Method for determining field of view settings

Abstract

A method for determining a field of view setting for an inspection tool with a configurable field of view FOV is disclosed herein, the method comprising: obtaining a process margin distribution of features on at least a portion of a substrate; obtaining a threshold; identifying one or more regions on at least a portion of the substrate in dependence on the obtained process margin distribution and the threshold; and determining the field of view setting in dependence on the identified one or more regions.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the priority of European application No. 19201911.5 filed on October 8, 2019 and European application No. 19209797.0 filed on November 18, 2019, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The description herein relates to fabrication, testing, measurement, and other processes that may be performed on semiconductor structures fabricated on substrates, and more particularly to methods, non-transitory computer-readable media, and systems for improving any of the processes relying on images of features of the structures. Background Art

[0004] A lithographic projection apparatus may be used, for example, to manufacture integrated circuits (ICs). In this case, a patterning device (e.g., a mask) may contain or provide a circuit pattern (a "design layout") corresponding to a separate layer of the IC, and this circuit pattern may be transferred to a target portion (e.g., comprising one or more dies) on a substrate (e.g., a silicon wafer) that has been coated with a layer of radiation-sensitive material ("resist") by a method such as irradiating the target portion through the circuit pattern on the patterning device. Typically, a single substrate comprises a plurality of adjacent target portions to which the circuit pattern is transferred successively, one target portion at a time, by the lithographic projection apparatus. In one type of lithographic projection apparatus, the circuit pattern on the entire patterning device is transferred to one target portion at a time; this apparatus is often referred to as a stepper. In an alternative apparatus, often referred to as a stepper-scan apparatus, the projection beam is scanned across the patterning device in a given reference direction (the "scanning" direction) while the substrate is synchronously moved parallel or antiparallel to this reference direction. Different portions of the circuit pattern on the patterning device are progressively transferred to one target portion. Typically, because the lithographic projection apparatus will have a magnification factor M (typically <1), the speed F at which the substrate is moved will be a factor M times the speed at which the projection beam scans the patterning device. Further information on lithographic apparatus as described herein can be gleaned, for example, from US 6,046,792, incorporated herein by reference.

[0005] Before the circuit pattern is transferred to the substrate from the pattern forming device, the substrate can undergo various processes, such as primer coating, resist coating and soft baking. After exposure, the substrate can undergo other processes, such as post-exposure baking (PEB), development, hard baking and measurement / inspection of the transferred circuit pattern. This series of processes is used as the basis for the individual layers of the manufacturing device (e.g., IC). Then, the substrate can undergo various processes such as etching, ion implantation (doping), metallization, oxidation, chemical mechanical grinding, etc., which are all intended to complete the individual layers of the device. If several layers are needed in the device, the entire process or its variation is repeated for each layer. Eventually, there will be devices in each target portion on the substrate. Then, these devices are separated from each other by technologies such as scribing or sawing, so that each device can be mounted on a carrier, connected to pins, etc.

[0006] As mentioned, photolithography is a core step in the fabrication of ICs, where patterns formed on a substrate define the functional elements of the IC, such as microprocessors, memory chips, etc. Similar photolithography techniques are also used to form flat panel displays, microelectromechanical systems (MEMS), and other devices.

[0007] As semiconductor manufacturing processes continue to advance, the size of functional elements has been continuously reduced for decades, while the number of functional elements such as transistors per device has been steadily increasing, following a trend generally referred to as "Moore's Law." In the current state of the art, the layers of a device are manufactured using a lithography projection apparatus that projects a design layout onto a substrate using illumination from a deep ultraviolet illumination source, thereby producing individual functional elements having a size well below 100 nm (i.e., less than half the wavelength of the radiation from the illumination source (e.g., an illumination source of 193 nm)).

[0008] This process for printing features with dimensions smaller than the classical resolution limit of the lithographic projection apparatus is often referred to as low-k1 lithography, according to the resolution formula CD=k1×λ / NA, where λ is the wavelength of the radiation used (currently 248 nm or 193 nm in most cases), NA is the numerical aperture of the projection optics in the lithographic projection apparatus, CD is the "critical dimension" (usually the smallest feature size printed), and k1 is an empirical resolution factor. In general, the smaller k1 is, the more difficult it becomes to reproduce on a substrate a pattern that resembles the shape and dimensions planned by the circuit designer in order to achieve specific electrical functionality and performance. In order to overcome these difficulties, it is necessary to accurately determine the control parameters of all processes in the manufacture of the device / object.

[0009] The control parameters may be independent metrology data that is determined. The metrology data may be obtained, for example, by an electron beam inspection tool or an optical measurement tool that measures properties of features on the substrate. The metrology data may include statistical properties of the measured features. A problem with relying on metrology data of features to obtain control parameters for all features is that it takes too long to obtain accurate metrology data over the entire surface of the substrate. Summary of the invention

[0010] According to a first aspect of the present invention, a method for determining a field of view setting of an inspection tool with a configurable field of view FOV is provided, the method comprising: obtaining a process margin distribution of features on at least a portion of a substrate; obtaining a threshold; identifying one or more regions on at least a portion of the substrate based on the obtained process margin distribution and the threshold; and determining a field of view setting based on the identified one or more regions.

[0011] Preferably, the method comprises determining a sampling scheme, wherein determining the sampling scheme comprises one or more of the following: jointly determining the sampling scheme and a field of view setting; determining the sampling scheme in dependence on the field of view setting; determining the field of view setting in dependence on the sampling scheme; determining the sampling scheme in dependence on the process margin distribution; determining the sampling scheme in dependence on the threshold value; and determining the sampling scheme in dependence on the one or more identified zones.

