阅读说明：本技术 量测方法和设备、计算机程序和光刻系统 (Metrology method and apparatus, computer program and lithographic system ) 是由 P·J·特纳 A·蔡亚马斯 于 2020-02-17 设计创作，主要内容包括：公开了一种用于量测的方法、计算机程序和相关联的设备。该方法包括确定衬底或衬底部分是否受到工艺效应。该方法包括：获取检查数据,检查数据包括与衬底或其一部分上的结构相关联的多组测量数据；例如测量光瞳；以及获取指纹数据,指纹数据描述感兴趣参数的空间变化。执行检查数据到指纹数据的迭代映射。结构是否受到工艺效应基于迭代映射收敛于解的程度。(A method, computer program and associated apparatus for metrology are disclosed. The method includes determining whether a substrate or substrate portion is subject to a process effect. The method comprises the following steps: acquiring inspection data, the inspection data comprising sets of measurement data associated with structures on the substrate or a portion thereof; such as measuring a pupil; and acquiring fingerprint data describing the spatial variation of the parameter of interest. An iterative mapping of the inspection data to the fingerprint data is performed. Whether a structure is subject to process effects is based on the extent to which the iterative mapping converges to a solution.)

1. A method of determining whether a substrate or substrate portion is subject to a process effect, the method comprising:

obtaining inspection data comprising sets of measurement data associated with structures on the substrate or the substrate portion;

acquiring fingerprint data describing a spatial variation of a parameter of interest on the substrate or the substrate portion;

performing an iterative mapping of the inspection data to the fingerprint data; and

determining whether the substrate is subject to a process effect based on a degree to which the iterative mapping converges to a solution.

2. The method of claim 1, wherein the plurality of sets of measurement data comprises a plurality of sets of measurement data associated with a plurality of different locations on the substrate.

3. The method of claim 2, wherein the plurality of sets of measurement data comprises a plurality of sets of measurement data acquired from some or each of a plurality of different locations on the substrate using different ones of the plurality of acquisition settings.

4. A method according to claim 1, 2 or 3, wherein each set of measurement data comprises angle-resolved intensity values.

5. The method of claim 1, wherein the fingerprint data is related to a fingerprint of a known process effect.

6. The method of claim 1, wherein the fingerprint data has been obtained from previous measurement data relating to at least one previous measurement of the known process effect.

7. The method of claim 1, wherein the iterative mapping produces one or more weight mappings for each iteration.

8. The method of claim 1, wherein the step of performing iterative mapping comprises calculating corrections to wafer background fingerprints that correct fingerprint contributors other than the parameter of interest.

9. The method of claim 1, wherein the fingerprint data is obtained from a library of sets of fingerprint data.

10. The method of claim 9, wherein the sets of fingerprint data comprise a plurality of examples of one, some, or all of: zernike polynomials, bezier functions, legendre polynomials, principal components from principal component analysis of metrology data relating to one or more metrology sources, known process fingerprints, historical process fingerprints, and predictions, random values of process fingerprints.

11. A method according to claim 9 or 10, comprising repeating the method for each of a different set of fingerprint data of the plurality of sets of fingerprint data.

12. The method of claim 11, comprising setting a convergence criterion for determining whether the structure is subject to a process effect based on a number of iterations and/or a size of a residual.

13. The method of claim 12, comprising reporting the strongest one or more sets of fingerprint data according to the convergence criterion.

14. A method according to claim 13, comprising determining a control strategy or correction, and/or determining a root cause for the strongest set or sets of fingerprint data.

15. The method of claim 1, wherein the parameter of interest is registered and each set of measurement data is associated with a pupil plane.

16. A computer program comprising processor readable instructions which, when run on a suitable processor control device, cause the processor control device to perform the method of claim 1.

17. A computer program carrier comprising a computer program according to claim 16.

Technical Field

The present invention relates to metrology methods and apparatus that may be used, for example, in the manufacture of devices by lithographic techniques.

Background

A lithographic apparatus is a machine that applies a desired pattern onto a substrate, usually onto a target portion of the substrate. Lithographic apparatus can be used, for example, in the manufacture of Integrated Circuits (ICs). In such cases, a patterning device (which is alternatively referred to as a mask or a reticle) may be used to generate a circuit pattern to be formed on an individual layer of the IC. The pattern can be transferred onto a target portion (e.g., comprising part of one or several dies) on a substrate (e.g., a silicon wafer). The transfer of the pattern is typically via imaging onto a layer of radiation-sensitive material (resist) provided on the substrate. Typically, a single substrate will contain a network of adjacent target portions that are successively patterned. In lithographic processes, it is often necessary to perform measurements on the created structures, for example, for process control and verification. Various tools for making such measurements are known, including scanning electron microscopes, which are commonly used to measure Critical Dimension (CD), and specialized tools for measuring overlay, which is a method for measuring the accuracy of alignment of two layers in a device. The overlay may be described in terms of the degree of misalignment between the two layers, for example a reference to a measurement overlay of 1nm may describe the case where the two layers are misaligned by up to 1 nm.

Recently, various forms of scatterometers have been developed for use in the field of lithography. These devices direct a beam of radiation onto a target and measure one or more characteristics of the scattered radiation-e.g., intensity at a single reflection angle as a function of wavelength; intensity at one or more wavelengths as a function of reflection angle; or polarization as a function of the angle of reflection-to obtain a diffraction image or pattern from which a property of interest of the target can be determined.

