded in the final imaged moiré pattern or intensity fringes (e.g., a dark-field image of the diffracted radiation from the sub-targets). The phase can be measured from each image of the fringe positions within the target's region of interest. Note that the actual arrangement of these sub-targets may differ from that shown.

[0038] In this method, the asymmetric signal AThis can be defined as the phase difference between the diffraction order of the "M pad" and the corresponding diffraction order of the "W pad," for example, (For example, sometimes referred to as a normal image) or (For example, sometimes called complementary images), where , These are the measured phase differences between the "M pad" and "W pad" of the +1 and -1 diffraction orders, respectively (other diffraction orders can be used). Optionally, the two diffraction orders of a complementary diffraction order pair can be summed: for example, Therefore, it can be understood that the concepts described in this paper apply to different types of asymmetric signals. This measurement principle is described in Matsunobu et al., “Novel diffraction-based overlay metrology utilizing phase-based overlay for improved robustness” (Proc. SPIE 11611, Metrology, Inspection, and Process Control for Semiconductor Manufacturing XXXV, 1161126 (February 22, 2021)) (incorporated hereby by reference).

[0039] Traditionally, for such targets, overlay engraving OV The phase difference between the M-pad target image and the W-pad target image can be extracted using the following equation, for example, Fourier plane images from the +1 and -1 diffraction orders, respectively: (1) Although, as mentioned earlier, the phase signal within the square brackets can also be or Only one of them.

[0040] Therefore, at least under a reasonable approximation of the commonly encountered overlay amplitude, overlay OV and asymmetric signals... A The relationship is linear, assuming the target is perfect and that overlay is the only asymmetric factor. In reality, the target is affected by various process variations and the resulting unnecessary asymmetries, which are indistinguishable from overlay using the aforementioned traditional techniques.

[0041] WO2022008135A1 (incorporated herein by reference) describes a method for addressing target process variations in the context of μDBO (where overlay is inferred from the asymmetric imbalance of complementary diffraction orders of offset targets with the same pitch in (typically) two layers). In WO2022008135A1, it is assumed that all measurements should be identical, and any variation is due to other target asymmetries. Therefore, it is assumed that the true values ​​of the parameters of interest (e.g., overlay or alignment positions) are universal for all measurements. Thus, multiple measurements are obtained from all targets located in the same vicinity or on the same wafer (e.g., more than one target). These concepts are based on the observation that, for a given measurement formulation or setup, the measured overlay of the target exhibits a significant linear correlation with either a target-related process asymmetry metric or a non-overlay asymmetry metric. A concrete example of the disclosed process asymmetry metric is the "distance to origin" (DTO), which is the distance the overlay regresses to the origin in the "asymmetry space" (more details can be found in the aforementioned publication). Therefore, this method involves finding a relationship relating the true overlay to a measure of process asymmetry (e.g., a scaling constant for the linear example described below, although other functions describing more complex relationships are also possible). Since the true overlay is unknown, the method is based on performing optimizations to minimize recipe-to-recipe, target-to-target (when measuring multiple targets), and optional polarization-to-polarization differences (e.g., in the same vicinity on the wafer) between the measured overlay value and a measurement expected to have the same true overlay. For example, in the same vicinity, they can be spaced no more than 1.5 mm or 1 mm apart (e.g., a distance between 10 µm and 1.5 mm).

[0042] Therefore, in a case study, it is recommended that all targets satisfy the following relationship: (2) in It is a true overlay. This refers to the measurement overlay, where C is a constant, and DTO is the distance to the origin as described above (or another non-overlay asymmetry measure). N and P refer to the target and polarization, such that all parameters except for the actual overlay depend on the target and measurement conditions (e.g., different measurement conditions can vary in one or more aspects such as wavelength, bandwidth, polarization, and angle of incidence). Assume... This is independent of the measurement conditions being optimized (e.g., all conditions are within a specific region or the distance between them). However, there are multiple such regions on the wafer to establish the correlation defined by equation (2). As previously stated, the linear relationship described herein is merely an example, and the proposed method can utilize other predictable relationships between overlay and (non-overlay) asymmetry measures. Although only one non-overlay asymmetry measure is mentioned in equation (2), it should be understood that equation (2) can be extended to include more than one non-overlay asymmetry measure.

[0043] The main assumption in WO2022008135A1 is that the actual overlay... The change should be zero (provided the target is close enough); however, multiple measurement overlay values ​​exist from different regions of the wafer. Therefore, an optimization method is proposed to find... , making The change is very small (minimized). This can be achieved by making all individual values ​​at a certain location... This is achieved by minimizing the value difference (e.g., for 3 objectives, if all differences are used, two polarizations provide 15 such differences).

[0044] Equation 1 can be restated in a generalized form. (3) in y is the true value of the parameter of interest, x is the measured value of the parameter of interest, c is the constant to be found, and z is the asymmetric offset term, which can be the DTO term in the overlay example; , N This is the total number of measurement conditions (e.g., target / polarization combinations). For example, for an example of two polarizations and three different markers or marker types, N It could be 6.