[0012] Preferably, determining the sampling scheme comprises determining one or more of: the number of fields of view used; the location of each field of view; the size of each field of view; and the density of measurements performed using images obtained from each field of view.

[0013] Preferably, determining the field of view settings comprises determining the number of fields of view, the size of each field of view and / or the position of each field of view.

[0014] Preferably, the size of each field of view is configurable at the location of each field of view; and determining the use of more than one size of the field of view.

[0015] Preferably, the field of view settings and / or sampling scheme are further determined in dependence upon an expected measurement time associated with implementation of the sampling scheme on one or more regions of said at least a portion of said substrate, such that the determined sampling scheme provides a total measurement time that is less than a sampling scheme for measuring all of said features on said at least a portion of said substrate.

[0016] Preferably, the process margin distribution includes data on variations in tolerance levels of indicators of features on at least a portion of the substrate; and the one or more identified regions on the at least a portion of the substrate are regions where the tolerance level included in the process margin distribution is less than the threshold.

[0017] Preferably, the determined field of view setting and / or sampling scheme is dependent on a determined minimum number of occurrences of at least one feature required to determine an index for said at least one feature.

[0018] Preferably, determining the field of view setting and / or sampling scheme comprises determining a number of fields of view to be increased so as to achieve a minimum number of occurrences of at least one feature.

[0019] Preferably, determining the field of view setting and / or sampling scheme comprises determining a reduction in the number of fields of view so as to avoid using substantially more fields of view for achieving a minimum number of occurrences of at least one feature.

[0020] Preferably, the method further comprises: obtaining a maximum allowable measurement time for obtaining measurement data of features on at least a portion of the substrate; wherein the field of view setting and / or sampling scheme is determined in dependence on the obtained maximum allowable measurement time, so that the time required to obtain the measurement result according to the determined field of view setting and / or sampling scheme is less than or equal to the maximum allowable measurement time.

[0021] Preferably, the process margin distribution includes a value of an indicator of the feature; and the indicator is an edge placement error EPE, a local overlap LOVL, or a local critical dimension uniformity LCDU.

[0022] Preferably, the process margin distribution is an EPE criticality map.

[0023] Preferably, the inspection tool is an electron beam based inspection tool or an optical inspection tool.

[0024] Preferably, the inspection tool consists of a metrology device.

[0025] Preferably, the process margin distribution depends on design data of one or more layers on the substrate.

[0026] Preferably, the design data of one or more layers on the substrate is dependent on design data of one or more masks used in processing the one or more layers on the substrate.

[0027] Preferably, the process margin distribution relies on a modeling step of the process on one or more layers.

[0028] Preferably, the modeling step involves the use of a physical model that predicts one or more properties of actual device features formed after exposing the mask, and one or more other process steps performed after exposing the mask.

[0029] Preferably, the process margin distribution relies on actual measurement data on one or more layers.

[0030] Preferably, said threshold is based on a process yield requirement, such as a minimum required probability of a manufacturing process producing a functional device on said at least a portion of said substrate.

[0031] Preferably, the field of view is in the range of 0.1 μm×0.1 μm to 40 μm×40 μm.

[0032] According to a second aspect of the present invention, there is provided a method for obtaining measurement data of features on at least a portion of a substrate, the method comprising: determining a sampling scheme and / or a field of view (FOV) setting for an inspection tool according to the method described in the first aspect; obtaining one or more images of corresponding one or more areas on at least a portion of the substrate using the determined sampling scheme and / or field of view setting; and determining the measurement data of features on at least a portion of the substrate based on the one or more images obtained.

[0033] According to a third aspect of the present invention, a computing system and an inspection tool are provided, wherein: the computing system is arranged to determine a sampling scheme and / or field of view FOV setting for the inspection tool according to the method described in the first aspect; and the inspection tool is arranged to obtain an image of one or more areas of at least a portion of the substrate relying on the determined sampling scheme and / or field of view setting.

[0034] According to a fourth aspect of the present invention, there is provided a metrology apparatus comprising the computing system according to the third aspect and an inspection tool.

[0035] According to a fifth aspect of the present invention, there is provided a non-transitory computer readable medium comprising instructions which, when executed, cause a computing system to perform the method of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a block diagram of various subsystems of a lithography system.

[0037] Figure 2 is corresponding to Figure 1 Block diagram of the simulation model of the subsystem in .

[0038] Figure 3A The LER is schematically depicted.

[0039] Figure 3BThe LWR is schematically depicted.

[0040] Figure 3C Schematic illustration of how random variations may affect lithography.

[0041] Figure 4A and Figure 4B A method for determining a relationship between random variations in characteristics of an aerial image or a resist image and one or more design variables is schematically illustrated.

[0042] Figure 5A and Figure 5B The results of the fit using the relationship are shown.

[0043] Figure 6 An exemplary flow chart for calculating and accounting for random variation is shown.

[0044] Figure 7 Shows hot spots identified using random variation.

[0045] Figure 8 A non-transitory computer-readable medium is shown containing randomly varying values ​​under multiple conditions and at multiple values ​​of a design variable.

[0046] Fig. 9A and Fig. 9B The intensity of the image across the edge of the pattern (space or resist) is shown respectively in the direction (x) perpendicular to the edge of the pattern.

[0047] Fig.10 The curve of the EPEILS term is schematically shown.

