Method for configuring field of view of inspection device

Abstract

A method for configuring a field-of-view configuration of an inspection device relative to a pattern on a patterned substrate to be measured is disclosed. The method comprises: obtaining metrology data comprising at least one parameter distribution across at least a portion of the pattern, the at least one parameter distribution relating to a respective one or more parameters of interest and / or a respective one or more proxy parameters for a parameter of interest; determining variation indicator data for a varying field-of-view configuration, the variation indicator data quantifying an amount of variation acquired within the parameter distribution when the parameter distribution is limited to a field of view; and configuring the field of view configuration relative to the pattern based on the change indicator data.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to European application 22193741.0 filed on September 02, 2022, and the entire contents of this European application are incorporated herein by reference. Technical Field

[0003] The present invention relates to methods and apparatus for applying patterns to substrates during photolithography, and more particularly to methods and apparatus for detecting such patterns once applied. Background Art

[0004] A lithographic apparatus is a machine that applies a desired pattern to a substrate, usually to a target portion of the substrate. A lithographic apparatus may be used, for example, in the manufacture of integrated circuits (ICs). In that case, a pattern forming device (which is alternatively referred to as a mask or reticle) may be used to generate a circuit pattern to be formed on a single layer of the IC. This pattern may be transferred to a target portion (e.g., a portion comprising one or several dies) on a substrate (e.g., a silicon wafer). The transfer of the pattern is usually performed via imaging onto a layer of radiation-sensitive material (resist) disposed on the substrate. Typically, a single substrate will include a network of adjacent target portions that are patterned in succession. Known lithographic apparatus include so-called steppers, in which each target portion is irradiated by exposing the entire pattern to the target portion at once; and so-called scanners, in which each target portion is irradiated by scanning the pattern via a radiation beam in a given direction (the "scanning" direction) while synchronously scanning the substrate parallel or antiparallel to such direction. It is also possible to transfer a pattern from a pattern forming device to a substrate by imprinting the pattern onto the substrate.

[0005] To monitor the lithography process, parameters of the patterned substrate are measured. For example, the parameters may include the overlay error between successive layers formed in or on the patterned substrate, and the critical line width or critical dimension (CD) of the developed photoresist. Such measurements may be performed on production substrates and / or on dedicated metrology targets. Various techniques exist for making measurements of microscopic structures formed during the lithography process, including the use of scanning electron microscopes and various other specialized tools.

[0006] When performing a lithographic process such as applying a pattern on a substrate or measuring such a pattern, process control methods are used to monitor and control the process. Such process control techniques are often performed to obtain corrections to the control of the lithographic process. It would be desirable to improve such process control methods.

[0007] These process control methods typically rely on inspection or metrology of the exposed pattern, for example using a scanning electron microscope. When the exposed pattern is irregular, such as for logic circuits, there are many different feature types that cannot all be measured in a practical commercial setting. Thus, a method of configuring an inspection device is desired. Summary of the invention

[0008] In a first aspect of the present invention, there is provided a method for configuring a field of view configuration of an inspection device relative to a pattern on a patterned substrate to be measured, the method comprising: obtaining measurement data comprising at least one parameter distribution spanning at least a portion of the pattern, the at least one parameter distribution being associated with corresponding one or more parameters of interest and / or corresponding one or more proxy parameters for the parameters of interest; determining variation indicator data based on the measurement data for a varying field of view configuration, the variation indicator quantifying an amount of variation obtained within the parameter distribution when the parameter distribution is limited to a field of view; and configuring the field of view configuration relative to the pattern based on the variation indicator.

[0009] In a second aspect of the present invention, there is provided a computing device comprising a processor and configured to execute the method of the first aspect.

[0010] In a third aspect of the invention there is provided an inspection apparatus operable to image one or more features on a substrate and comprising the computing apparatus of the second aspect.

[0011] In a fourth aspect of the invention there is provided a computer program comprising program instructions operable to perform the method of the first aspect when run on a suitable apparatus.

[0012] Other aspects, features and advantages of the present invention, as well as the structure and operation of various embodiments of the present invention are described in detail below with reference to the accompanying drawings. It should be noted that the present invention is not limited to the specific embodiments described herein. These embodiments are presented herein only for illustrative purposes. Based on the teachings included in the present invention, those skilled in the relevant art will understand additional embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Embodiments of the present invention will now be described by way of example with reference to the accompanying drawings, in which:

[0014] Figure 1 Depicts lithographic equipment along with other equipment that forms a production facility for semiconductor devices;

[0015] Figure 2 is a flow chart of a simulation model corresponding to a subsystem of a lithography apparatus;

[0016] Figure 3 Two examples of random variations are schematically depicted: (a) line edge roughness LER; and (b) line width roughness (LWR);

[0017] Figure 4 shows a graph of dose E versus position x, illustrating the concept of blurring the ILS;

[0018] Figure 5 is a flow chart of a method for determining a relationship between random variations in a characteristic of an aerial image or a resist image and one or more design variables;

[0019] Figure 6 is a flow chart of a method for calculating and illustrating said random variation; and

[0020] Figure 7 is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] Before describing embodiments of the present invention in detail, it is instructive to present an example environment that can be used to implement embodiments of the present invention.

[0022] Figure 1 At 200, a lithographic apparatus LA is shown as part of an industrial production facility that implements a high-volume lithographic manufacturing process. In this example, the manufacturing process is adapted for the manufacture of semiconductor products (integrated circuits) on substrates such as semiconductor wafers. It will be appreciated by those skilled in the art that a wide variety of products can be manufactured by processing different types of substrates with variations of this process. The production of semiconductor products is used only as an example that has great commercial significance today.