In order to diffract radiation impinging on the substrate, an object having a particular shape is printed onto the substrate and is often referred to as a scatterometry target or simply a target. As described above, the actual shape of the scatterometry object may be determined using a cross-sectional scanning electron microscope or the like. However, this involves a lot of time, effort and dedicated equipment and is not well suited for measurements in a production environment, since separate dedicated equipment is required in line with ordinary equipment in e.g. a lithography unit.

The substrate may be subject to process effects and thus have one or more process or background fingerprints, which may result in, for example, undesirable and un-engineered asymmetries in the structures on the substrate. These process fingerprints may affect device functionality directly (e.g., the CDU associated with the film thickness affects the gate clock frequency) or indirectly (e.g., negatively affects control metrology used to control and monitor the fabrication process). For example, it is difficult to separate these process fingerprints from the overlay in the overlay control loop.

It is therefore desirable to be able to obtain more information about such process fingerprints.

Disclosure of Invention

The present invention provides in a first aspect a method of determining whether a substrate or substrate portion is subject to a process effect, the method comprising: acquiring inspection data comprising a plurality of sets of measurement data associated with a structure on a substrate or a portion thereof; acquiring fingerprint data describing the spatial variation of a parameter of interest on a substrate or a part thereof; performing an iterative mapping of inspection data to fingerprint data; and determining whether the substrate is subject to a process effect based on the extent to which the iterative mapping converges to the solution.

The present invention provides, in a second aspect, a metrology apparatus operable to perform the method of the first aspect.

The invention also provides a computer program comprising processor readable instructions which, when run on a suitable processor control apparatus, cause the processor control apparatus to perform the method of the first aspect, and a computer program carrier comprising such a computer program. The processor control apparatus may comprise the metrology apparatus of the second aspect.

Further features and advantages of the invention, as well as the structure and operation of various embodiments of the invention, are described in detail below with reference to the accompanying drawings. Note that the present 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 persons skilled in the relevant art(s) based on the teachings contained herein.

Drawings

Embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

FIG. 1 depicts a lithographic apparatus;

FIG. 2 depicts a lithography unit;

FIG. 3 depicts a first scatterometer;

FIG. 4 depicts a second scatterometer;

FIG. 5 is a flow chart depicting an example process of reconstructing a structure from scatterometer measurements; and

FIG. 6 is a flow chart depicting an in-device measurement method used in embodiments of the present invention.

Detailed Description

Before describing embodiments of the present invention in detail, it is helpful to present an example environment in which embodiments of the present invention may be implemented.

FIG. 1 schematically depicts a lithographic apparatus LA. The apparatus comprises: an illumination optical system (illuminator) IL configured to condition a radiation beam B (e.g. UV radiation or DUV radiation); a patterning device support or support (e.g. a mask table) MT constructed to support a patterning device (e.g. a mask) MA and connected to a first positioner PM configured to accurately position the patterning device in accordance with certain parameters; a substrate table (e.g. a wafer table) WT constructed to hold a substrate (e.g. a resist-coated wafer) W and connected to a second positioner PW configured to accurately position the substrate in accordance with certain parameters; and a projection optical system (e.g. a refractive projection lens system) PS configured to project a pattern imparted to the radiation beam B by patterning device MA onto a target portion C (e.g. comprising one or more dies) of the substrate W.

The illumination optics may include various types of optical components, such as refractive, reflective, magnetic, electromagnetic, electrostatic or other types of optical components, or any combination thereof, for directing, shaping, or controlling radiation.

The patterning device support holds the patterning device in a manner that depends on the orientation of the patterning device, the design of the lithographic apparatus, and other conditions, such as for example whether or not the patterning device is held in a vacuum environment. The patterning device support may use mechanical, vacuum, electrostatic or other clamping techniques to hold the patterning device. The patterning device support may be a frame or table, for example, which may be fixed or movable as required. The patterning device support may ensure that the patterning device is at a desired position, for example with respect to the projection system. Any use of the terms "reticle" or "mask" herein may be considered synonymous with the more general term "patterning device".

The term "patterning device" used herein should be broadly interpreted as referring to any device that can be used to impart a radiation beam with a pattern in its cross-section such as to create a pattern in a target region of the substrate. It should be noted that the pattern imparted to the radiation beam may not exactly correspond to the desired pattern in the target portion of the substrate, for example if the pattern includes phase-shifting features or so called assist features. Generally, the pattern imparted to the radiation beam will correspond to a particular functional layer in a device being created in the target portion, such as an integrated circuit.

The patterning device may be transmissive or reflective. Examples of patterning devices include masks, programmable mirror arrays, and programmable LCD panels. Masks are well known in lithography, and include mask types such as binary, alternating phase-shift, and attenuated phase-shift, as well as various hybrid mask types. An example of a programmable mirror array employs a matrix arrangement of small mirrors, each of which can be individually tilted so as to reflect an incoming radiation beam in different directions. The tilted mirrors impart a pattern in a radiation beam which is reflected by the mirror matrix.

As here depicted, the apparatus is of a transmissive type (e.g., employing a transmissive mask). Alternatively, the apparatus may be of a reflective type (e.g. employing a programmable mirror array of a type as referred to above, or employing a reflective mask).

The lithographic apparatus may also be of a type wherein: wherein at least a portion of the substrate may be covered by a liquid having a relatively high refractive index, e.g. water, so as to fill a space between the projection system and the substrate. Immersion liquids may also be applied to other spaces in the lithographic apparatus, for example, between the mask and the projection system. Immersion techniques for increasing the numerical aperture of projection systems are well known in the art. The term "immersion" as used herein does not mean that a structure, such as a substrate, must be submerged in liquid, but rather only means that liquid is located between the projection system and the substrate during exposure.