[0048] Fig.11 is a flow chart illustrating aspects of an exemplary method of joint optimization / co-optimization.

[0049] Fig.12 An example of another optimization method is shown.

[0050] Fig.13A , Fig. 13B and Fig.14 Exemplary flow charts showing various optimization processes.

[0051] Fig.15A A flow chart showing a method for identifying hot spots on an aerial image or a resist image based on random variations in a characteristic (eg, LER) or a function based on the random variations (eg, bl_ILS, ILS, or NILS).

[0052] Fig. 15BA flowchart showing another method for identifying hot spots on an aerial image or a resist image based on random variations (e.g., LER) of characteristics of the aerial image or resist image (e.g., edge location) or based on a function of the random variations (e.g., bl_ILS, ILS, or NILS).

[0053] Fig.16 A flow chart illustrating a method for reducing random variations (eg, LER) in one or more characteristics (eg, edge location) of an aerial image or a resist image.

[0054] Fig.17 is a block diagram of an exemplary computer system.

[0055] Fig.18 It is a schematic diagram of a lithography projection apparatus.

[0056] Fig.19 It is a schematic diagram of another type of lithography projection equipment.

[0057] Fig. 20 yes Fig.19 A more detailed view of the devices in .

[0058] Fig.21 yes Fig.19 and Fig. 20 A more detailed view of the source collector module SO of the device.

[0059] Fig. 22 Several relationships of throughput to measures of random variation are shown.

[0060] Fig.23 A flowchart schematically illustrates a method for optimizing a set of values ​​for one or more design variables and presenting various characteristics of a process, an aerial image, and / or a resist image to a user so that the user can select a set of values ​​for one or more design variables based on the user's desired characteristics.

[0061] Fig.24 An image showing features on a substrate.

[0062] Fig.25 Shown are multiple aligned and overlaid images according to an embodiment.

[0063] Fig.26 is a flow chart of a method according to an embodiment.

[0064] Figure 27a depicts the variation in EPE criticality of features distributed across an area on a substrate.

[0065] Figure 27b depicts the variation in EPE criticality of features distributed across an area on a substrate.

[0066] FIG. 27c depicts a determined field of view setting according to an embodiment.

[0067] FIG. 27d depicts a determined field of view setting according to an embodiment.

[0068] Fig.28 is a flow chart of a process for determining a field of view (FOV) setting for an inspection tool having a configurable field of view, according to an embodiment. DETAILED DESCRIPTION

[0069] Although specific reference may be made herein to the manufacture of ICs, it should be clearly understood that the description herein has many other possible applications. For example, the present application may be used to manufacture integrated optical systems, guidance and detection patterns for magnetic domain memories, liquid crystal display panels, thin film magnetic heads, etc. The skilled person will appreciate that in the context of such alternative applications, any use of the terms "reticle", "wafer" or "die" herein should be considered interchangeable with the more general terms "mask", "substrate" and "target portion", respectively.

[0070] In this document, the terms "radiation" and "beam" are used to cover all types of electromagnetic radiation, including ultraviolet radiation (e.g., having a wavelength of 365nm, 248nm, 193nm, 157nm or 126nm) and EUV (extreme ultraviolet radiation, e.g., having a wavelength in the range of 5nm to 20nm).

[0071] As used herein, the terms "optimize" and "optimize" refer to or mean adjusting the lithography projection equipment, lithography process, etc., so that the results and / or processes of the lithography have more ideal characteristics, such as higher accuracy of the projection of the design layout on the substrate, a larger process window, etc. Therefore, as used herein, the terms "optimize" and "optimize" refer to or mean identifying a process for one or more values ​​of one or more parameters, which provide an improvement in at least one related indicator (e.g., a local optimum) compared to an initial set of one or more values ​​for those one or more parameters. "Optimal" and other related terms should be interpreted accordingly. In an example, the optimization step can be iteratively applied to provide further improvements in one or more indicators.

[0072] Furthermore, the lithographic projection apparatus may be of a type having two or more tables (e.g., two or more substrate tables, a substrate table and a measurement table, two or more patterning device tables, etc.). In these "multi-stage" apparatuses, multiple tables may be used in parallel, or preparatory steps may be performed on one or more tables while one or more other tables are being used for exposure. For example, a dual-stage lithographic projection apparatus is described in US 5,969,441, which is incorporated herein by reference.

[0073] The pattern forming device referred to above includes or can form one or more design layouts. The design layout can be generated using a CAD (computer-aided design) program, and this process is often referred to as EDA (electronic design automation). Most CAD programs follow a predetermined set of design rules to generate a functional design layout / pattern forming device. These rules are set by processing and design restrictions. For example, the design rules limit the spatial tolerance between circuit devices (such as gates, capacitors, etc.) or interconnects to ensure that the circuit devices or lines do not interact with each other in an undesirable manner. One or more of the design rule restrictions can be referred to as "critical dimensions" (CD). The critical dimension of the circuit can be limited to the minimum width of a line or hole, or the minimum space between two lines or two holes. Therefore, CD determines the overall size and density of the designed circuit. Of course, one of the goals in integrated circuit manufacturing is to reproduce the original circuit design faithfully on the substrate (by pattern forming devices).