[0045] WO2022008135A1 also discloses a specific alignment embodiment, which is based on the same fundamental assumption that, for all measurements of one or more targets (alignment marks) in the same vicinity, there is only one true alignment position. There are also asymmetry measures used for alignment. This can be used in place of DTO determination. Such asymmetry measures can include, for example, color-to-color asymmetry measures, intensity difference measures (difference between two complementary diffraction orders), (bottom grating) asymmetry measurements using another device (such as a scatterometer that can be used for overlay measurements), marker deformation estimates from external algorithms (such as Kramers-Kronig type inference schemes), or derived estimates such as the derivative of the diffraction order intensity difference or the ratio relative to the wavelength. In such an example, equation (2) becomes: (4) in It is the alignment value measured.

[0046] Improvements to the teachings of WO2022008135A1 will be described, primarily in the context of using DBO measurements of the target, such as Figure 4 As shown, while some aspects (particularly calibration) are more generally applicable to other measurement techniques, including μDBO, the proposed method, according to one embodiment, has the advantage of not requiring measurements of each target using different measurement conditions (e.g., wavelength and / or polarization), although measurement data associated with different measurement conditions can still be used. This advantage is obtained due to calibration based on reliable reference data. However, such external reference data is not strictly required, and other embodiments can use reference data of the same type as phase calibration data (e.g., associated with different measurement conditions, such as different wavelengths and / or polarizations).

[0047] This concept includes acquiring with one or more Figure 4 Measurement data related to the type of target or other targets, from which phase metrics can be measured, such as those obtained from the imaging intensity fringes of the target's diffraction radiation, are used to measure parameters of interest, such as overlay (the position of the second layer relative to the first layer; note that the terms first and second layers do not exclude the existence of intermediate layers) or position. The method may include determining, based on the measurement data, both an intensity asymmetry metric value and a phase difference metric value for each of one or more intensity asymmetry metrics.

[0048] Phase difference metrics can be obtained from the positional difference of such fringes in a pair of different sub-target types (e.g., M pads and W pads) and can include original values ​​of the parameters of interest (e.g., original overlay values, for example determined according to equation (1)). Each intensity asymmetry metric can be related to the imbalance or asymmetry of one or more intensity metrics between diffraction orders from the sub-target or between a pair of complementary diffraction orders (e.g., +1 and -1 orders) of each sub-target, where each intensity metric is related to the intensity or amplitude of the fringe.

[0049] Using predetermined or pre-calibrated asymmetry coefficients, the corrected values ​​of the parameter of interest can be obtained from the phase difference metric (e.g., the measurement overlay determined according to equation (1)) and the intensity asymmetry metric, wherein the pre-calibrated asymmetry coefficients include the corresponding pre-calibrated asymmetry coefficient for each intensity asymmetry metric (where each intensity asymmetry metric can be derived from the corresponding different intensity metric). For example, the corrected values ​​of the parameter of interest may include the linear sum of the phase difference metric and the product of each intensity asymmetry metric and its corresponding asymmetry coefficient.

[0050] The asymmetry coefficient can be calibrated during the calibration phase. When using, for example... Figure 4 In the case of the target (or alignment context) shown, the calibration phase may include acquiring calibration data from each of a plurality of such targets (or calibration markers), which includes phase calibration data related to phase difference metrics, such as uncorrected or raw phase calibration data (e.g., uncorrected parameter of interest data (e.g., acquired according to equation (1)) and intensity calibration data (related to intensity asymmetry metrics). However, this calibration method is also applicable to other measurement techniques, such as μDBO, and in this case, the calibration data may include uncorrected parameter of interest or overlay data determined according to conventional μDBO techniques, along with additional intensity asymmetry metrics.

[0051] Back Figure 4 Within the target context, different intensity asymmetry metrics can be determined from various intensity metrics and their combinations. These metrics describe the imbalance in diffraction order (e.g., fringe) intensity between diffraction orders in complementary diffraction order pairs for each target and / or each of its sub-targets. Reference data, such as reliable reference data for parameters of interest, can also be acquired. Reference data can be obtained from the same target as the target for which calibration data is being measured, or from a nearby reference target / structure that is also nearby as the target for which calibration data is being measured. "Same nearby" can describe, for example, an interval not exceeding 1.5 mm or 1 mm (e.g., an interval distance between 10 µm and 1.5 mm).

[0052] During the calibration phase, asymmetry coefficients (for each intensity asymmetry measure) can be found. When these asymmetry coefficients are added to the intensity calibration data weighted by the asymmetry coefficients, they minimize the difference between the phase calibration data and the reference data.

[0053] Optionally, the calibration phase may additionally train one or more model term coefficients, where each corresponding model term describes a known spatial pattern or fingerprint on the wafer or a portion thereof (e.g., a field). The model terms can be described by functions that depend only on the wafer or field location, i.e., not on the measurement data. Such functions can be any suitable mathematical function describing a two-dimensional shape, such as a polynomial, Fourier, or Zernike function, and can describe known shapes encountered in metrology. More specifically, these model terms can be used to model any “device metrology” offset. For example, any error resulting from differences between the measurement methods, tools, and / or targets used when acquiring calibration / metrology data and those used when acquiring reference data (e.g., measurement data involving multiple wafers).

[0054] Optionally, the filter can be applied to asymmetry metric data, (uncorrected) phase metric data, and / or reference data, for example, as part of an evaluation function / optimization function during calibration, or as a data processing step prior to optimization. The filter may include, for example, a smoothing filter that facilitates smoothing the solution and / or an evaluation function filter for modifying the evaluation function being optimized. For example, the evaluation function filter may define specific weights for optimization (e.g., weights for each location / region), thereby making the learning adaptive.