[0023] In a lithographic apparatus (or simply "lithography tool" 200), a measurement station MEA is shown at 202, and an exposure station EXP is shown at 204. A control unit LACU is shown at 206. In this example, each substrate visits a measurement station and an exposure station to have a pattern applied. For example, in an optical lithography apparatus, a projection system is used to transfer a product pattern from a patterning device MA to a substrate and projection system using conditioned radiation and a projection system. This is done by forming an image of the pattern in a layer of radiation sensitive material.

[0024] The term "projection system" as used herein should be broadly interpreted to encompass any type of projection system suitable for the exposure radiation used or for other factors such as the use of immersion liquid or the use of vacuum, including refractive, reflective, reflective-refractive, magnetic, electromagnetic and electrostatic optical systems, or any combination thereof. The pattern forming MA device can be a mask or reticle that imparts a pattern to a radiation beam transmitted or reflected by the pattern forming device. Well-known operating modes include stepping mode and scanning mode. It is well known that the projection system can cooperate with the support and positioning system for the substrate and the pattern forming device in a variety of ways to apply the desired pattern to many target portions across the substrate. A programmable pattern forming device can be used instead of a mask with a fixed pattern. Radiation (for example) can include electromagnetic radiation in the deep ultraviolet (DUV) or extreme ultraviolet (EUV) band. The present invention is also applicable to other types of lithography processes, such as (for example) imprint lithography and direct write lithography by electron beam.

[0025] The lithographic apparatus control unit LACU controls all movement and measurement of the various actuators and sensors for receiving the substrate W and the mask MA and performing the patterning operation. The LACU also includes signal processing and data processing capabilities for performing the desired calculations associated with the operation of the apparatus. In practice, the control unit LACU will be implemented as a system of many sub-units, each of which delivers real-time data acquisition, processing and control of subsystems or components within the apparatus.

[0026] Before the pattern is applied to the substrate at the exposure station EXP, the substrate is processed at the measurement station MEA so that various preparatory steps can be performed. The preparatory steps may include using a level sensor to map the surface height of the substrate, and using an alignment sensor to measure the position of the alignment marks on the substrate. The alignment marks are nominally arranged in a regular grid pattern. However, due to the inaccuracy of the marks produced and also due to the deformation of the substrate throughout its process, the marks deviate from the ideal grid. Therefore, in addition to measuring the position and orientation of the substrate, the alignment sensor must also measure the positions of many marks across the substrate area in detail in practice (in the case where the device will print product features in the correct position with very high accuracy). The device can be of a so-called dual-platform type with two substrate stages, each of which has a positioning system controlled by a control unit LACU. While exposing one substrate on one substrate stage at the exposure station EXP, another substrate can be loaded onto the other substrate stage at the measurement station MEA, so that various preparatory steps can be performed. Therefore, the measurement of the alignment marks is very time-consuming, and providing two substrate stages will achieve a substantial increase in the throughput of the device. If the position sensor IF is not able to measure the position of the substrate table when the substrate table is at the measurement station and when it is at the exposure station, a second position sensor may be provided to enable the position of the substrate table to be tracked at both stations. The lithographic apparatus LA may, for example, be of a so-called dual stage type having two substrate tables and two stations (an exposure station and a measurement station) between which the substrate table is exchangeable.

[0027] Within a production facility, the apparatus 200 forms part of a "lithography cell" or "lithography cluster" which also includes a coating apparatus 208 for applying photoresist and other coatings to a substrate W for patterning by the apparatus 200. At the output side of the apparatus 200, a baking apparatus 210 and a developing apparatus 212 are provided for developing the exposed pattern into a solid resist pattern. Between all these apparatuses, a substrate transport system is responsible for supporting the substrate and transferring the substrate from one apparatus to the next. These apparatuses, often collectively referred to as track or coating and developing systems, are under the control of a track or coating and developing system control unit, which itself is controlled by a management control system SCS, which also controls the lithography apparatus via a lithography apparatus control unit LACU. Thus, different apparatuses can be operated to maximize throughput and processing efficiency. The management control system SCS receives recipe information R which provides a very detailed definition of the steps to be performed to produce each patterned substrate.

[0028] Once the pattern has been applied and developed in the lithography unit, the patterned substrate 220 is transferred to other processing equipment such as those illustrated at 222, 224, and 226. A wide range of processing steps are implemented by various equipment in a typical manufacturing facility. For example, in this embodiment, the equipment 222 is an etching station, and the equipment 224 performs a post-etching annealing step. Other physical and / or chemical processing steps are applied in other equipment 226, etc. Many types of operations may be required to manufacture real devices, such as deposition of materials, modification of surface material properties (oxidation, doping, ion implantation, etc.), chemical mechanical polishing (CMP), etc. In practice, the equipment 226 can represent a series of different processing steps performed in one or more equipment. As another example, equipment and processing steps for implementing self-aligned multi-patterning can be provided to produce multiple smaller features based on the laying of a precursor pattern by a lithography device.

[0029] As is well known, the manufacture of semiconductor devices involves many repetitions of such processing to create device structures with appropriate materials and patterns layer by layer on the substrate. Thus, the substrate 230 arriving at the lithography cluster may be a newly prepared substrate, or it may be a substrate that has been completely processed previously in the cluster or in another device. Similarly, depending on the required processing, the substrate 232 leaving the device 226 may be returned for subsequent patterning operations in the same lithography cluster, it may be destined for patterning operations in a different cluster, or it may be a finished product to be sent for dicing and packaging.

[0030] Each layer of the product structure requires a different set of different process steps, and the equipment 226 used at each layer can be completely different in type. In addition, even if the processing steps to be applied by the equipment 226 are nominally the same in a large facility, there may be several assumed identical machines working in parallel to perform step 226 on different substrates. Small differences or defects in the settings between these machines can mean that they affect different substrates in different ways. Even for steps that are relatively common to each layer, such as etching (equipment 222), it can be implemented by several etching equipment that are nominally the same but work in parallel to maximize throughput. In addition, in practice, different layers require different etching processes according to the details of the material to be etched, for example, chemical etching, plasma etching, and require specific requirements, such as anisotropic etching.