[0074] The term "mask" or "patterning device" as used herein may be broadly interpreted as referring to a general patterning device that can be used to impart a patterned cross-section to an incident radiation beam, the patterned cross-section corresponding to the pattern to be produced in a target portion of the substrate; the term "light valve" may also be used in this context. In addition to classical masks (transmissive or reflective; binary, phase-shift, hybrid, etc.), other examples of such patterning devices include:

[0075] -Programmable mirror array. An example of such a device is a matrix-addressable surface having a viscoelastic control layer and a reflective surface. The basic principle underlying this apparatus is that (for example) addressed areas of the reflective surface cause incident radiation to be reflected as diffracted radiation, while non-addressed areas cause incident radiation to be reflected as non-diffracted radiation. With the use of appropriate filters, the non-diffracted radiation can be filtered out from the reflected beam, leaving only the diffracted radiation; in this way, the beam is patterned according to the addressing pattern of the matrix-addressable surface. Suitable electronic components can be used to perform the required matrix addressing. More information on such mirror arrays can be gleaned, for example, from U.S. Patents Nos. 5,296,891 and 5,523,193, which are incorporated herein by reference.

[0076] - A programmable LCD array. An example of such a configuration is given in US Pat. No. 5,229,872, which is incorporated herein by reference.

[0077] As a brief introduction, Figure 1An exemplary lithographic projection apparatus 10A is illustrated. The main components are: a radiation source 12A, which may be a deep ultraviolet excimer laser source or other types of sources including extreme ultraviolet (EUV) sources (as discussed above, the lithographic projection apparatus itself need not have a radiation source); illumination optics, which define a partial coherence (denoted as σ) and may include optics 14A, 16Aa, and 16Ab that shape the radiation from source 12A; a patterning device 14A; and transmission optics 16Ac that projects an image of the patterning device pattern onto a substrate plane 22A. An adjustable filter or aperture 20A at a pupil plane of the projection optics may limit the range of beam angles impinging on the substrate plane 22A, wherein the maximum possible angle defines the numerical aperture NA=n sin(θmax) of the projection optics, where n is the refractive index of the medium between the last component of the projection optics and the substrate.

[0078] In the optimization process of the system, the quality factor of the system can be expressed as a cost function. The optimization process is reduced to the process of finding a set of parameters (design variables) that optimize (e.g., minimize or maximize) the cost function of the system. The cost function can have any suitable form depending on the goal of the optimization. For example, the cost function can be the weighted root mean square (RMS) of the deviation of certain characteristics (evaluation points) of the system relative to the expected values ​​(e.g., ideal values) of these characteristics; the cost function can also be the maximum value (i.e., the worst deviation) of these deviations. The term "evaluation point" in this article should be broadly interpreted to include any characteristics of the system. Due to the practicality of the implementation of the system, the design variables of the system can be limited to a limited range and / or be interdependent. In the case of a lithographic projection device, constraints are often associated with physical properties and characteristics of the hardware (such as, tunable range and / or pattern forming device manufacturability design rules), and the evaluation points can include physical points on the resist image on the substrate, as well as non-physical characteristics such as dose and focus.

[0079] In a lithographic projection apparatus, a source provides illumination (i.e., radiation) to a pattern forming device, and a projection optical device directs the illumination onto a substrate through the pattern forming device and shapes the illumination. Here, the term "projection optical device" is broadly defined as including any optical component that can change the wavefront of a radiation beam. For example, the projection optical device may include at least some of components 14A, 16Aa, 16Ab, and 16Ac. An aerial image (AI) is a distribution of radiation intensity at the substrate level. A resist layer on the substrate is exposed, and the aerial image is transferred to the resist layer as a latent image "resist image" (RI) in the resist layer. A resist image (RI) can be defined as a spatial distribution of the solubility of the resist in the resist layer. A resist model can be used to calculate a resist image based on an aerial image, an example of which can be found in U.S. Patent Application Publication No. US 2009-0157360, the entire disclosure of which is incorporated herein by reference. The resist model only concerns the properties of the resist layer (e.g. the effects of the chemical processes occurring during exposure, PEB and development). The optical properties of the lithographic projection apparatus (e.g. the properties of the source, patterning device and projection optics) dictate the aerial image. Since the patterning device used in the 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.

[0080] Figure 2 An exemplary flow chart for simulating lithography in a lithographic projection apparatus is illustrated in FIG. 3 . 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 in 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 in 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 based on the design layout model 35, the projection optics model 32, and the design layout model 35. A resist image 38 can be simulated based on 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.

[0081] More specifically, note that the source model 31 can represent the optical properties of the source, including but not limited to NA settings, standard deviation (σ) 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, distortions, 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, such as described 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, spatial image intensity slope, and / or CD, which can then be compared to the expected design. The expected 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.

[0082] From this design layout, one or more portions, referred to as "snippets," may be identified. In an example, a set of snippets are extracted that represent complex patterns in the design layout (although typically about 50 to 1000 snippets, any number of snippets may be used). These patterns or snippets represent smaller portions of the design (i.e., circuits, cells, or patterns), and more specifically, snippets typically represent smaller portions that require special attention and / or verification. In other words, a snippet 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 critical features are identified through experience (including snippets provided by a customer), through trial and error, or by performing full chip simulations. A snippet may contain one or more test patterns or gauge patterns.

[0083] An initial larger set of segments may be pre-provided by the customer based on one or more known critical 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 using some automatic (such as machine vision) or manual algorithm that identifies one or more critical feature areas.