[0055] Optionally, the learning can make the asymmetry coefficients location-dependent (e.g., at each location scale or other regional scale, such as edge locations versus center locations). This can be achieved by defining a suitable model matrix that is combined with the asymmetry metric data, thereby optimizing the location-dependent asymmetry coefficients. As an alternative or supplement to location-dependent coefficients, asymmetry coefficients with other dependencies, such as context-dependent coefficients, can be defined. Examples of context-dependent coefficients can include, for example, asymmetry coefficients that can be defined using chuck-dependent coefficients (e.g., a lithography chuck used during any grating exposure of the target), etching chamber-dependent coefficients (e.g., an etching chamber / tool ​​used to etch any grating of the target), and / or CMP tool-dependent coefficients (e.g., a CMP tool used for polishing).

[0056] Figure 5 This is a flowchart describing such a calibration method according to an embodiment. In step 500, calibration measurement data can be obtained from one or more calibration wafers, each wafer including multiple measurement targets (e.g., μDBO, ...). Figure 4 (Target or alignment target / marker). The calibration measurement data in the specific example to be described can be related to... Figure 4 Type-specific DBO measurements include calibrated phase difference metrics and / or parameter of interest (POI) data, as well as calibrated intensity metrics. The calibrated phase difference and intensity metrics can be obtained from images (e.g., dark fields) of individual diffraction orders of radiation diffracted from such targets (with regions of interest defined for individual sub-targets). Each image of a sub-target includes moiré fringes due to the different pitches within the sub-)targets. Phase difference metrics can be obtained by the positions of these fringes for M and W sub-target pairs, while intensity metrics can be correlated with the fringe intensities in these images.

[0057] The calibration phase difference metric data and / or parameter of interest data may describe or relate to the phase difference between diffracted radiations from multiple targets of one or more calibration wafers from M pads and W pads (e.g., each pad corresponds to a diffraction order or is averaged over more than one corresponding diffraction order), or uncorrected parameter of interest data (e.g., uncorrected overlay or position data) determined therefrom, for example, which may be conventionally determined from the position of imaging fringes according to known DBO (or alignment) methods. In a particular embodiment, the calibration phase difference metric data and / or parameter of interest data may be, for example, raw or uncorrected overlay data determined according to equation (1).

[0058] Figure 6 It shows that it can be obtained from Figure 4 Image IM is acquired from imaging a single diffraction order of a sub-target of a type of target. This image includes multiple moiré fringes FR, providing periodic or sinusoidal intensity signals corresponding to the periodic orientation of the sub-target. A mapping of intensity I within the region of interest (ROI) versus image location (x-direction as shown in the figure) is also shown (images are typically processed to use only portions of the image within the ROI (e.g., the central region) of each sub-target to mitigate target edge effects and the influence of surrounding structures). The maximum intensity is marked on this map. Average strength and minimum strength These values ​​can be used to determine the various strength measures and strength asymmetry measures described in this paper.

[0059] Some specific examples of intensity measures will now be described, which act as proxies for diffraction intensity or amplitude signals. Calibration intensity asymmetry measurement data can include any one or more of these measures. An intensity measure can be a function of the maximum, minimum, or mean fringe intensity (although any other fringe intensity measure is possible), determined based on the diffraction order of the complementary pair. For example, intensity measures... It can include metrics ... Any one of the metrics, where It is the intensity measure of the first diffraction order (e.g., +1) in a complementary pair. It is the intensity measure of the second diffraction order (i.e., -1st order) in a complementary pair: Therefore, the intensity metric can describe the average intensity, amplitude intensity, and / or diffraction efficiency of the sub-grating and / or the primary grating, and can optionally be normalized.

[0060] In step 510, the calibration intensity measurement data can be used as a basis. Determine calibration intensity asymmetry measurement data a Calibrated intensity asymmetry metrics can describe the intensity or amplitude imbalance between complementary pairs of the same diffraction order. Therefore, for each intensity metric, the intensity imbalance metric can include the imbalance metric between the +1 and -1 orders, i.e., typically... , where k can be any one of 1 to 8 above (or any other suitable metric). For example, the first intensity asymmetry metric. Datasets can be based on , To determine another measure of intensity asymmetry Datasets can be based on , This can be determined for any or all of the aforementioned intensity measures (or other measures); for example, the intensity measure determined for calibrating measurement data. ... Any subset or all of.

[0061] Calibration intensity asymmetry metrics can include, or be based on, differences or ratios of intensity asymmetry metrics such as +1 or -1 diffraction orders. Specific examples may include, among others:

[0062] In step 520, a post-processing step may be applied. This step is optional, particularly useful when calibration uses a linear evaluation function to determine linear correction. This is because nonlinearity may arise if the target deformation becomes large. This step may include determining one or more additional (e.g., power) terms in a Taylor-like expansion. ,in It can be one or more of the following (and assume) (Already confirmed) etc.

[0063] In step 530, reference data may be acquired, such as reliable or known values ​​of the parameters of interest for (multiple) calibration wafers. For example, such reference data may be measured using known scattering measurement techniques (e.g., correcting for process variation asymmetries) and / or scanning electron microscopy / electron beam metrology.