[0031] Previous and / or subsequent processes (as just mentioned) may be performed in other lithographic apparatuses as just mentioned, and may even be performed in different types of lithographic apparatuses. For example, some layers in the device manufacturing process that are very demanding in terms of parameters such as resolution and overlay may be performed in more advanced lithographic tools than other layers that are less demanding. Thus, some layers may be exposed in immersion lithographic tools, while other layers are exposed in "dry" tools. Some layers may be exposed in tools operating at DUV wavelengths, while other layers are exposed using EUV wavelength radiation.

[0032] In order to correctly and consistently expose the substrate exposed by the lithography apparatus, it is desirable to inspect the exposed substrate to measure properties such as overlay error between subsequent layers, line thickness, critical dimension (CD), etc. Therefore, a manufacturing facility in which a lithography unit LC is positioned also includes a metrology system that receives some or all of the substrates W that have been processed in the lithography unit. The metrology results are provided directly or indirectly to the management control system SCS. If an error is detected, the exposure of subsequent substrates can be adjusted, especially when the measurement can be performed quickly and quickly enough so that other substrates of the same batch are still to be exposed. In addition, the already exposed substrates can be stripped and reworked to improve the yield or discarded, thereby avoiding further processing of substrates that are known to be defective. In the case where only some target portions of the substrate are defective, other exposures can be performed only on those target portions that are good.

[0033] Figure 1 Also shown is a metrology device 240, which is provided for measuring parameters of the product at desired stages in the manufacturing process. A common example of a metrology station in a modern lithography production facility is a scatterometer (e.g. a dark field scatterometer, an angle resolved scatterometer or a spectral scatterometer), and it can be applied to measure the properties of the developed substrate at 220 before etching in the device 222. With the use of the metrology device 240, it can be determined that important performance parameters such as overlay or critical dimension (CD) do not meet specified accuracy requirements in the developed resist. Prior to the etching step, there is an opportunity to strip the developed resist and reprocess the substrate 220 via the lithography cluster. By managing small adjustments over time by the control system SCS and / or the control unit LACU 206, the metrology results 242 from the device 240 can be used to maintain accurate performance of the patterning operation in the lithography cluster, thereby minimizing the risk of producing defective products and requiring rework.

[0034] Another example of a metrology station is a scanning electron microscope (SEM), otherwise known as an electron beam (e-beam) metrology device, which may be included in addition to or as an alternative to a scatterometer. Thus, the metrology equipment 240 may include an electron beam or SEM metrology device alone or in addition to a scatterometer. Electron beam and SEM metrology devices have the advantage of measuring features directly (i.e., they image the features directly), rather than the indirect measurement techniques used in scatterometry (where the parameter value is determined by the reconstruction and / or asymmetry of the diffraction orders of the radiation diffracted by the measured structure). The main disadvantage of electron beam or SEM measurement devices is the much slower measurement speed than scatterometry, which limits the potential application of electron beam or SEM measurement devices to specific offline monitoring processes.

[0035] Additionally, metrology equipment 240 and / or other metrology equipment (not shown) may be applied to measure properties of processed substrates 232, 234 and incoming substrate 230. Metrology equipment may be used on processed substrates to determine important parameters such as overlay or CD.

[0036] Lithographic projection equipment typically projects a patterned (i.e., through a mask) image at a point immediately above the substrate, and then ultimately into the resist. The projected image is called an aerial image, which includes a distribution of light intensity that varies depending on the spatial position in the image plane. The aerial image is the source of information exposed into the resist, thereby forming a gradient of dissolution rates that enables a three-dimensional resist image to be presented during development.

[0037] In a lithographic projection apparatus, a source provides illumination (i.e., radiation) to a pattern forming device, and projection optics directs the illumination onto a substrate via the pattern forming device and shapes the illumination. Here, the term "projection optics" is broadly defined to include any optical component that can change the wavefront of a radiation beam. For example, the projection optics may include at least some of components 14A, 16Aa, 16Ab, and 16Ac. The aerial image (AI) is the distribution of radiation intensity at the substrate level. The resist layer on the substrate is exposed, and the aerial image is transferred to the resist layer to serve as a potential "resist image" (RI) therein. The resist image (RI) may be defined as the spatial distribution of the solubility of the resist in the resist layer. A resist model may be used to calculate a resist image from an aerial image, and an example of this situation may be found in U.S. Patent Application Publication No. US 2009-0157360, the entire disclosure of which is hereby incorporated by reference herein. The resist model relates only to the properties of the resist layer (e.g., the effects of chemical processes occurring during exposure, PEB, and development). The optical properties of a lithographic projection apparatus (e.g., properties of the source, patterning device, and projection optics) specify an aerial image. Since the patterning device used in a lithographic projection apparatus can be varied, it is desirable to separate the optical properties of the patterning device from the optical properties of the rest of the lithographic projection apparatus, including at least the source and projection optics.

[0038] Figure 2 is an exemplary flow chart for simulating lithography in a lithographic projection apparatus. A source model 31 represents the optical properties of a source (including radiation intensity distribution and / or phase distribution). A projection optics model 32 represents the optical properties of the projection optics (including changes to the radiation intensity distribution and / or phase distribution caused by the projection optics). A design layout model 35 represents the optical properties of a design layout (including changes to the radiation intensity distribution and / or phase distribution caused by a given design layout 33), which is a representation of the arrangement of features on or formed by a pattern forming device. An aerial image 36 can be simulated from the design layout model 35, the projection optics model 32, and the design layout model 35. A resist image 38 can be simulated from the aerial image 36 using a resist model 37. The simulation of lithography can, for example, predict the profile and CD in the resist image.