[0084] In a lithographic projection apparatus, for example, using an EUV (extreme ultraviolet radiation, e.g., with a wavelength in the range of 5 nm to 20 nm) source or a non-EUV source, the reduced radiation intensity may result in stronger random variations, such as noticeable line width roughness and / or local CD variations in smaller two-dimensional features (such as holes). In a lithographic projection apparatus using an EUV source, the reduced radiation intensity may be attributed to lower total radiation output from the source, radiation losses from optical devices that shape the radiation from the source, transmission losses through the projection optical devices, higher photon energies resulting in fewer photons at a constant dose, etc. The random variations may be attributed to factors such as photon shot noise in the resist, secondary electrons generated by photons, variations in photon absorption, and / or photon generation. The smaller size of the features further exacerbates this random variation. Random variations in smaller features are an important factor in product yield and are justified in multiple optimization processes for inclusion in the lithographic process and / or lithographic projection apparatus.

[0085] 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 random variations. 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 times for the purpose of increasing throughput. Therefore, the methods and apparatus described herein that take into account random variations in the optimization process are not limited to EUV lithography.

[0086] Throughput may also be affected by the total amount of radiation directed to the substrate.In some lithographic projection apparatuses, a portion of the radiation from the source is sacrificed in order to achieve a desired shape of the illumination.

[0087] Figure 3ALine edge roughness (LER) is schematically depicted. Assuming that all conditions are the same in three exposures or exposure simulations of the edge 903 of the feature on the design layout, the resist images 903A, 903B, and 903C of the edge 903 may have slightly different shapes and positions. The positions 904A, 904B, and 904C of the resist images 903A, 903B, and 903C may be measured by averaging the resist images 903A, 903B, and 903C, respectively. Random variations such as line edge roughness are usually represented by parameters of the distribution of potential characteristics. In this example, assuming that the distribution is a normal distribution, the LER of edge 903 may be represented by 3σ of the spatial distribution of edge 903. 3σ may be derived from the position of edge 903 (e.g., position 904A, 904B, and 904C) in multiple exposures or simulations of edge 903. The LER represents the range into which edge 903 is likely to fall due to random effects. Therefore, LER may also be referred to as random edge placement error (SEPE). LER may be larger than changes in edge 903 position caused by non-random effects.

[0088] Figure 3B Line width roughness (LWR) is schematically depicted. Assuming that all conditions are the same in three exposures or exposure simulations of a long rectangular feature 910 having width 911 on a design layout, the resist images 910A, 910B, and 910C of rectangular feature 910 may have slightly different widths 911A, 911B, and 911C, respectively. The LWR of rectangular feature 910 may be a measure of the distribution of widths 911A, 911B, and 911C. For example, assuming that the distribution is a normal distribution, the LWR may be 3σ of the distribution of width 911. The LWR may be derived from many exposures or simulations of width 911 (e.g., widths 911A, 911B, and 911C) of rectangular feature 910. In the context of shorter features (e.g., contact holes), the width of the image of the feature is not well defined because the long edges cannot be used to average the position of the image of the feature. A similar number of LCDUs may be used to characterize random variations. The LCDU is 3σ of the distribution of the measured CDs of the images of shorter features (assuming the distribution is normal).

[0089] Figure 3C Schematic illustration of how random variations may affect lithography. Figure 3CIn the example of , the expected position of the edge of the feature in the aerial image or resist image is indicated as dotted line 982. The actual edge is indicated as curve 995, which includes both random variations (in this example, LER) and errors (e.g., caused by such as dose variations, focus variations, source shapes, pattern forming devices (e.g., mask) errors, etc.). The average position of the actual edge is indicated as solid line 981. The difference 980 between the average position (solid line 981) and the expected position (dotted line 982) is an error unrelated to random effects, which can be referred to as edge placement error (EPE). The variation of the actual edge relative to the average position is a random variation. The band 990 surrounding the average position (solid line 981) that encloses the random variation can be referred to as a random variation band, which represents the range that the actual local edge placement can reach due to random effects. The width of the random variation band can be greater than the EPE. Therefore, the total probability deviation from the expected position of the edge (dotted line 982) can be the sum of the EPE and the random variation band. If there is no random variation, the actual position of the edge in this example will be at the position indicated by the solid line 981, which does not combine with the adjacent feature 983 and thus does not produce a defect. However, when there is random variation and the random variation band is large enough (e.g., band 990), the actual edge may combine with the adjacent feature 983 (marked by the dotted circle) and thus produce a defect. Therefore, random variation needs to be evaluated, simulated, or reduced.

[0090] Figure 4A In the flowchart and Figure 4B 1301, a method for determining a relationship between a random variation of a characteristic of an aerial image or a resist image and one or more design variables is schematically depicted. In step 1301, a value 1503 of a characteristic is measured from a plurality of aerial images or resist images 1502 formed (by actual exposure or simulation) for each of a plurality of sets 1501 of values ​​of one or more design variables. In step 1302, a value 1505 of a random variation is determined for each set 1501 of values ​​of one or more design variables from a distribution 1504 of the value 1503 of the characteristic, the value 1503 of the characteristic being measured from the aerial image or resist image formed for the set 1501 of values ​​of one or more design variables. In step 1303, a relationship 1506 is determined by fitting one or more parameters of a model from the value 1504 of the random variation and the set 1501 of values ​​of one or more design variables.

[0091] In the example, the random variation is LER, and one or more design variables are blurred image ILS (bl_ILS), dose, and image intensity. The model can be:

[0092] LER = a × bl_ILSb × (dose × image intensity)c (Equation 30) The parameters a, b and c can be determined by fitting. Blurred Image ILS (bl_ILS) is the image logarithmic slope ILS with spatial blur applied. Spatial blur can represent blurring of the resist image due to diffusion of chemicals generated in the resist layer by exposure to radiation.