Referring to FIG. 1, the illuminator IL receives a radiation beam from a radiation source SO. The source and the lithographic apparatus may be separate entities, for example when the source is an excimer laser. In such cases, the source is not considered to form part of the lithographic apparatus and the radiation beam is passed from the source SO to the illuminator IL with the aid of a beam delivery system BD including, for example, suitable directing mirrors and/or a beam expander. In other cases the source may be an integral part of the lithographic apparatus, for example when the source is a mercury lamp. The source SO and the illuminator IL, together with the beam delivery system BD if required, may be referred to as a radiation system.

The illuminator IL may comprise an adjuster AD for adjusting the angular intensity distribution of the radiation beam. Generally, at least the outer and/or inner radial extent (commonly referred to as σ -outer and σ -inner, respectively) of the intensity distribution in a pupil plane of the illuminator can be adjusted. IN addition, the illuminator IL may include various other components, such as an integrator IN and a condenser CO. The illuminator may be used to condition the radiation beam, to have a desired uniformity and intensity distribution in its cross-section.

The radiation beam B is incident on the patterning device (e.g., mask) MA, which is held on the patterning device support (e.g., mask table MT), and is patterned by the patterning device. After passing through the patterning device (e.g. mask) MA, the radiation beam B passes through the projection optics PS, which focuses the beam onto a target portion C of the substrate W, thereby projecting an image of the pattern onto the target portion C. With the aid of the second positioner PW and position sensor IF (e.g. an interferometric device, linear encoder, 2D encoder or capacitive sensor), the substrate table WT can be moved accurately, e.g. so as to position different target portions C in the path of the radiation beam B. Similarly, the first positioner PM and another position sensor (which is not explicitly depicted in fig. 1) can be used to accurately position the patterning device (e.g. mask) MA with respect to the path of the radiation beam B, e.g. after mechanical retrieval from a mask library, or during a scan.

Patterning device (e.g., mask) MA and substrate W may be aligned using mask alignment marks Ml, M2 and substrate alignment marks Pl, P2. Although the substrate alignment marks as shown occupy dedicated target portions, they may be located in spaces between target portions (these are known as scribe-lane alignment marks). Similarly, in situations in which more than one die is provided on the patterning device (e.g. mask) MA, the mask alignment marks may be located between the dies. Small alignment marks may also be included within the die, in device features, in which case it is desirable that the marks be as small as possible and that no different imaging or processing conditions are required than adjacent features. An alignment system for detecting alignment marks is described further below.

The lithographic apparatus LA in this example is of a so-called dual stage type, having two substrate tables WTa, WTb and two stations (an exposure station and a measurement station) between which the substrate tables can be exchanged. When one substrate on one substrate table is exposed at the exposure station, another substrate may be loaded onto the other substrate table at the measurement station and various preparation steps performed. The preparation steps may include mapping surface controls of the substrate using the level sensor LS and measuring the position of alignment marks on the substrate using the alignment sensor AS. This enables a significant increase in the throughput of the apparatus.

The depicted apparatus may be used in a variety of modes, including, for example, a step mode or a scan mode. The construction and operation of lithographic apparatus are well known to those skilled in the art and need not be described further in order to understand the present invention.

As shown in fig. 2, the lithographic apparatus LA forms part of a lithographic system, called a lithographic cell LC or lithographic cell. The lithography unit LC may further comprise equipment for performing pre-exposure and post-exposure processes on the substrate. Typically 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. The substrate handler or robot RO picks up substrates from the input/output ports I/O1, I/O2, moves them between different processing apparatuses, and then transfers them to a load table LB of the lithographic apparatus. These devices (generally referred to as tracks) are controlled by a track control unit TCU, which itself is controlled by a monitoring system SCS, which also controls the lithographic apparatus via a lithographic control unit LACU. Thus, different equipment may be operated to maximize throughput and processing efficiency.

In order to properly and consistently expose a substrate exposed by a lithographic apparatus, the exposed substrate needs to be inspected to measure characteristics such as overlay error, line thickness, Critical Dimension (CD), etc. between subsequent layers. If an error is detected, the exposure of subsequent substrates can be adjusted, particularly if the inspection can be fast and rapid enough so that other substrates of the same batch are still exposed. In addition, substrates that have already been exposed may be stripped and reworked (to improve yield) or discarded, thereby avoiding performing exposures on substrates that are known to be defective. In case only some target portions of the substrate are defective, a further exposure may be performed only on good target portions.

The detection device is used to determine how characteristics of the substrate, in particular characteristics of different substrates or different layers of the same substrate, vary between layers. The inspection apparatus may be integrated into the lithographic apparatus LA or the lithographic cell LC or may be a stand-alone device. In order to enable the fastest measurements, it is desirable for the inspection apparatus to measure the properties in the exposed resist layer immediately after exposure. However, latent images in resist have very low contrast-the difference in refractive index between portions of resist that have been exposed to radiation and portions that have not-been exposed to radiation is small-and not all detection devices have sufficient sensitivity to make useful measurements of the latent image. Thus, the measurement may be performed after a post-exposure bake step (PEB), which is typically the first step performed on an exposed substrate, and increases the contrast between exposed and unexposed portions of the resist. At this stage, the image in the resist may be referred to as semi-latent. The developed resist image may also be measured, when either the exposed or unexposed portions of the resist have been removed, or after a pattern transfer step such as etching. The latter possibility limits the possibility of rework of defective substrates but still provides useful information.