[0039] More specifically, it should be noted that the source model 31 can represent the optical properties of the source, including but not limited to NA settings, sigma settings, and any specific illumination shape (e.g., off-axis radiation sources, such as toroidal, quadrupole, dipole, etc.). The projection optics model 32 can represent the optical properties of the projection optics, including aberrations, deformations, one or more refractive indices, one or more physical sizes, one or more physical dimensions, etc. The design layout model 35 can represent one or more physical properties of a physical pattern forming device, as described, for example, in U.S. Patent No. 7,587,704, which is incorporated herein by reference in its entirety. The goal of the simulation is to accurately predict, for example, edge placement, aerial image intensity slope, and / or CD, which can then be compared to an intended design. An intended design is typically defined as a pre-OPC design layout that can be provided in a standardized digital file format such as GDSII or OASIS or other file formats.

[0040] From this design layout, one or more portions, referred to as "clips," may be identified. In an example, a set of clips are extracted that represent complex patterns in the design layout (typically about 50 to 1000 clips, but any number of clips may be used). These patterns or clips represent small portions of the design (i.e., circuits, cells, or patterns), and more particularly, the clips typically represent small portions that require special attention and / or verification. In other words, a clip may be a portion of a design layout, or may be similar or have similar behavior to a portion of a design layout, where one or more key features are identified through experience (including clips provided by customers), trial and error, or running full-chip simulations. A clip may include one or more test patterns or gauge patterns.

[0041] An initial larger set of segments may be provided a priori by the customer based on one or more known key feature areas in the design layout that require specific image optimization. Alternatively, in another embodiment, an initial larger set of segments may be extracted from the entire design layout by using some automated (such as machine vision) or manual algorithm that identifies the one or more key feature areas.

[0042] For example, stochastically induced failure predictions are often made based on measurements of variations in dimensional parameters such as CD (so-called local CD uniformity, LCDU), line edge position (so-called line edge roughness, LER), or line width (so-called line width roughness, LWR). Accurately measuring the number of failures is cumbersome, since low failure rates (e.g., on the order of one part per million to one part per billion) can be expected in an optimized process. Therefore, failure predictions are based on stochastic LCDU, LER, or LWR predictions, which rely on relatively few measurements. In particular, it is well known that stochasticity is driven by optical contrast, and more particularly, by the image logarithmic slope (ILS). The ILS is the gradient of the logarithm of the spatial image intensity I:

[0043]

[0044] Since the variation of resist edge position (line width) is usually expressed as a percentage of the nominal line width, the position coordinate x can be normalized by multiplying the ILS by the nominal line width w to obtain the so-called normalized ILS (NILS):

[0045]

[0046] The concept of using ILS for randomly induced fault prediction is known. Such a concept can be found in, for example, PCT patent publications WO2015 / 121127 and WO2016 / 128392, the contents of which are incorporated herein in their entirety.

[0047] Randomness is more important for extreme ultraviolet (EUV) lithography because the features are smaller, as is the number of photons in the exposure radiation (due to their higher energy), relative to lithography techniques using lower energy exposure radiation. EUV radiation is electromagnetic radiation having a wavelength in the range of 5 nm to 20 nm (e.g., in the range of 13 nm to 14 nm). It has been additionally proposed that EUV radiation with a wavelength less than 10 nm, such as in the range of 5 nm to 10 nm (e.g., 6.7 nm or 6.8 nm), could be used. Such radiation is known as extreme ultraviolet radiation or soft x-ray radiation. Possible sources for EUV radiation include, for example, laser-produced plasma sources, discharge plasma sources, or sources based on synchrotron radiation provided by electron storage rings.

[0048] Imaging using a lithographic projection apparatus will cause random variations in one or more parameters, such as significant line width roughness (LWR), and local CD variations for small two-dimensional features such as holes. The random variations can be attributed to factors such as photon shot noise in the resist, secondary electrons generated by photons, variations in photon absorption, and / or acid generation by photons. In the case of EUV lithography, the small feature sizes required for EUV further compound and exacerbate such random variations. Random variations in smaller features are a significant factor in product yield and justify their inclusion in various optimization processes for the lithographic projection apparatus.

[0049] At the same radiation intensity, a lower exposure time per substrate results in a higher throughput of the lithographic projection apparatus, but results in stronger stochastic effects. The photon shot noise in a given feature at a given radiation intensity is proportional to the square root of the exposure time. In lithography using EUV and other radiation sources, there is a desire to reduce exposure time for the purpose of increasing the throughput. Therefore, the methods and apparatus described herein that take into account the stochastic effects in the optimization process are not limited to EUV lithography.

[0050] The throughput may also be affected by the total amount of light directed to the substrate. In some lithographic projection apparatus, a portion of the light from the source is sacrificed in order to achieve a desired shape of the source (e.g. pupil filling ratio). Typically, if the pupil filling ratio is lower, a higher (N) ILS is obtained, and thus there is a tradeoff.

[0051] Figure 3 (a) Schematic depiction of the random effect, line edge roughness, LER. Assuming all conditions are the same in three exposures or exposure simulations of an edge 903 of a feature on a design layout, the resist images 903A, 903B, and 903C of the edge 903 may have slightly different shapes and locations. The locations 904A, 904B, and 904C of the resist images 903A, 903B, and 903C may be measured by averaging the resists 903A, 903B, and 903C to obtain effective (averaged) edge locations 902A, 902B, and 902C, respectively. The LER of the edge 903 may be a measure of the spatial distribution of the locations 904A, 904B, and 904C. For example, the LER may be 3σ of the spatial distribution (assuming the distribution is a normal distribution). The LER may be derived from many exposures or simulations of the edge 903.