[0093] Figure 5A The results of the fit using the model in Equation 30 are shown. Figure 4A and Figure 4B The method in is used to determine the values ​​of LER 1400 (as an example of random variation) for more than 900 different features at constant image intensity and constant dose, including long channel 1401, long line 1402, short line 1403, short channel 1404, short line end 1405, and short channel end 1406. The parameters a and b in Equation 30 are determined by fitting the values ​​of LER with the values ​​of the design variable bl_ILS (parameter c is combined with parameter a because the dose-weighted blurred image intensity is constant). The fitting result is shown as curve 1410.

[0094] Figure 5B The results of the fit 1510 using the model in Equation 30 are shown. Figure 4A and Figure 4B The method in is used to determine the values ​​of the LCDU 1500 (as an example of random variation) in the CD in the width direction of the 20 nm×40 nm channel 1505 and in the CD in the length direction of the 20 nm×40 nm channel 1505 at various doses and various image intensities. The parameters a, b, and c in Equation 30 are determined by fitting the value of LWR with the values ​​of the design variables bl_ILS, dose, and image intensity.

[0095] Once through such Figure 4A and Figure 4B The method in the method determines a relationship between random variations in a characteristic of an aerial image or a resist image and one or more design variables, which relationship can be used to calculate a value of the random variation for the characteristic. Figure 6An exemplary flow chart for this calculation is shown. In step 1610, a set of conditions is selected (e.g., NA, σ, dose, focus, resist chemistry, one or more projection optics 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 value of the edge position of the resist image and bl_ILS along the edge. In step 1630, the value of the random variation is calculated based on the relationship between the random variation and one or more design variables. For example, in the example, the random variation is the LER of the edge. In optional step 1640, a noise vector can be defined, the frequency distribution of which roughly matches the true substrate measurement value. In optional step 1650, the noise vector is superimposed on the result (e.g., the random edge of the aerial image or the resist image).

[0096] The relationship between random variations in 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" in the aerial image or resist image, such as Figure 7 As shown in . A "hotspot" 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 higher chance of joining each other.

[0097] In an example, values ​​of random variations (and / or functions thereof) under multiple conditions and at multiple values ​​of one or more design variables may be calculated and compiled on a non-transitory computer readable medium 1800 (eg, Figure 8 The computer may query the medium 1800 and calculate randomly varying values ​​based on the contents of the medium 1800.

[0098] Determination of random variations in properties of a space / resist image can be useful in a number of ways in a lithography process. In one example, random variations can be accounted for in optical proximity correction (OPC).

[0099] As an example, OPC deals with 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 simply depend on the size and placement of the design layout on the patterning device. Note that the terms "mask", "reticle", "patterning device" can be used interchangeably in this article. 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, because: in lithography simulation / optimization, a physical patterning device is not necessarily used, but a design layout can be used to represent a physical patterning device. For smaller feature sizes and higher 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 adjacent features. These proximity effects arise from tiny 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 from diffusion and other chemical effects during, for example, post-exposure baking (PEB), resist development, and etching that typically follow lithography.

[0100] To help ensure that the projected image of the design layout is in accordance with the requirements of a given target circuit design, complex numerical models, corrections, or pre-distortions of the design layout should be used to predict and compensate for proximity effects. The paper "Full-Chip Lithography Simulation and Design Analysis-How OPC Is Changing IC Design" (C. Spence, Proc. SPIE, Vol. 5751, pp. 1-14 (2005)) provides an overview of the "model-based" optical proximity correction process. In a typical high-end design, almost every feature of the design layout has some modification to achieve higher fidelity of the projected image to the target design. These modifications can include shifts or deviations in edge position or line width, as well as the application of "auxiliary" features intended to assist the projection of other features.

[0101] With millions of features typically present in chip designs, applying model-based OPC to a target design involves good process models and considerable computational resources. However, applying OPC is typically not an "exact science" but rather an empirical iterative process that does not always compensate for all possible proximity effects. Therefore, the effects of OPC (e.g., design layout after applying OPC and / or any other RET) should be verified by design checks (i.e., enhanced full-chip simulations using calibrated numerical process models) in order to reduce or minimize the likelihood that design defects will be formed into the patterning device pattern. This is driven by the huge cost of manufacturing high-end patterning devices, which is in the millions of dollars range, and the impact of reworking or repairing the actual patterning device on turnaround time once the actual patterning device has been manufactured.

[0102] Both OPC and full-chip RET verification can be based on the numerical modeling systems and methods described in, for example, U.S. Patent Application Publication No. US 2005-0076322 and the paper by Y. Cao et al. entitled "Optimized Hardware and Software For Fast, Full Chip Simulation" (Proc. SPIE, Vol. 5754, 405 (2005)).

[0103] One RET involves adjustment of the global deviation (also called "mask deviation") of the design layout. The global deviation is the difference between the pattern in the design layout and the pattern intended to be printed on the substrate. For example, a 25 nm diameter annular pattern may be printed on the substrate with a 50 nm diameter pattern in the design layout or with a 20 nm diameter pattern in the design layout but with a higher dose, ignoring (de)magnification by the projection optics.