FIG. 3 depicts a scatterometer that can be used with the present invention. It comprises a broadband (white light) radiation projector 2 projecting radiation onto a substrate W. The reflected radiation is passed to a spectrometer detector 4, the spectrometer detector 4 measuring the spectrum 10 (intensity as a function of wavelength) of the specularly reflected radiation. From this data, the structure or profile that produces the detected spectrum can be reconstructed by the processing unit PU, for example by rigorous coupled wave analysis and non-linear regression or by comparison with a library of simulated spectra as shown at the bottom of fig. 3. Generally, for reconstruction, the general form of the structure is known and some parameters are assumed from knowledge of the manufacturing process of the structure, leaving only a few parameters of the structure to be determined from the scatterometry data. Such scatterometers may be configured as normal incidence scatterometers or oblique incidence scatterometers.

Another scatterometer that may be used with the present invention is shown in fig. 4. In this device, radiation emitted by the radiation source 2 is collimated using a lens system 12 and transmitted through an interference filter 13 and a polarizer 17, is partially reflected by a reflective surface 16, and is focused onto a substrate W via a microscope objective 15, the microscope objective 15 having a high Numerical Aperture (NA), preferably at least 0.9, more preferably at least 0.95. The immersion scatterometer may even have a lens with a numerical aperture greater than 1. The reflected radiation is then transmitted through partially reflective surface 16 into detector 18 for detection of the scatter spectrum. The detector may be located in a back projection pupil plane 11, the back projection pupil plane 11 being at the focal length of the lens system 15, however, the pupil plane may alternatively be re-imaged with secondary optics (not shown) onto the detector. A pupil plane is a plane in which the radial position of the radiation defines the angle of incidence and the angular position defines the azimuth angle of the radiation. The detector is preferably a two-dimensional detector so that a two-dimensional angular scatter spectrum of the substrate target 30 can be measured. The detector 18 may be, for example, a CCD or CMOS sensor array, and may use, for example, an integration time of 40 milliseconds per frame.

Reference beams are often used, for example, to measure the intensity of incident radiation. To this end, when the radiation beam is incident on the beam splitter 16, a portion of it is transmitted through the beam splitter as a reference beam towards the reference mirror 14. The reference beam is then projected onto a different part of the same detector 18 or alternatively onto a different detector (not shown).

A set of interference filters 13 may be used to select wavelengths of interest in the range of, for example, 405 to 790nm or even lower, such as 200 to 300 nm. The interference filter may be tunable rather than comprising a set of different filters. A grating may be used instead of the interference filter.

The detector 18 may measure the intensity of scattered light at a single wavelength (or narrow range of wavelengths), the intensity of individual wavelengths, or the intensity integrated over a range of wavelengths. Furthermore, the detector may measure the intensity of the transverse magnetic polarized light and the transverse electric polarized light and/or the phase difference between the transverse magnetic polarized light and the transverse electric polarized light, respectively.

It is possible to use a broadband light source (i.e. a light source with a wide range of light frequencies or wavelengths-and therefore colors), which provides a large spread of light to allow mixing of multiple wavelengths. The plurality of wavelengths in the broadband preferably each have a bandwidth of Δ λ and a separation of at least 2 Δ λ (i.e., twice the bandwidth). Several "sources" of radiation may be different parts of an extended radiation source, which parts have been separated using a fiber bundle. In this way, angle-resolved scatter spectra at multiple wavelengths can be measured in parallel. A 3D spectrum (wavelength and two different angles) can be measured, which 3D spectrum contains more information than a 2D spectrum. This allows more information to be measured, thereby improving the robustness of the metrology process.

The target 30 on the substrate W may be a 1D grating, the 1D grating being printed such that after development, the bars are formed from solid resist lines. The target 30 may be a 2D grating, the 2D grating being printed such that after development, the grating is formed by solid resist pillars or vias in the resist. Rods, posts or vias may alternatively be etched into the substrate. The pattern is sensitive to chromatic aberrations in the lithographic projection apparatus, in particular the projection system PL, and illumination symmetry and the presence of such aberrations will manifest themselves in variations in the printed grating. Thus, the scatterometry data of the printed grating is used to reconstruct the grating. From knowledge of the printing step and/or other scatterometry processes, parameters of the 1D grating (such as line width and shape) or parameters of the 2D grating (such as post or via width or length or shape) may be input to the reconstruction process performed by the processing unit PU.

As described above, the target is on the surface of the substrate. The target typically takes the shape of a series of lines in a grating or a substantially rectangular structure of a 2D array. The objective of rigorous optical diffraction theory in metrology is to efficiently calculate the diffraction spectrum reflected from a target. In other words, the target shape information is obtained for CD (critical dimension) uniformity and overlay or focus metrology. Overlay metrology is a measurement system in which the overlay of two targets is measured to determine whether two layers on a substrate are aligned. The focus measurements determine the focus volume (and/or dose) settings used in forming the target. CD uniformity is simply a measure of the uniformity of the grating across the spectrum to determine how the exposure system of the lithographic apparatus works. In particular, the CD or critical dimension is the width of an object "written" on the substrate and is the limit at which the lithographic apparatus can physically write on the substrate.