[0052] Figure 3(b) Schematic depiction of LWR. Assuming all conditions are the same in three exposures or exposure simulations of a long rectangular feature 910 with width 911 on the design layout, the resist images 910A, 910B, and 910C of the rectangular feature 910 may have slightly different widths 911A, 911B, and 911C, respectively. The LWR of the rectangular feature 910 may be a measure of the distribution of the widths 911A, 911B, and 911C. For example, the LWR may be 3σ of the distribution (assuming the distribution is a normal distribution). The LWR may be derived from many exposures or simulations of the rectangular feature 910. In the context of a short feature (e.g., a contact hole), the width of the image of the feature is not well defined because the long edge is not available to average the location of the image of the feature. A similar quantity LCDU may be used to characterize the random variation. LCDU is 3σ of the distribution of the measured CD of the image of the short feature (assuming the distribution is a normal distribution).

[0053] The nature of randomness is related to the absorbed dose, which fluctuates due to the finite number of (absorbed) photons. This is actually reflected in the CD variation between different features and / or over the length of the feature. Thus, random variation as described herein may include a measure of variation in any dimensional parameter, and thus may include line edge roughness (LER), line width roughness (LWR), LCDU, hole LCDU, circle edge roughness (CER), edge placement error (EPE), or a combination thereof.

[0054] In an embodiment, the design variables may include an ILS (eg, more particularly, a fuzzy ILS (ILS B ), dose and / or image intensity (e.g. spatial image intensity). Blurred ILS (ILS B ) is the image log slope ILS (or normalized ILS) with spatial blur applied (e.g., by convolution with a Gaussian distribution) such that the blurred ILS has less contrast / slope than the unblurred aerial image. The spatial blur may represent blurring of the resist image due to diffusion of chemicals produced in the resist layer by exposure to radiation.

[0055] Figure 4 ILS is a fuzzy ILS. B can be used to convert local dose variations into local CD (or another random parameter) variations. It shows a plot of dose E versus position x, with the curve representing the absorbed dose within the resist. Due to the finite number of photons, the absorbed dose follows Poissonian statistics, giving rise to an intrinsic local dose variation σ dose , which in turn causes the number of photons absorbed by the resist to be <N ph > Change. Fuzzy ILS is ILSB including the ILS defined by the aerial image and this local dose variation σ dose Fuzzy ILS is ILS B The local dose variation σ dose The resulting local CD variation LCDU is shown here to describe the CD variation range of a circular feature CF, but this is equally applicable to any feature. According to the following equation, LCDU (in nm) will depend on the blur ILS, that is, ILS B and the number of photons absorbed by the resist < N ph >:

[0056]

[0057] in As the local dose changes σ dose And zoom.

[0058] Figure 5 1301, the value of the characteristic is measured from a plurality of aerial images or resist images formed (by actual exposure or simulation) for each of a plurality of value sets of the one or more design variables. In step 1302, the value of the random variation is determined for each value set of the one or more design variables based on the distribution of the values ​​of the characteristic measured for the aerial images or resist images formed for the value sets of the one or more design variables. In step 1303, the relationship is determined by fitting one or more parameters of a model from the values ​​of the random variation and the value sets of the one or more design variables.

[0059] In an example, the random variation is LER and the one or more design variables is the blurred image ILS , dose E and image intensity I. The model can be:

[0060]

[0061] The parameters a, b and c can be determined by fitting. Blurred image ILS is the image logarithmic slope ILS with spatial blur applied. The spatial blur may represent the blurring of the resist image due to diffusion of chemical species generated in the resist layer by exposure to radiation.

[0062] Once the relationship between the random variation in a characteristic of the aerial image or resist image and one or more design variables is determined, the relationship can be used to calculate a value for the random variation for the characteristic.

[0063] Figure 6 An exemplary flow chart for such calculation is shown. In step 1610, a set of conditions is selected (e.g., NA, s, dose, focus, resist chemical reaction, one or more projection optical device parameters, one or more irradiation parameters, etc.). In step 1620, the values ​​of one or more design variables are calculated under these conditions. For example, the edge position of the resist image and the value of the blurred ILS along the edge are calculated. In step 1630, the value of the random variation is calculated based on the relationship between the random variation and the one or more design variables. For example, in an embodiment, the random variation is the LER of the edge. In optional step 1640, a noise vector can be defined, and the frequency distribution of the noise vector roughly matches the real substrate measurement. In optional step 1650, the noise vector is superimposed on the result (e.g., the random edge of the aerial image or resist image).

[0064] The relationship between the random variation of the characteristics of the aerial image or resist image and one or more design variables can also be used to identify one or more "hot spots" or key features of the aerial image or resist image. A "hot spot" can be defined as a location on the image where the random variation exceeds a certain value. For example, if two locations on two nearby edges have large LER values, then these two locations have a high chance of joining each other.

[0065] In an example, the value of the random variation (and / or its function) under multiple conditions and multiple values ​​of one or more design variables can be calculated and compiled in a non-transitory computer-readable medium, such as a database stored on a hard drive. A computer can query the medium and calculate the value of the random variation from the contents of the medium.

[0066] Determination of random variations in properties of an air / resist image can be useful in many ways during lithography. In one example, the random variations can be accounted for in optical proximity correction (OPC).

[0067] As an example, OPC addresses the fact that the final size and placement of the image of the design layout projected on the substrate will not be the same as, or will depend only on, the size and placement of the design layout on the patterning device. It should be noted that the terms "mask", "reticle", and "patterning device" are used interchangeably herein. In addition, those skilled in the art will recognize that the terms "mask" / "patterning device" and "design layout" can be used interchangeably, especially in the context of lithography simulation / optimization, such as in lithography simulation / optimization, it is not necessary to use a physical patterning device, but a design layout can be used to represent a physical patterning device. For the small feature sizes and high feature densities present on some design layouts, the location of a particular edge of a given feature will be affected to some extent by the presence or absence of other neighboring features. These proximity effects arise due to minute amounts of radiation coupled from one feature to another and / or non-geometric optical effects such as diffraction and interference. Similarly, proximity effects can arise due to diffusion and other chemical effects during, for example, post-exposure baking (PEB), resist development, and etching, which are typically subsequent to lithography.