[0104] In addition to the optimization of the design layout or pattern forming device (e.g., OPC), the irradiation can also be optimized jointly or separately with the pattern forming device optimization to improve the overall lithography fidelity. The terms "irradiation source" and "source" can be used interchangeably in this document. Many off-axis irradiations such as annular, quadrupole and dipole have been introduced, and many off-axis irradiations have provided more degrees of freedom for OPC design, thereby improving imaging results. Off-axis irradiation is a way to distinguish fine structures (i.e., target features) included in the pattern forming device. However, when compared with traditional irradiation, off-axis irradiation generally provides a smaller radiation intensity for aerial images (AI). Therefore, it is necessary to try to optimize the irradiation source to achieve an optimal balance between finer resolution and reduced radiation intensity.

[0105] For example, a number of illumination optimization methods can be found in the paper entitled "Optimum Mask and Source Patterns to Print a Given Shape" by Rosenbluth et al., Journal of Microlithography, Microfabrication, Microsystems 1(1), pp. 13-20 (2002). The light source is divided into several regions, each of which corresponds to a certain region of the pupil spectrum. The source distribution is then assumed to be uniform in each source region, and the brightness of each region is optimized for the process window. However, the assumption that the source distribution is uniform in each source region is not always valid, and therefore, the effectiveness of this method is reduced. In another example, Granik, entitled "Source Optimization for Image Fidelity and Throughput" (Journal of Microlithography, Microfabrication, Microsystems 3 (4), pp. 509-522 (2004), reviews several existing source optimization methods and proposes an illuminator pixel-based method that converts the source optimization problem into a series of non-negative least squares optimizations. Although these methods show some success, they generally require multiple complex iterations to converge. In addition, it can be difficult to determine appropriate / optimal values ​​for some additional parameters (such as γ in Granik's method), which dictate a trade-off between optimizing the source for substrate image fidelity and the smoothness requirement of the source.

[0106] For low-k1 lithography, optimization of both the source and the patterning device helps ensure a feasible process window for projection of critical circuit patterns. Some algorithms (e.g., Socha et al., Proc. SPIE, Vol. 5853, 2005, p. 180) discretize the illumination into independent source points and the patterning device into diffraction orders in the spatial frequency domain, and separately formulate a cost function based on a process window metric such as exposure margin (the cost function is defined as a function of one or more selected design variables), which can be predicted by an optical imaging model from source point intensities and patterning device diffraction orders.

[0107] The term "design variables" as used herein include a set of parameters of a lithographic projection apparatus or a lithographic process, for example, parameters that a user of the lithographic projection apparatus can adjust, or image characteristics that a user can adjust by adjusting those parameters. It should be understood that any one or more characteristics of a lithographic projection process including one or more characteristics of an illumination, pattern forming device, projection optics, and / or resist can be represented by the design variables in the optimization. The cost function is often a nonlinear function of the design variables. Then, standard optimization techniques are used to optimize the cost function.

[0108] Relatedly, the pressure to continually reduce design rules has driven semiconductor chipmakers deeper into the era of low k1 lithography with existing 193 nm ArF lithography. The move toward lower k1 lithography places a lot of demands on RET, exposure tools, and the need for lithography-friendly design. Ultra-numerical aperture (NA) exposure tools of 1.35 ArF may be used in the future. To help ensure that circuit designs can be produced onto substrates with a workable process window, illumination patterning device optimization (referred to herein as source mask optimization or SMO) is becoming a significant RET for the 2× nm node.

[0109] U.S. Patent Application Publication No. US 2011-0230999 describes an illumination and patterning device (design layout) optimization method and system that allows the use of a cost function to simultaneously optimize the illumination and patterning device without constraints and within a feasible amount of time, and the entire contents of which are incorporated herein by reference. U.S. Patent Application Publication No. 2010 / 0315614 describes another SMO method and system involving optimizing a source by adjusting its pixels, and the entire contents of which are incorporated herein by reference.

[0110] In a lithographic projection apparatus, as an example, the cost function can be expressed as

[0111]

[0112] Among them, (z1, z2, …, z N ) are N design variables or their values. p (z1,z2,…,z N ) can be the design variables (z1, z2, …, z N ), such as the design variables (z1, z2, …, z N ) is the difference between the actual value and the expected value of the characteristic at the evaluation point. p is with f p (z1,z2,…,z N) is associated with a weight constant. A higher w may be assigned to an evaluation point or pattern that is more critical than other evaluation points or patterns. p It is also possible to assign higher w values ​​to patterns and / or evaluation points with more occurrences. p Examples of evaluation points may be any physical point or pattern on the substrate, any point on the virtual design layout, or a resist image, or an aerial image, or a combination thereof. p (z1,z2,…,z N ) can also be a function of one or more random variations such as LWR, LER and / or LCDU, which in turn is a design variable (z1, z2, ..., z N ). p (z1,z2,…,z N ) can be an explicit function of random variation, such as f p (LER)=LER 2 (z1,z2,…,z N ). p (z1,z2,…,z N ) can be an explicit function of a variable that is a function of a random variation such as LER. For example, bl_ILS can be as given by Equation 30 and The indicated LER function. p (z1,z2,…,z N ) can be variables that affect random changes such as LER.