[0064] In step 540, optimization issues can be identified to ensure that the calibrated phase difference measurement data (e.g., raw / uncorrected overlay data) is accurate. ) and (reliable) reference data The difference between them is minimized. In one embodiment, there can be at least four intensity asymmetry measures and therefore four coefficients. c 1. c 2. c 3. c 4. Calibration is required. From a performance perspective, this may be preferred because the target (by direction) can include two sub-targets, each including two gratings. However, within the scope of this disclosure, models based on fewer (at least one) or more intensity asymmetry measures can be used because even with one or two intensity asymmetry measures, calibration can improve the performance of the original parameter of interest data.

[0065] In one embodiment, the model or evaluation function to be optimized can be a linear function, for example: Or more generally: Or, for four intensity metric examples, in matrix form (for one calibration wafer)... )express: Each of these chips has i Locations ( x and y (This represents the orientation of the sub-objective). However, it is understandable that other, more complex (non-linear) models can also be used. The coefficients can be determined. c 1- c k It makes the reference data or reliable parameter data of interest. Compared with calibrated phase difference measurement data (e.g., raw / uncorrected overlay data) Minimize the differences between them.

[0066] If there is more than one calibration chip (e.g., l Each chip, waf 1- wafl Then the evaluation function can be, for example:

[0067] However, it will be understood that the sampling locations (e.g., positions and / or numbers) may differ between different wafers. Those skilled in the art will recognize how the evaluation function can be adjusted accordingly.

[0068] In the embodiment of performing step 520, the single-wafer asymmetry metric matrix ... Each may include additional columns for each additional asymmetry term (e.g., additional columns for some or all of the following: , , , And / or additional columns for other exponentiation).

[0069] The optimization in step 540 can use any standard optimization technique, such as least squares optimization. Alternatively or additionally, the optimization can be optionally regularized, such as L1-regularized least squares optimization or L2-regularized least squares optimization.

[0070] In one embodiment, the evaluation function may include a wafer model matrix. It includes one or more additional model terms to train the coefficients of the corresponding model terms. - (For example, included within matrix c), where each model term describes a known spatial pattern or fingerprint on a wafer or a portion thereof (e.g., a field). This allows optimization to collectively optimize the static error fingerprint or the metrology-to-device (MTD) fingerprint (e.g., this could be the same for all wafers, not a function of measurement data / observations, but simply their spatial location). Wafer model term index n The number of (e.g., orthogonal) modes used to model the static error of this MTD is described. n It can be in the range of 6 to 50.

[0071] In this embodiment, the evaluation function can take the following form (for example):

[0072] The concept of a wafer model matrix can be extended to define model matrices for different contexts, such as different wafer groups grouped according to the chuck used for exposure, the CMP tools used, and / or the etching chamber used. Therefore, a corresponding model matrix can be applied to each wafer group. Other known MTD processing techniques for modeling MTD fingerprints can be used.

[0073] Preferably, the sum of all (interpolated) locations on the calibration wafer should (e.g., much) be greater than the wafer model item index. n Furthermore, in this embodiment, calibration data from multiple calibration wafers is preferred to better tune the matrix; multiple wafers help capture the “average” (static) error fingerprint through model term coefficients and reconstruct the dynamic error fingerprint through intensity asymmetry measures.

[0074] Any suitable function can generate the wafer model matrix. The model items included. Chip model matrix. It can include any number of model terms, for example, each model term describes a specific known or expected shape, and for shapes that do not exist, the corresponding coefficients will simply become zero.

[0075] As an alternative to applying the wafer model matrix to actual optimization, the same (or similar) results can be obtained by applying the model terms as a preprocessing step to the calibration measurement data before optimization.

[0076] For example, after all locations on each wafer have been measured, one or more filter terms can be added to the optimized evaluation function in step 540. This includes one or more wafer model terms (in addition to the wafer model matrix). In addition to the chip model term, it can also include (multiple) filter terms, or, in the absence of a chip model term, include (multiple) filter terms individually. One such filter term can be used to preprocess asymmetry, for example, by applying a spatial smoothing filter (e.g., an outlier removal filter) to smooth the intensity asymmetry metric data. Thus, the smoothing filter can provide noise reduction through spatial averaging. Of course, such a smoothing filter can also be applied to preprocess phase difference metric data (e.g., raw / unprocessed parameters of interest / overlay data).

[0077] Alternatively or additionally, evaluation filters can be provided to modify the evaluation function, for example, to make the learning adaptive. Such evaluation filters can apply weights to the optimization (e.g., weighted per location / region). By way of concrete examples, such an evaluation filter can select a specific aspect of the MTD error to be minimized (e.g., non-binary weighting only at wafer edges, only within fields, only between fields, or between such schemes). In another example, if the sampling schemes used to obtain calibration measurement data and reference data are different, the evaluation filter can select between two sampling schemes. In yet another example, such a filter can implement a "compliant die" evaluation function that aims to maximize the number of compliant dies, rather than applying average optimization across the entire substrate (e.g., least-squares optimization). Therefore, "compliant die" optimization uses prior knowledge of the product (die layout) when optimizing process parameters. Least-squares optimization typically treats each location equally, regardless of die layout. Therefore, least squares optimization can be more inclined to result in "only" four non-specification locations, but each location is corrected in a different die, rather than seven non-specification locations, but only affecting the correction of two dies (e.g., one die has four defects and the other has three defects). However, from a yield perspective, the latter is clearly preferable.