[0068] For irregular or non-repeating patterns, such as those of logic structures, there are many different features. In such cases, due to changes in the optical proximity effect caused by different local mask transmittances (LMTs), the lithography performance will vary from one feature to another, that is, the lithography performance will vary from feature to feature. Therefore, due to the number and diversity of different features present in these patterns, the measurement of the lithography performance used to characterize these features should cover a wide measurement area in order to check a sufficient variety of features. Ideally, a complete characterization of lithography performance would require SEM measurements of the entire area containing all features. However, this is too expensive in terms of time.

[0069] An alternative approach to measuring a large area is to focus on, i.e., pay attention to, a few segments or hot spots / hot spots and monitor, for example, via pitch. This can cover some feature ranges within the logic design. However, hot spots are usually not clustered in small areas, so a single or limited number of SEM images using a limited field of view (FOV) will not capture all species, even in just the hot spots. These hot spots are likely to have very different lithographic properties.

[0070] The size of the SEM's FOV sets the size of the pattern area that is imaged, and therefore how many individual features are acquired and measured. Which individual features are acquired further depends on the position of the FOV relative to the pattern.

[0071] Currently, sub-areas of the exposed pattern (i.e., die pattern) are measured using SEM and used to calculate the performance of all mask features. A grouping approach can be used that groups features into groups with similar lithographic performance, so that only a subset of each group needs to be measured, assuming that all features of the group behave identically.

[0072] Since the pattern is not uniform, the selection of the FOV or set of FOVs for the SEM is important. The FOV will determine how accurately the imaging performance of a unique feature can be calculated based on the metrology data obtained in the measurement. Therefore, it is desirable to determine the optimal FOV (or set of FOVs) before performing any measurements. It should be noted that while in some embodiments, a method for configuring a single FOV will be determined, the concepts disclosed herein can be used to determine a set of FOVs (e.g., each FOV covering a respective different area of ​​the pattern), for example, provided that the set of FOVs can be acquired within an acceptable time or an allocated metrology budget.

[0073] Additionally, multiple performance indicators or parameters of interest may be calculated from the measurements, such as, for example, CD and placement error (PE) / edge placement error (EPE). These multiple parameters of interest may also be directionally calculated for different features. Each of these parameters of interest may have a different optimal FOV; however, it is also not desirable to measure a different FOV for each parameter of interest.

[0074] Thus, a method is proposed for configuring a field of view (FOV) configuration of an inspection device relative to a patterned substrate to be measured, the method comprising: obtaining at least one parameter distribution of interest across at least a portion of the patterned substrate; determining a variation indicator for varying FOV configurations, the variation indicator quantifying an amount of variation obtained within the parameter distribution when the parameter distribution is limited to the FOV; and configuring the FOV configuration relative to the pattern based on the variation indicator.

[0075] The FOV configuration may describe the size and / or position of a FOV or a set of FOVs.

[0076] The at least one parameter distribution of interest may include a plurality of parameter distributions of interest, each of the plurality of parameter distributions of interest being associated with a different respective parameter of interest, such that the FOV or set of FOVs is optimized for or across a plurality of different parameters of interest.

[0077] The FOV configuration method can be based on an "optimal design" objective, which maximizes the information obtained from the experimental design. In experimental design, an optimal design is a class of experimental designs that are optimal with respect to some statistical criteria. In experimental designs for estimating statistical models, optimal designs allow parameters to be estimated without bias and with minimal variance. Non-optimal designs require a larger number of experimental runs to estimate the parameters with the same accuracy as the optimal design. In practice, optimal experiments can reduce experimental costs. The optimality of the design depends on the statistical model and is evaluated relative to statistical criteria, for example, the statistical criteria are related to the variance matrix of the estimator.

[0078] Maximizing the information obtained from the experimental design is equivalent to minimizing the expected variance of the statistical model used to estimate the parameter of interest from the experiment (measurement). The optimal design depends on the category of the statistical model. The method proposed in this article can use a linear estimator for the parameter of interest. This covers a wide range of models without the need to design for a specific model selection.

[0079] In an embodiment, the measurements for optimizing the FOV (or a set of complementary FOVs) may be used to estimate a parameter of interest for a heterogeneous layout feature, or jointly estimate multiple parameters of interest for a heterogeneous layout feature. Examples of these parameters of interest may include one or more imaging parameters, such as one or more of CD, PE, LCDU, and / or LPE in horizontal and / or vertical directions / orientations.

[0080] Figure 7 is a flow chart describing a method of configuring a FOV or a set of FOVs according to an embodiment. At step 700, the method may begin by obtaining measurement data that may be used to estimate the inter-feature covariance of a parameter of interest. The data may be simulated measurement data, measured measurement data, or a combination of both. The measurement data may include data for one or more parameters of interest, for example, the measurement data may describe a different parameter of interest for each of the features.

[0081] Using simulated measurement data before performing any measurement can be the most practical implementation. In the case of using simulated measurement data, the simulated measurement data can be obtained by simulating the parameters of interest using lithography simulation. The simulation can be performed for varying imaging conditions (e.g., within the expected range that may be encountered in an actual lithography process). The varying imaging conditions can be, for example, focus and / or dose. When the parameter of interest cannot be simulated directly, another image parameter that has a theoretical correlation with the parameter of interest can be used as a proxy. For example, instead of LCDU, the image logarithmic slope ILS can be used instead. More generally, a complete 3D resist spatial image can be simulated, which can be used as a proxy parameter related to all parameters of interest.