Using scatterometry, measurements of the shape and other parameters of a structure, such as target 30, may be performed in a number of ways, such as described above in connection with modeling of the target structure and its diffraction characteristics. An example of this process is depicted in fig. 5. A first estimated diffraction pattern based on the target shape (first candidate structure) is calculated and compared to the observed diffraction pattern. The parameters of the model are then systematically varied and the diffraction recalculated in a series of iterations to generate new candidate structures to arrive at a best fit. In one variation of this process, the diffraction spectra of many different candidate structures may be pre-computed to create a "library" of diffraction spectra. The diffraction pattern observed from the measurement target is then compared to the library of calculated spectra to find the best fit. These two methods can be used together: a rough fit can be obtained from the library and then the best fit found by an iterative process.

Throughout the description of fig. 5, the term "diffraction image" will be used, assuming the scatterometer of fig. 3 or 4 is used. A diffraction image is an example of an inspection data element in the context of the present disclosure. Those skilled in the art can readily adapt the teachings to different types of scatterometers, and even other types of measurement instruments.

FIG. 5 is a flow chart generally depicting steps of a method of measuring a target shape and/or material property. The procedure is as follows, then described in more detail:

402-measuring the diffraction image;

403-defining model matching schemes;

404-estimate the shape parameter θ₀；

406 — calculating a model diffraction image;

408-comparing the measured image and the calculated image;

410-calculating a cost function;

412-Generation of a revised shape parameter θ₁；

414-report final shape parameters

For the present description, it will be assumed that the target is periodic in only 1 direction (1D structure). In practice it may be periodic in 2 directions (two-dimensional structure) and the processing will be adapted accordingly.

402: diffraction images of actual targets on the substrate are measured using a scatterometer such as described above. The measured diffraction image is forwarded to a computing system, such as a computer. The computing system may be the processing unit PU described above, or may be a separate device.

403: a profile is established that defines a parameterized model of the target structure in terms of a plurality of parameters theta. For example, in a 1D periodic structure, these parameters may represent the angle of the sidewalls, the height or depth of the features, the width of the features. The properties of the target material and the underlying layer are also represented by parameters such as the refractive index (at the specific wavelength present in the scattered measuring radiation beam). Specific examples will be given below. Importantly, while the target structure may be defined by many parameters describing its shape and material properties, the contours will define many of them as having fixed values, while others are defined as variable or "floating" parameters for the purposes of the following process steps. Furthermore, a method of allowing parameter variations rather than completely independent floating parameters will be described. For the description of fig. 5, only the variable parameter is regarded as the parameter θ. The profile also defines the measured radiation settings for a given target structure and how the parameter values are estimated by fitting the inspection data to the model.

404: by setting an initial value theta for the floating parameter₀To estimate the model object shape. Each floating parameter will be generated within a specific predetermined range, as defined in the scheme.

406: the parameters representing the estimated shape are used together with the optical properties of the different elements of the model to calculate the scattering properties, e.g. using a rigorous optical diffraction method such as RCWA or any other solver equation of Maxwell. This gives an estimate or model diffraction image of the estimated target shape.

408. 410: the measured diffraction image and the model diffraction image are then compared and their similarity and difference are used to calculate an "merit function" for the model target shape.

412：Assuming that the evaluation function indicates that the model needs to be refined before it accurately represents the actual target shape, a new parameter θ is estimated₁Etc. and iteratively fed back to step 406. Steps 406 to 412 are repeated.

To assist in the search, the calculation in step 406 may also generate a partial derivative of the merit function to indicate that increasing or decreasing the parameter in this particular region in the parameter space will increase or decrease the sensitivity of the merit function. The calculation of the evaluation function and the use of the derivative are well known in the art and will not be described in detail here.

414: when the merit function indicates that the iterative process has converged to a solution with the desired accuracy, the currently estimated parameters are reported as a measure of the actual target structure.

The computation time of this iterative process is mainly determined by the forward diffraction model used, i.e. the estimated model diffraction image is computed using strict optical diffraction theory from the estimated target structure. There are more degrees of freedom if more parameters are needed. The computation time increases in principle with increasing degrees of freedom, however, this can be mitigated if finite differences are used to approximate the jacobian. The estimated or model diffraction image computed at 406 may be represented in various forms. If the calculated image is represented in the same form (e.g., spectrum, pupil image) as the measurement image generated in step 402, the comparison is simplified.

The creation of the contour involves multiple refinements of the contour, where the physical model is gradually adjusted to best represent the inspection data. The inspection data may include inspection data elements. The inspection data element may be an image, a diffraction image (if a diffraction-based scatterometer is used), a spectrum, or a pupil image; or may be a reconstruction parameter value obtained from such a diffraction image or the like. Each of the inspection data elements may be acquired by inspecting a corresponding target structure, for example, using a scatterometer such as described above. Each of these inspection data elements may be described by a plurality of intensity values. The adjustment is typically based on the results of the reconstruction. As described above, the reconstruction fits the model to the inspection data, thereby converting the inspection data elements into parameter values. At the beginning of the process, the reconstruction may fail because the uncertainty may be large. Thus, it may be more efficient to reconstruct only one or a few measurements rather than the complete data set.

In order to make the profile more robust, the nominal parameter values of the profile should be well chosen. Ideally, many target structures should be reconstructed in order to correctly estimate these nominal parameter values.