[0078] These are just examples of filters that can be applied; in principle, any suitable filter can be applied.

[0079] In this embodiment, the evaluation function can take the following form (for example): As mentioned earlier, the filter matrix Can be independent of the wafer model matrix This is achieved. In one embodiment, the filter matrix... It can include an evaluation matrix and pseudo-inverse smoothing matrix The product of; that is .

[0080] In one embodiment, an additional model matrix can be applied, which can be used to train location-related or region-related coefficients (i.e., matrix c can be extended to include location-related coefficients). This embodiment can be adopted when there are different asymmetric patterns that can be spatially separated. Naturally, such an embodiment increases the complexity of the model because there are now k×u coefficients to train instead of k (k=4 in the specific example given in this section), where u is the number of patterns (locations / regions to be processed individually).

[0081] Finally, the output of this method, 550, is the calibration coefficient matrix c (e.g., the calibration coefficients in a given specific example).c 1 to c 4) and optional calibration model term coefficients. to (If applicable).

[0082] Figure 7 This is a flowchart of a method for performing measurements using the concepts disclosed herein. The acquired measurement data 700 includes phase difference measurement data. (For example, parameters of interest or overlay measurement data measured from one or more DBO targets according to equation (1)) and intensity asymmetry measurement data (related to one or more intensity asymmetry measures determined from stripe intensity using the methods already described). One or more pre-calibrated coefficients 710 (corresponding to the number of intensity asymmetry measures described in the intensity asymmetry measurement data) were also obtained, for example, using... Figure 5 The coefficients are obtained using this method. The pre-calibrated coefficients 710 can be applied to the intensity asymmetry measurement data to correct the phase difference measurement data. This allows for the determination of corrected phase difference measurement data. 730 (e.g., corrected parameters of interest or overlay data); for example, based on (example using a four-intensity asymmetry metric):

[0083] Optionally, in step 720, the corrected phase difference measurement data The equation can also include the coefficients of its corresponding model terms. to Weighted model terms ,Right now This can compensate for static MTD errors. This can also be done both outside the measurement tool and inside the control feedback loop.

[0084] While the targets described above are measurement targets specifically designed and formed for measurement purposes, in other embodiments, characteristics can be measured on targets that are functional components of devices formed on a substrate. Many devices have regular grating-like structures. The terms "target grating" and "target" as used herein do not require that the structure be specifically provided for the measurement in progress. Furthermore, the pitch P of the measurement target is close to the resolution limit of the optical system of the scattering instrument, but can be much larger than the size of a typical product feature fabricated by photolithography in the target portion C. In practice, the lines and / or spaces of the overlay grating within the target can include smaller structures with dimensions similar to those of the product feature.

[0085] In relation to the physical grating structure of the target realized on the substrate and patterning device, one embodiment may include a computer program containing one or more machine-readable sequences of instructions describing methods for measuring the target on the substrate and / or analyzing the measurements to obtain information about the lithography process. For example, the computer program may... Figure 3 Unit PU and / or in the device Figure 2 The computer program is executed within the LACU (Large Control Unit). A data storage medium (e.g., semiconductor memory, disk, or optical disk) storing this computer program may also be provided. If an existing measuring device (e.g., Figure 3 The present invention (which is of the type shown) is already in production and / or use, and can be implemented by providing an updated computer program product that enables a processor to execute the methods disclosed herein.

[0086] Although the embodiments disclosed above are described based on diffraction-based overlay measurements (e.g., using...), Figure 3 (a) Measurements performed by the second measurement branch of the apparatus shown, but in principle, the same model can be used for pupil-based overlay measurements (e.g., using...). Figure 3 (b) Measurements performed by the first measurement branch of the apparatus shown. Therefore, it should be understood that the concepts described herein also apply to diffraction-based overlay measurements and pupil-based overlay measurements.