[0082] Other simulated metrology data that may be used (as an alternative or in addition to other data types discussed herein) may also include mask aerial image measurement (AIMS) data. AIMS data may simulate the aerial image produced via a mask (as described in LMT data or layout data or any suitable description of the pattern, e.g., as can be determined from a.gds archive) using a specific lithography tool (scanner) and specific scanner settings that indicate the actual tool and settings to be used. AIMS is described in the following publications: AM Zibold et al., "Aerial Image Measurement Technique for Today's and Future 193nm Lithography Mask Requirements"; 10.1117 / 12.568016; SPIE Conference Proceedings (incorporated herein by reference).

[0083] Alternatively or in addition to other data types discussed herein, the metrology data may be measured metrology data, such as SEM measurements from an exposed wafer (having had the pattern exposed thereon.) In such a case, the parameters of interest for all features or a subset of features may be measured during a setup phase with a large number of measurements.

[0084] At step 710, a measurement data matrix X is constructed. This measurement data matrix may describe a simulation / measurement parameter (column) per layout coordinate (row). The layout coordinate may correspond to a specific segment of the edge of the polygon (layout feature) to which the parameter belongs.

[0085] At step 720, a dimensionality reduction step may be performed. This may include, for example, computing the singular value decomposition (X = USV T ). For the subsequent steps, keep the first few left singular vectors corresponding to the largest singular values. These first few left singular vectors can be represented by the matrix Uk Described by the matrix U k consists of the first k columns of the matrix U, where k can be any number less than 20, or for example any number less than 10.

[0086] At step 730, for a given FOV size (or given multiple FOV sizes, i.e., a given FOV size may be associated with non-contiguous windows), an exhaustive search for all possible FOVs (candidate FOVs) and locations may be performed. This may be achieved, for example, by scanning a sliding window over the pattern or associated unit cells of the layout. The features acquired in each window may be recorded. For example, the index set of features falling in the i-th window may be described as r i .

[0087] At step 740, for each window, an optimality criterion may be calculated; for example, using the row r corresponding to the feature in the window i and the submatrix consisting of the first k columns of matrix U (No dimensionality reduction is performed, and the matrix ). Depending on the goal, different optimality criteria can be used based on the determination of the change indicator. Examples of different optimality criteria include:

[0088] AOptimal criteria: . is a measure of the expected total variance of the error of the linear estimator trained on the measurements of the i-th window.

[0089] E optimal criteria: ,in, is a matrix The minimum singular value of is a measure of the expected variance of the worst parameter or worst case error of a linear estimator trained on the measurements of the i-th window. It should be noted that the worst case does not have to be one of the parameters of interest, e.g., the parameter with the highest correlated error. For example, it can also be a linear combination of these parameters with the highest correlated error.

[0090] At step 750, steps 730 and 740 may be repeated for different FOV sizes to establish a tradeoff between FOV size (related to the cost of the measurement) and the optimality measure (benefit).

[0091] At step 760, the node with the smallest change index (e.g., the smallest or The window serves as the optimal FOV for measurement.

[0092] Other embodiments of the invention are disclosed in the following list of numbered aspects:

[0093] 1. A method for configuring a field of view configuration of an inspection device relative to a pattern on a patterned substrate to be measured, the method comprising: obtaining measurement data including at least one parameter distribution across at least a portion of the pattern, the at least one parameter distribution being associated with corresponding one or more parameters of interest and / or corresponding one or more proxy parameters for the parameters of interest; determining variation indicator data based on the measurement data for a varying field of view configuration, the variation indicator data quantifying an amount of variation obtained within the parameter distribution when the parameter distribution is limited to a field of view; and configuring the field of view configuration relative to the pattern based on the variation indicator data.

[0094] 2. A method according to clause 1, wherein the field of view configuration describes the size and / or position of a field of view or the corresponding sizes and / or positions of a set of complementary fields of view.

[0095] 3. A method according to aspect 1 or 2, wherein the at least one parameter distribution comprises a plurality of parameter distributions, each parameter distribution being associated with a different corresponding parameter of interest or proxy parameter, such that the field of view configuration is configured for a number of different parameters.

[0096] 4. A method according to any preceding aspect, wherein the one or more parameters of interest include one or more of critical dimensions, placement error, edge placement error, line placement error and / or local CD uniformity in one or two perpendicular orientations of the substrate plane.

[0097] 5. A method according to any preceding aspect, wherein the one or more proxy parameters include a simulated resist aerial image and / or an image logarithmic slope.

[0098] 6. A method according to any preceding aspect, wherein the measurement data comprises analog measurement data.

[0099] 7. The method according to aspect 6, comprising performing an initial simulation step to obtain the simulated measurement data.

[0100] 8. The method of clause 7, comprising performing the simulation step for various imaging conditions.

[0101] 9. The method according to aspect 8, wherein the imaging conditions may include focus and / or dose.

[0102] 10. A method according to any preceding clause, wherein the metrology data comprises measured metrology data from a previously exposed substrate which has had the pattern exposed thereon.

[0103] 11. A method according to any preceding clause, wherein the pattern comprises a plurality of different features.

[0104] 12. A method according to any of the preceding aspects, wherein the configuration step includes configuring the field of view configuration so as to minimize the expected variance described by the variation indicator data of a statistical model used to estimate the at least one parameter of interest and / or at least one proxy parameter based on the measurement data.

[0105] 13. A method according to any of the preceding aspects, comprising: limiting a field of view size; performing a search for candidate fields of view on the pattern, each candidate field of view having the field of view size; and determining an optimality criterion for each of the candidate fields of view, the optimality criterion being based on the variation indicator.

[0106] 14. The method of clause 13, wherein the step of performing a search comprises: scanning a sliding window defined by the field of view size over a unit cell of the pattern; and determining the feature within each window.

[0107] 15. A method according to aspect 13 or 14, wherein the optimality criterion comprises an A-optimality criterion.