Another measurement technique includes in-die measurement (IDM). IDM is a specific metrology application that is commonly used to measure post-etch in-device registration between structures/layers in a semiconductor process flow. This is achieved by measuring the "pupil", which is an angle-resolved scatterometry signal collected by a suitable measurement tool (alternatively, image plane measurements may be used). The tool focuses a spot onto a small sub-unit of the actual product structure, rather than a dedicated metrology target, and collects a pupil that includes information about the structure illuminated by the spot.

The information projected onto the pupil plane is "blended together" such that no single pixel in the pupil contains isolated information about any feature on the wafer. In another application, as shown in FIG. 5, this information is "reconstructed" by building a parameterized version of the device structure and running the model through a simulation engine to generate "simulated pupils". The optimization engine then uses the difference between the measured and simulated pupils to drive the model parameters and iteratively attempts to determine the content on the wafer.

However, it is well known that such reconstruction is difficult to perform correctly and takes a lot of time to set up. For most applications, the cost/benefit ratio is unbalanced (setting is too difficult and the resulting data is sometimes problematic). Furthermore, it has been found that the current models used in reconstruction (typically used to reconstruct CDs) are technically insufficient to model the very small signals used to extract overlay information (they are optimized for symmetric rather than asymmetric parameter extraction). Therefore, the above reconstruction techniques are typically only used to monitor film thickness, nk, and/or CD. They do not provide a good solution to report asymmetric process parameters (e.g., 3DNAND column tilt). Because of these problems, IDM techniques (such as IDM-Overlay, for measuring Overlay) use a "data-driven" scheme setup method.

Asymmetric process parameters (line/pillar tilt, Bottom Grating Asymmetry (BGA), sidewall angle (SWA) asymmetry, pitch walk, etc.) are cumbersome because nothing is typically designed to be asymmetric, and therefore many metrology tools employ symmetric structures. Because these asymmetric process fingerprints are difficult to measure directly using existing strategies and tools, only the effects of these fingerprints (large residuals, wafer-to-wafer overlay variations, etc.) can be detected. Therefore, it is very valuable to provide information about these process fingerprints, even if the information is not perfect.

The iterative training process for in-die metrology is depicted by fig. 6. This shows that the metrology data or pupil PU and the parameter fingerprint FP (or an error file derived from the fingerprint), such as an overlay fingerprint, are fed into the estimator EST. In this context, an overlay fingerprint may include a description of an overlay on a wafer or portion thereof (e.g., a field); for example, it may comprise a set of overlay values on the substrate or field. In the first iteration, the fingerprint FP input to the estimator EST may comprise an initial fingerprint SFP, such as any initial estimate or guess. In an overlay example, the initial estimate may simply comprise an overlay value for the scanner settings (i.e., an intentional bias or an intentional overlay applied to aid metrology calculations). For other process fingerprints, the initial fingerprint SFP may come from a variety of sources, including but not limited to historically significant process fingerprints, other semiconductor processing tools (e.g., etch chamber chuck heating parameters), other metrology tools (e.g., film thickness measurements).

The estimator computes one or more weight maps WM or schemes that describe the mapping of the pupil PU to the fingerprint FP (i.e. to the overlay or other parameter values of interest). For example, a plurality of weight maps WM may be calculated for a plurality of pupils PU of the same structure captured under different illumination conditions (e.g. different wavelengths, polarizations, incident angles, etc. or combinations thereof). One or more of these pupils may be significantly different from the others, even if they are acquired by measurements of the same structure.

Once the weight maps WM are calculated, they are combined with the pupil PU and averaged to obtain an average fingerprint AFP. This will be iteratively fed back to the estimator as an updated fingerprint UFP. This operation is performed until convergence to produce the final weight map and fingerprint, or until a predefined number of iterations are performed without achieving convergence. As shown, in each iteration, instead of feeding the original average fingerprint directly, a modeling step MOD may be performed. The modeling step may calculate the fingerprint residual RES from the difference in yield of the average fingerprint AFP and the fingerprint PFP used in the previous iteration (e.g., scanner setting overlay SFP of the first iteration). This residual RES is used to update the model UPD to determine an updated fingerprint UFP. The updated fingerprint UFP is used as the fingerprint FP for the next iteration.

The correction may be applied to the fingerprint FP in one or more iterations (e.g., each iteration), which accounts for the error between the actual fingerprint on the wafer and the fingerprint SFP/FP used for that iteration. Such corrections may comprise wafer background fingerprint corrections WBFC and may be performed, for example, by the estimator EST or as part of the modeling step MOD. The correction may operate by dynamically modifying the fingerprint FP according to the model to improve the result and may therefore be seen as a mechanism to compensate for a "non-perfect" initial estimate of the fingerprint SFP. For example, the correction may use Principal Component Analysis (PCA) on the raw pupil, as the wafer background fingerprint tends to be orthogonal to the overlay fingerprint being sought. Thus, the estimator EST may comprise a correction mechanism (e.g. using the initial fingerprint SFP and updating based on the WBFC mechanism) that enables the fingerprint FP to float according to some model parameters.

It has been determined that the aforementioned method including the WBFC step converges to a solution faster (fewer iterations) if the initial estimate is closer to the actual fingerprint (solution) than farther away. It has also been shown that a process fingerprint generating a large number of signals in the pupil plane requires a lower accuracy of the initial fingerprint estimate SFP. Pushing this to the extreme, the inventors have observed that inputting a random fingerprint (random overlay value) as the initial estimate SFP of the iterative solver of fig. 6 may result in convergence of the solution. In other words, if a process fingerprint can be described by model parameters in a calibration (e.g., WBFC) mechanism, and if the signal strength is strong enough, meaningful process fingerprint information can be extracted without prior knowledge of the appearance of the fingerprint.