[0087] Other embodiments of the invention are described in the following numbered clauses: 1. A measurement method, comprising: Acquire measurement data related to one or more targets on a substrate, the measurement data including intensity measurement data related to one or more intensity measurements and phase difference measurement data related to phase difference measurements; Intensity asymmetry measurement data is determined based on the intensity measurement data, the intensity asymmetry measurement data describing the asymmetry of the intensity measurement data between diffraction orders in complementary diffraction order pairs after being diffracted by the one or more targets for each intensity measurement; Obtain one or more pre-calibrated asymmetry coefficients; and The value of the parameter of interest is determined based on the phase difference metric data and the intensity asymmetry metric data weighted by the pre-calibrated asymmetry coefficient. 2. The method according to Clause 1, wherein the one or more intensity measures comprise a plurality of intensity measures, each intensity measure having at least one associated asymmetry coefficient. 3. The method according to Clause 2, wherein the plurality of intensity measures includes at least four intensity measures. 4. The method according to any one of the preceding clauses, wherein each of the intensity measures comprises a function of one or more of the following: the maximum fringe intensity, the minimum fringe intensity, or the average fringe intensity of the fringes imaged from each of the diffraction orders. 5. The method according to Clause 4, wherein the phase difference measurement data relates to the positional difference between the fringe imaged from one or each of the diffraction orders and the first sub-target type and the second sub-target type of each of the at least one target. 6. The method according to any one of the preceding clauses, wherein each of the intensity asymmetry measures is associated with an intensity measure data value of the first diffraction level in the complementary diffraction level pair and the second diffraction level in the complementary diffraction level pair, respectively, based on a difference or ratio measure for each intensity measure. 7. The method according to any one of the preceding clauses further includes obtaining one or more pre-calibrated model term coefficients and one or more associated model terms, each model term describing a function of the substrate location independent of the parameter of interest; and The step of determining the value of the parameter of interest further determines the value of the parameter of interest based on one or more associated model terms weighted by the coefficients of the pre-calibrated model terms. 8. The method according to any one of the preceding clauses, wherein the intensity asymmetry measurement data includes one or more additional powers of one or more intensity asymmetry measurements. 9. The method according to any one of the preceding clauses, wherein the step of determining the value of the parameter of interest comprises determining a linear sum of the phase difference metric data and the intensity asymmetry metric data weighted by the pre-calibrated asymmetry coefficients. 10. The method according to any one of the preceding clauses, wherein the parameter of interest is overlaid. 11. The method according to any one of the preceding clauses, wherein the phase difference measurement data includes uncorrected overlay data. 12. The method according to any one of the preceding clauses, wherein each of the one or more targets comprises at least a pair of sub-targets, wherein the first sub-target of the at least pair of sub-targets comprises a first layer periodic structure having a first pitch and a second layer periodic structure having a second pitch, and the second sub-target of the at least pair of sub-targets comprises a first layer periodic structure having the second pitch and a second layer periodic structure having the first pitch. 13. The method according to Clause 12, wherein each of the one or more targets comprises two sub-target pairs in the pair of sub-targets, each of the sub-target pairs being oriented in different mutually orthogonal directions. 14. The method according to any one of the preceding clauses, comprising an initial calibration step for calibrating the asymmetry coefficient, the initial calibration step comprising: Acquire calibration measurement data associated with multiple targets on at least one calibration substrate, the calibration measurement data including intensity measurement data associated with the intensity measurement and the phase difference measurement data; Based on the intensity metric data, calibration intensity asymmetry metric data is determined, wherein the calibration intensity asymmetry metric data describes the asymmetry of the intensity metric data between diffraction orders in a complementary diffraction order pair after being diffracted by the target for each intensity metric. Obtain reference data related to the parameter of interest; and The one or more asymmetry coefficients are optimized to minimize the difference between the reference data and the uncorrected parameter of interest data when correction is performed using the combination of the calibration intensity asymmetry metric data and the asymmetry coefficients. 15. The method according to Clause 14, wherein the optimization step includes minimizing an evaluation function that correlates the reference data with the uncorrected parameter of interest data and the calibration intensity asymmetry metric data weighted by the asymmetry coefficients. 16. The method according to Clause 15, wherein the evaluation function comprises individual terms of corresponding weighted subsets of the calibrated intensity asymmetry metric data, each of the weighted subsets being associated with a corresponding intensity metric in the intensity metric and weighted by a corresponding asymmetry coefficient among the plurality of asymmetry coefficients. 17. The method according to Clause 16, wherein the evaluation function comprises the linear sum of the weighted subset and the uncorrected data of the parameter of interest. 18. The method according to any one of Clauses 14 to 17 further includes calibrating one or more model term coefficients, each model term coefficient corresponding to a corresponding model term describing a function of a substrate location independent of the parameter of interest. 19. The method according to Clause 18, wherein the optimization step includes optimizing simultaneously for the asymmetry coefficients and the model term coefficients. 20. The method according to Clause 18 or 19, wherein the optimization step includes applying the one or more model terms as part of the optimization to the calibration intensity asymmetry metric data. 21. The method according to Clause 18 or 19, comprising applying the one or more model terms as part of a preprocessing step to the calibration intensity asymmetry metric data. 22. The method according to any one of Clauses 14 to 21, comprising applying at least one filtering term to the calibration intensity asymmetry metric data before or as part of the optimization step. 23. The method according to Clause 22, wherein the at least one filtering term includes a smoothing filtering term. 24. The method according to clause 22 or 23, wherein the at least one filter term includes an evaluation filter term capable of configuring one aspect of the optimization in the optimization step. 25. The method according to any one of the preceding clauses, wherein the one or more pre-calibrated asymmetry coefficients include location- or region-dependent pre-calibrated asymmetry coefficients and / or context-dependent pre-calibrated asymmetry coefficients. 