[0108] 16. A method according to clause 15, wherein the variation indicator is a measure of the expected total variance of the error of a linear estimator trained on measurements defined by each candidate field of view.

[0109] 17. A method according to aspect 13 or 14, wherein the optimality criterion comprises an E-optimality criterion.

[0110] 18. A method according to clause 17, wherein the variation indicator is a measure of the expected variance of the error of the worst parameter or combination of parameters of a linear estimator trained on measurements defined by each candidate field of view.

[0111] 19. A method according to any preceding aspect, comprising performing an initial dimensionality reduction on the measurement data.

[0112] 20. The method of clause 19, wherein the dimensionality reduction comprises singular value decomposition, wherein the measurement data is replaced with a first one or more left singular vectors corresponding to a maximum singular value for the steps of determining a variation indicator and configuring a field of view configuration.

[0113] 21. A method according to any preceding aspect, wherein the inspection device comprises an electron microscope.

[0114] 22. A method according to any preceding clause, comprising performing metrology on a substrate using the field of view configuration.

[0115] 23. A computing device comprising a processor and configured to perform the method according to any preceding aspect.

[0116] 24. An inspection apparatus operable to image one or more features on a substrate and comprising a computing apparatus according to clause 23.

[0117] 25. The inspection apparatus according to aspect 24, wherein the inspection apparatus is an electron microscopy inspection apparatus.

[0118] 26. The inspection apparatus according to aspect 24, wherein the inspection apparatus is a scanning electron microscopy inspection apparatus or a transmission electron microscopy inspection apparatus.

[0119] 27. A computer program comprising program instructions operable to perform the method according to any of clauses 1 to 21 when run on a suitable device.

[0120] 28. A non-transitory computer program carrier comprising a computer program according to clause 27.

[0121] Although the concept is disclosed in terms of a scanning electron microscope (SEM) including an electron beam metrology / inspection tool, it is also applicable to any other metrology or inspection equipment with a configurable FOV in terms of the size and / or position of the pattern being measured. For example, the metrology / inspection equipment may be any other electron microscope, such as a transmission electron microscope (TEM).

[0122] The terms “radiation” and “beam” as used with respect to a lithographic apparatus encompass all types of electromagnetic radiation, including ultraviolet (UV) radiation (e.g., having a wavelength of or about 365 nm, 355 nm, 248 nm, 193 nm, 157 nm, or 126 nm) and extreme ultraviolet (EUV) radiation (e.g., having a wavelength in the range of 5 nm to 20 nm), as well as particle beams, such as ion beams or electron beams.

[0123] The term "lens" may refer, where the context permits, to any one or combination of various types of optical components, including refractive, reflective, magnetic, electromagnetic, and electrostatic optical components.

[0124] The foregoing description of specific embodiments will therefore fully reveal the general nature of the invention: without departing from the general concept of the invention, others can easily modify and / or adapt these specific embodiments for various applications by applying the knowledge understood by those skilled in the art without undue experimentation. Therefore, based on the teachings and guidance presented by the present invention, it is hoped that these adaptations and modifications belong to the meaning and scope of equivalents of the disclosed embodiments. It should be understood that the words or terms herein are for the purpose of description by example rather than limitation, so that the terms or words of this specification should be interpreted by those skilled in the art in view of the teachings and guidance.

[0125] Thus, 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.

Claims

1. A method for configuring a field of view configuration of an inspection device relative to a pattern on a patterned substrate to be measured, the method comprising: obtaining measurement data comprising at least one parameter distribution across at least a portion of the pattern, the at least one parameter distribution being associated with respective one or more parameters of interest and / or respective one or more proxy parameters for the parameters of interest; determining variation indicator data from the measurement data for a varying field of view configuration, the variation indicator data quantifying an amount of variation obtained within the parameter distribution when the parameter distribution is restricted to the field of view; as well as The field of view configuration is configured relative to the pattern based on the variation indicator data.

2. The method according to claim 1, wherein: The field of view configuration describes the size and / or position of a field of view or the corresponding sizes and / or positions of a set of complementary fields of view.

3. The method according to claim 1, wherein The at least one parameter distribution comprises a plurality of parameter distributions, each parameter distribution being associated with a different respective parameter of interest or proxy parameter, such that the field of view configuration is configured for a number of different parameters.

4. The method according to claim 1, wherein: The one or more parameters of interest include one or more of a critical dimension, a placement error, an edge placement error, a line placement error, and / or a local CD uniformity in one or two perpendicular orientations to the substrate plane.

5. The method according to claim 1, wherein: The one or more proxy parameters include a simulated resist aerial image and / or an image log slope.

6. The method according to claim 1, wherein: The measurement data includes analog measurement data.

7. The method of claim 6, comprising performing an initial simulation step to obtain the simulated measurement data.

8. The method according to claim 1, wherein: The metrology data includes measured metrology data from a previously exposed substrate that had the pattern exposed thereon.

9. The method according to claim 1, wherein: The pattern includes a plurality of distinct features.

10. The method according to claim 1, wherein: The configuring step comprises configuring the field of view configuration so as to minimize an expected variance described by the variation indicator of a statistical model used for estimating the at least one parameter of interest and / or at least one proxy parameter from the measurement data.

11. The method according to claim 1, comprising: Limit the size of the field of view; performing a search for candidate fields of view relative to the pattern, each candidate field of view having the field of view size; and An optimality criterion is determined for each of the candidate fields of view, the optimality criterion being based on the variation indicator.

12. The method according to claim 11, wherein: The step of performing the search includes: scanning a sliding window defined by the field of view size over a unit cell of the pattern; and determining the feature within each window.

13. The method of claim 1, comprising performing an initial dimensionality reduction on the measurement data.

14. A computer program comprising program instructions operable to perform the method according to any one of claims 1 to 13 when run on a suitable device.

15. A non-transitory computer program carrier comprising a computer program according to claim 14.

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