The inventors speculate that this observation may be used to obtain useful data about process effects. By inputting either a "background fingerprint" or a "process fingerprint". I.e. a fingerprint describing something other than overlay, it can be determined whether this fingerprint is present in (or described by) the measured pupil data. Furthermore, estimates may be determined as to the extent to which such fingerprints are present (e.g., their dominance in the data) and/or the location of the particular identified fingerprint/process effect. The input process fingerprint may be any known fingerprint (or component thereof), such as found in historical data and/or polynomials, such as Zernike (Zernike) polynomials, Legendre (Legendre) polynomials, or Bessel (Bessel) functions. The fingerprint may describe, for example, a wafer effect (wafer fingerprint) and/or a field effect (field fingerprint). The iterative process and corrections therein tend to ensure convergence of the principal components of the fingerprint. In case the input is not an overlay, the first principal component may correspond to something other than an overlay, in particular a wafer background effect or a process effect.

In an embodiment, it is proposed to build a library comprising many examples of such fingerprints and their components. Since the required knowledge of the underlying background fingerprint of interest required to generate the wafer map need not be complete, but only a close enough initial estimate or guess, such library fingerprints can be input into the flow of FIG. 6 (i.e., as initial fingerprint SFPs) to see which converges, and how fast. It is expected that the major components in the process fingerprint library do not contain complete fingerprints, only the initial "seed". The degree of convergence of the pupil mapping estimator EST indicates the intensity of the input signal present on the wafer. If the input "fingerprint seed" is closely related to the actual fingerprint on the wafer, the estimator will soon converge to a good solution; conversely, if the input fingerprint seed is not correlated to any actual parameters on the wafer, the pupil mapping result will be poor. By "injecting" the signal (process fingerprint) into the pupil mapping tool and looking at which converge, process fingerprint information can be extracted without any a priori knowledge of the process fingerprint.

Such libraries may include, for example, many examples of one, some, or all of the following:

zernike polynomials (wafer fingerprints);

bessel function (wafer fingerprint);

legendre polynomials (live fingerprints);

principal components from PCA analysis of various measurement sources;

known process fingerprints;

historical process fingerprints;

prediction of process fingerprint;

any random pattern or value.

At present, large databases comprising metrology data from multiple sources (scanner/etcher/thin film deposition/scatterometry, etc.) are being constructed in any case, and it is suggested that such databases may be used to construct the suggested libraries.

Once constructed, library fingerprints may be injected into the process described in FIG. 6 to find wafer maps/recipes associated with these library fingerprints; for example, as part of a brute force search in which each library fingerprint is entered. Such a search method may include the setting of convergence criteria. The convergence criterion may be set according to the number of iterations/convergence speed and/or the residual size. For example, a threshold may be set for one or both of these convergence criteria. For example, a library fingerprint may be considered to exist when a processing loop converges on a wafer map within a threshold maximum number of iterations and/or results in a minimum residual value (e.g., in terms of an average over a wafer or portion thereof, such as per field). Such a process may be done in parallel with IDM or other metrology, e.g., using the same pupil. Therefore, no additional metrology work is required.

The strongest signal found in searching the process fingerprint library may be reported in parallel with any overlay (or other parameter of interest) values. These may be tracked and used to determine control strategies or corrections, and/or to determine root causes (e.g., set up A/B tests). This may be particularly valuable for asymmetric process fingerprints, as there is usually nothing designed to be asymmetric, so any observed asymmetry is indicative of a detrimental parameter that needs to be controlled/corrected.

In a particular use case, for 3D structures such as 3DNAND, it has been observed that one source of process asymmetry (process effect) is column tilt, and that column tilt corresponds directly to the overlay on the product. Thus, the proposed method can be used to provide a feedback mechanism for controlling the column tilt (e.g. by injecting a process fingerprint or a component thereof related to the column tilt), the result of which can also be used as a measure of the overlay and thus for monitoring/controlling the overlay on the product.

Thus, a concept is described that is able to extract the largest sources of process asymmetries and map them to wafer locations without any initial knowledge of the types of process fingerprints that may be present on the wafer, and without additional metrology (i.e., based on the same pupil as that which is being performed as part of the control or monitoring action). It may also provide knowledge of the structure on the wafer beyond the expected model domain.

While the method has been described in terms of the iterative method of fig. 6, it should be understood that model-based OCD calculations such as that described in fig. 5 may also extract the same information, and thus such methods fall within the scope of the present disclosure.

In connection with physical grating structures of a target structure as implemented on a substrate and a patterning device, embodiments may include a computer program containing one or more sequences of machine-readable instructions describing a method of measuring a target structure on a substrate and/or analysing measurements to obtain information about a lithographic process. The computer program may be executed, for example, in the unit PU in the device of fig. 3 or 4 and/or the control unit LACU of fig. 2. A data storage medium (e.g., semiconductor memory, magnetic or optical disk) having such a computer program stored therein may also be provided. In case an existing measuring device of the type as shown for example in fig. 3 or fig. 4 has been put into production and/or used, the invention may be implemented by providing an updated computer program product to cause a processor to perform the methods as described above and in the claims.

Further embodiments of the invention are disclosed in the following list of numbered clauses:

1. a method of determining whether a substrate or substrate portion is subject to a process effect, the method comprising:

obtaining inspection data comprising sets of measurement data associated with structures on the substrate or the substrate portion;

acquiring fingerprint data describing a spatial variation of a parameter of interest on the substrate or the substrate portion;

performing an iterative mapping of the inspection data to the fingerprint data; and

determining whether the substrate is subject to a process effect based on a degree to which the iterative mapping converges to a solution.