26. The method according to any one of the preceding clauses, comprising: Process corrections are determined based on the values ​​of the parameters of interest. 27. The method described in Clause 26 includes performing subsequent exposure actions based on the process correction. 28. A method for calibrating a plurality of asymmetry coefficients, each of the asymmetry coefficients being associated with a corresponding intensity metric, the method comprising: Acquire calibration measurement data associated with multiple targets on at least one calibration substrate, the calibration measurement data including uncorrected parameter of interest data and intensity measurement data associated with the intensity measurement; Based on the intensity metric data, calibration intensity asymmetry metric data is determined, wherein the calibration intensity asymmetry metric data describes the asymmetry of the intensity metric data between diffraction orders in a complementary diffraction order pair after being diffracted by the target for each intensity metric. Obtain reference data related to the parameter of interest; and The asymmetry coefficient is optimized to minimize the difference between the reference data and the uncorrected parameter of interest data when correction is performed using a combination of the calibration intensity asymmetry metric data and the asymmetry coefficient. 29. The method according to Clause 28, wherein the plurality of intensity measures comprises at least four intensity measures. 30. The method according to Clause 28 or 29, wherein each of the intensity measures comprises a function of one or more of the following: the maximum fringe intensity, the minimum fringe intensity, or the average fringe intensity of the fringes imaged from each of the diffraction orders. 31. The method according to any one of clauses 28 to 30, wherein each of the intensity asymmetry measures is associated with an intensity measure data value of the first diffraction level in the complementary diffraction level pair and the second diffraction level in the complementary diffraction level pair, respectively, based on a difference or ratio measure for each intensity measure. 32. The method according to any one of Clauses 28 to 31, wherein the parameter of interest is overlaid. 33. The method according to any one of clauses 28 to 31, wherein the parameter of interest is the alignment position. 34. The method according to any one of clauses 28 to 33, wherein the optimization step comprises minimizing an evaluation function that correlates the reference data with the uncorrected parameter of interest data and the calibration intensity asymmetry metric data weighted by the asymmetry coefficients. 35. The method according to Clause 34, wherein the evaluation function comprises individual terms of corresponding weighted subsets of the calibrated intensity asymmetry metric data, each of the weighted subsets being associated with a corresponding intensity metric in the intensity metric and weighted by a corresponding asymmetry coefficient among the plurality of asymmetry coefficients. 36. The method according to Clause 35, wherein the evaluation function is a linear sum of the weighted subset and the uncorrected data of the parameter of interest. 37. The method according to clauses 34, 35 or 36, wherein the evaluation function includes one or more additional terms, each additional term being power-dependent on a different subset of one or more subsets of the subset of the calibration intensity asymmetry metric data. 38. The method according to any one of clauses 28 to 37 further includes calibrating one or more model term coefficients, each model term coefficient corresponding to a corresponding model term describing a function of a substrate location independent of the parameter of interest. 39. The method according to Clause 38, wherein the optimization step includes optimizing both the asymmetry coefficients and the model term coefficients simultaneously. 40. The method according to clause 38 or 39, wherein the optimization step includes applying the one or more model terms as part of the optimization to the calibration intensity asymmetry metric data. 41. The method according to clause 38 or 39 includes applying the one or more model terms as part of the preprocessing step to the calibration intensity asymmetry metric data. 42. The method according to any one of clauses 28 to 41, comprising applying at least one filtering term to the calibration intensity asymmetry metric data before or as part of the optimization step. 43. The method according to Clause 42, wherein the at least one filtering term includes a smoothing filtering term. 44. The method according to clause 42 or 43, wherein the at least one filter term includes an evaluation filter term capable of configuring one aspect of the optimization in the optimization step. 45. The method according to any one of Clauses 28 to 44, wherein each of the one or more targets comprises at least a pair of sub-targets, wherein the first sub-target of the at least pair of sub-targets comprises a first layer periodic structure having a first pitch and a second layer periodic structure having a second pitch, and the second sub-target of the at least pair of sub-targets comprises a first layer periodic structure having the second pitch and a second layer periodic structure having the first pitch. 46. ​​The method according to any one of Clauses 28 to 44, wherein each of the one or more targets comprises at least a pair of sub-targets, wherein each of the at least one pair of sub-targets comprises a first layer periodic structure and a second layer periodic structure having a first pitch, wherein the first sub-target of the at least one pair of sub-targets comprises a first offset between the first layer periodic structure and the second layer periodic structure, and the second sub-target of the at least one pair of sub-targets comprises a second offset between the first layer periodic structure and the second layer periodic structure. 47. The method according to clause 45 or 46, wherein each of the one or more targets comprises two sub-target pairs of the pair of sub-targets, each pair being oriented in different mutually orthogonal directions. 48. The method according to any one of Clauses 28 to 47, wherein the asymmetry coefficient includes a location- or region-dependent asymmetry coefficient and / or a context-dependent pre-calibrated asymmetry coefficient. 49. A computer program comprising program instructions that, when run on a suitable device, are operable to perform the method according to any one of the preceding clauses. 50. A non-transitory computer program carrier, comprising the computer program as described in Clause 49. 51. A processing arrangement comprising: a nontransitory computer program carrier including a computer program, the computer program including program instructions that, when executed on a suitable device, are operable to perform a method according to any one of the preceding clauses; and a processor operable to run the computer program included on the nontransitory computer program carrier. 52. A photolithography apparatus, comprising: an alignment sensor; a patterning device support for supporting a patterning device; a substrate support for supporting a substrate; and a processing arrangement according to claim 51. 53. A measurement apparatus comprising: a support for a substrate; an optical system for irradiating the structure with measurement radiation; a detector for detecting the measurement radiation scattered by the structure; and a processing arrangement according to clause 51.

[0088] While the foregoing may specifically refer to the use of embodiments of the invention in the context of optical lithography, it should be understood that the invention can be used in other applications, such as imprint lithography, and is not limited to optical lithography where the context permits. In imprint lithography, the morphology in a patterning apparatus defines a pattern created on a substrate. The morphology of the patterning apparatus can be pressed into a resist layer supplied to the substrate, and then the resist is cured by applying electromagnetic radiation, heat, pressure, or a combination thereof. After the resist has cured, the patterning apparatus is removed from the resist to leave a pattern therein.

[0089] As used herein, the terms “radiation” and “beam” include all types of electromagnetic radiation, including ultraviolet (UV) radiation (e.g., wavelengths of 365 nm, 355 nm, 248 nm, 193 nm, 157 nm, or 126 nm, or about 365 nm, 355 nm, or 248 nm, 193 nm, or 126 nm) and extreme ultraviolet (EUV) radiation (e.g., wavelengths in the range of 5 nm to 20 nm), as well as particle beams, such as ion beams or electron beams.

[0090] Where the context permits, the term “lens” can refer to any one or a combination of various types of optical elements, including refractive, reflective, magnetic, electromagnetic, and electrostatic optical elements.

[0091] The foregoing description of specific embodiments will fully reveal the general nature of the invention, enabling others to readily modify and / or adapt these specific embodiments for various applications by applying the knowledge of those skilled in the art, without departing from the general concept of the invention and without excessive experimentation. Therefore, based on the teachings and guidance set forth herein, such adaptations and modifications are intended to be within the equivalent meaning and scope of the disclosed embodiments. It should be understood that the wording or terminology in this specification is for illustrative purposes only and not for limitation, and that the terminology or terminology of this specification will be interpreted by those skilled in the art based on the teachings and guidance.

[0092] The breadth and scope of this invention should not be limited by any of the exemplary embodiments described above, but should be defined only by the following claims and their equivalents.

Claims

1. A measurement method, comprising: Acquire measurement data related to one or more targets on a substrate, the measurement data including intensity measurement data related to one or more intensity measurements and phase difference measurement data related to phase difference measurements; Intensity asymmetry measurement data is determined based on the intensity measurement data, the intensity asymmetry measurement data describing the asymmetry of the intensity measurement data between diffraction orders in complementary diffraction order pairs after being diffracted by the one or more targets for each intensity measurement; Obtain one or more pre-calibrated asymmetry coefficients; as well as The value of the parameter of interest is determined based on the phase difference metric data and the intensity asymmetry metric data weighted by the pre-calibrated asymmetry coefficient.

2. The method of claim 1, wherein the one or more intensity measures comprise a plurality of intensity measures, each intensity measure having at least one correlated asymmetry coefficient.

3. The method according to any one of the preceding claims, wherein each of the intensity measures comprises a function of one or more of the following: the maximum fringe intensity, the minimum fringe intensity, or the average fringe intensity of the fringes imaged from each of the diffraction orders.

4. The method of claim 3, wherein the phase difference measurement data relates to the positional difference between the fringe imaged from one or each of the diffraction orders and the first sub-target type and the second sub-target type of each of the at least one target.

5. The method according to any one of the preceding claims, wherein each of the intensity asymmetry measures is associated with an intensity measure data value of the first diffraction level in the complementary diffraction level pair and the second diffraction level in the complementary diffraction level pair, respectively, based on a difference or ratio measure for each intensity measure.

6. The method according to any one of the preceding claims further comprises obtaining one or more pre-calibrated model term coefficients and one or more associated model terms, each model term describing a function of the substrate location independent of the parameter of interest; and The step of determining the value of the parameter of interest further determines the value of the parameter of interest based on one or more associated model terms weighted by the coefficients of the pre-calibrated model terms.

7. The method according to any one of the preceding claims, wherein the intensity asymmetry measurement data includes one or more additional powers of one or more intensity asymmetry measurements.

8. The method according to any one of the preceding claims, wherein the step of determining the value of the parameter of interest comprises determining a linear sum of the phase difference metric data and the intensity asymmetry metric data weighted by the pre-calibrated asymmetry coefficients.

9. The method according to any one of the preceding claims, comprising an initial calibration step for calibrating the asymmetry coefficient, the initial calibration step comprising: Acquire calibration measurement data associated with multiple targets on at least one calibration substrate, the calibration measurement data including the phase difference measurement data and intensity measurement data associated with the intensity measurement; Based on the intensity metric data, calibration intensity asymmetry metric data is determined, wherein the calibration intensity asymmetry metric data describes the asymmetry of the intensity metric data between diffraction orders in a complementary diffraction order pair after being diffracted by the target for each intensity metric. Obtain reference data related to the parameter of interest; as well as The one or more asymmetry coefficients are optimized to minimize the difference between the reference data and the uncorrected parameter of interest data when correction is performed using the combination of the calibration intensity asymmetry metric data and the asymmetry coefficients.

10. The method of claim 9, wherein the optimization step comprises minimizing an evaluation function that correlates the reference data with uncorrected data of the parameter of interest and a calibration intensity asymmetry metric weighted by the asymmetry coefficients.

11. The method according to any one of claims 9 to 10, further comprising calibrating one or more model term coefficients, each model term coefficient corresponding to a corresponding model term describing a function of a substrate location independent of the parameter of interest.

12. The method according to any one of claims 9 to 11, comprising applying at least one filtering term to the calibration intensity asymmetry measurement data before or as part of the optimization step.

13. The method according to any one of the preceding claims, wherein the one or more pre-calibrated asymmetry coefficients include location- or region-dependent pre-calibrated asymmetry coefficients and / or context-dependent pre-calibrated asymmetry coefficients.

14. The method according to any one of the preceding claims, comprising determining process corrections based on the value of the parameter of interest.

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