2. The method of clause 1, wherein the plurality of sets of measurement data comprises a plurality of sets of measurement data relating to a plurality of different locations on the substrate.

3. The method of clause 2, wherein the plurality of sets of measurement data comprises a plurality of sets of measurement data acquired from some or each of a plurality of different locations on the substrate using different ones of the plurality of acquisition settings.

4. The method of clause 1, 2 or 3, wherein each set of measurement data comprises angle-resolved intensity values.

5. The method of any preceding clause, wherein the fingerprint data relates to a fingerprint of a known process effect.

6. The method of any preceding clause, wherein the fingerprint data has been obtained from previous measurement data relating to at least one previous measurement of the known process effect.

7. The method of clause 6, wherein the fingerprint data comprises a principal component of the previous measurement data.

8. The method of any preceding clause, wherein the fingerprint data comprises polynomial data.

9. The method of clause 8, wherein the polynomial data comprises a zernike polynomial, a legendre polynomial, or a bezier function.

10. The method of any preceding clause, wherein the fingerprint data comprises substrate fingerprint data describing a spatial variation of the parameter of interest on the substrate.

11. The method of any preceding clause, wherein the fingerprint data comprises field fingerprint data describing a spatial variation of the parameter of interest over a field.

12. The method of any preceding clause, wherein the iterative mapping generates one or more weight mappings for each iteration.

13. The method of any preceding clause, wherein the step of performing iterative mapping comprises calculating a correction of a wafer background fingerprint that corrects fingerprint contributors other than the parameter of interest.

14. The method of clause 13, wherein the correcting of the wafer background fingerprint comprises performing a compositional analysis.

15. The method of clause 14, wherein the correcting of the wafer background fingerprint comprises: removing one or more principal components other than the first principal component when determining an updated fingerprint for each iteration of the iterative mapping.

16. The method of any preceding clause, wherein the determining whether the structure is subject to process effects is based on a convergence criterion.

17. The method of any preceding clause, including determining one or more values for the parameter of interest based on the inspection data.

18. The method of any preceding clause, wherein the fingerprint data is obtained from a library of sets of fingerprint data.

19. The method of clause 18, wherein the sets of fingerprint data comprise a plurality of examples of one, some, or all of the following: zernike polynomials, bezier functions, legendre polynomials, principal components from principal component analysis of metrology data relating to one or more metrology sources, known process fingerprints, historical process fingerprints, and predictions, random values of process fingerprints.

20. The method of clause 18 or 19, including repeating the method for each of a different set of fingerprint data of the plurality of sets of fingerprint data.

21. The method of clause 20, including setting a convergence criterion for determining whether the structure is process-effected based on a number of iterations and/or a size of a residual.

22. The method of clause 21, including reporting the strongest one or more sets of fingerprint data according to the convergence criteria.

23. The method of clause 22, including determining a control strategy or correction, and/or determining a root cause for the strongest one or more sets of fingerprint data.

The method of any of clauses 18-23, including the initial step of building the library.

25. The method of any preceding clause, wherein the parameter of interest is an overlay.

26. The method of any preceding clause, wherein each set of measurement data is associated with a pupil plane.

27. A metrology apparatus operable to perform the method of any one of clauses 1 to 26.

28. The metrology apparatus of clause 27, comprising:

a support for a substrate having a plurality of target structures thereon;

an optical system for measuring each target structure; and

a processor arranged to perform the method according to any of clauses 1 to 26.

29. A computer program comprising processor readable instructions which, when run on a suitable processor control apparatus, cause the processor control apparatus to perform the method according to any one of clauses 1 to 26.

30. A computer program carrier comprising a computer program according to clause 29.

Although specific reference may have been made above to the use of embodiments of the invention in the context of optical lithography, it will be appreciated that the invention may be used in other applications, for example imprint lithography, and where the context allows, is not limited to optical lithography. In imprint lithography, a topography in a patterning device defines the pattern created on a substrate. The topography of the patterning device may be pressed into a layer of resist provided to the substrate, the resist being cured by applying electromagnetic radiation, heat, pressure or a combination thereof. After the resist is cured, the patterning device is moved out of the resist, leaving a pattern therein.

The terms "radiation" and "beam" used herein encompass all types of electromagnetic radiation, including Ultraviolet (UV) radiation (e.g. having a wavelength of or about 365, 355, 248, 193, 157 or 126nm) and extreme ultra-violet (EUV) radiation (e.g. having a wavelength in the range of 5-20 nm), as well as particle beams, such as ion beams or electron beams.

The term "lens", where the context allows, may refer to any one or combination of various types of optical components, including refractive, reflective, magnetic, electromagnetic and electrostatic optical components.

The foregoing description of the specific embodiments will so fully reveal the general nature of the invention that others can, by applying knowledge within the skill of the art, readily modify and/or adapt for various applications such specific embodiments, without undue experimentation, without departing from the general concept of the present invention. Therefore, such adaptations and modifications are intended to be within the meaning and range of equivalents of the disclosed embodiments, based on the teaching and guidance presented herein. It is to be understood that the phraseology or terminology herein is for the purpose of description by way of example and not of limitation, such that the terminology or phraseology of the present specification is to be interpreted by the skilled artisan in light of the teachings and guidance.

The breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.