[0113] Therefore, using f including random variations p (z1,z2,…,z N ) may result in the value of one or more design variables that reduce or minimize the random variation. The cost function may represent any one or more suitable characteristics of the lithographic projection apparatus, the lithographic process, or the substrate, such as focus, CD, image displacement, image distortion, image rotation, random variation, throughput, LCDU, or a combination thereof. LCDU is the local CD variation (e.g., three times the standard deviation of the local CD distribution). In one example, the cost function represents LCDU, throughput, and random variation (i.e., is a function of LCDU, throughput, and random variation). In one example, the cost function represents EPE, throughput, and random variation (e.g., includes f as a function of EPE, throughput, and random variation). p (z1,z2,…,z N )). In one example, the cost function includes f as a function of EPE p (z1,z2,…,z N ) and f as a function of random variations such as LER p(z1,z2,…,z N In one example, the design variables (z1, z2, ..., z N ) includes one or more items selected from the group consisting of dose, global bias of the pattern forming device, shape of the illumination, or a combination thereof. Since the resist image often defines the pattern on the substrate, the cost function may include a function representing one or more characteristics of the resist image. For example, f at this evaluation point p (z1,z2,…,z N ) can simply be the distance between a point in the resist image and the expected position of that point (i.e., the edge placement error EPE p (z1,z2,…,z N )). The design variables may include any adjustable parameters, such as adjustable parameters of the source, pattern forming device, projection optics, dose, focus, etc.

[0114] The lithographic apparatus may include components collectively referred to as "wavefront manipulators", which may be used to adjust the shape and intensity distribution and / or phase shift of the wavefront of the radiation beam. In an example, the lithographic apparatus may adjust the wavefront and intensity distribution at any position along the optical path of the lithographic projection apparatus, such as before the pattern forming device, near the pupil plane, near the image plane, and / or near the focal plane. The wavefront manipulator may be used to correct or compensate for certain distortions of the wavefront and intensity distribution and / or phase shift caused by, for example, temperature changes in the source, the pattern forming device, the lithographic projection apparatus, thermal expansion of components of the lithographic projection apparatus, and the like. Adjusting the wavefront and intensity distribution and / or phase shift may change the values ​​of the evaluation points and the cost function. These changes may be simulated according to a model or actually measured. Of course, CF(z1, z2, ..., z N ) is not limited to the form in equation 1. CF(z1, z2, ..., z N ) may be in any other suitable form.

[0115] According to an example, the cost function representing both EPE and LER may have the following form:

[0116]

[0117] This is because both EPE and LER are dimensionless in length. Therefore, they can be added directly. Alternative cost functions can be used, including one in which the LER is included in the EPE.

[0118] Equation 30 relates bl_ILS to LER. Therefore, the optimization using the cost function representing bl_ILS is similar to the optimization using the cost function representing LER. A larger bl_ILS leads to a smaller LER, and vice versa. According to an example, the cost function can represent both EPE and bl_ILS (or normalized ILS (NILS)). However, because bl_ILS does not measure length and EPE measures length, or NILS is dimensionless and EPE has the dimension of length, EPE and bl_ILS (or NILS) may not be added directly. Therefore, representing bl_ILS (or NILS) by a function representing length makes it possible to directly add the representation to EPE.

[0119] ILS is defined as bl_ILS is the spatially blurred ILS. NILS is defined as =CD×ILS. These definitions imply that either ILS, bl_ILS or NILS can be expressed and represent a function of length, and therefore allow direct addition to the EPE. Fig. 9A and Fig. 9B The intensity of the (space or resist) image across the edge of the pattern in the direction (x) perpendicular to the edge of the pattern is shown respectively. A higher slope of the intensity with respect to x means a higher ILS, bl_ILS and NILS. Therefore, Fig. 9A Compared to the example Fig. 9B The example has higher ILS, bl_ILS, and NILS. The edge position Xe shifts with the intensity sufficient to expose the resist I. When the duration of exposure is fixed, the intensity sufficient to expose the resist I varies with the dose. Therefore, the shift amount of the edge position Xe caused by a given change in dose (e.g., ±δ relative to the nominal dose, ±δ can be a parameter selected by the user) (hereinafter, "EPEILS", e.g., 2911 and 2912) is determined by ILS, bl_ILS, or NILS. Because Fig. 9A The example is therefore Fig. 9B The example has higher ILS, bl_ILS and NILS, so Fig. 9A In the example, EPEILS is less than Fig. 9B Thus, EPEILS is an example of a function that can represent ILS, bl_ILS or NILS and represents the length, where it is allowed to add this EPEILS directly to the EPE in the cost function. EPEILS can be written as:

[0120]

[0121] Among them, ILS(x e (0)) are the design variables (z1, z2, …, z NAccording to an example, the cost function representing both EPE and ILS, bl_ILS or NILS may have the following form:

[0122]

[0123] Among them, EPE p (z1,z2,…,z N )| δ＝0 is the EPE value at the nominal dose, p is the pth evaluation point, and S p Yes EPE ILS The weight of the term. So, for example, by minimizing this cost function, the optimization of ILS(x e (0)) is maximized and hence LER is minimized.

[0124] According to the example, when the EPE term increases, EPE ILS item The weight of can be relative to the EPE term (e.g., ) is reduced, making EPE IL s item Not leading EPE items If EPE ILS term dominates, the EPE term will not be fully reduced due to optimization. For example, when |EPE p |Above the user selected offset, at |EPE p |>OF (the optimization thus ignores the EPEILS term and only reduces the EPE term) p = 0, and in |EPE p |≤OF p ≠0, where OF is the offset. For example,

[0125]

[0126] A higher weight of the EPE term will make the optimization favor the reduction of the EPE term in the optimization using the cost function.

[0127] Fig.10 Schematically shown as EPE p The cost function curve of the function, where the weight

[0128]

[0129] like Fig.10 As shown, due to the weight w p has a larger value, so the EPE term is in |EPE p |>OF leads to a larger ratio of the cost function.

[0130] The design variables can have constraints, which can be expressed as (z1, z2, ..., z N )∈Z, where Z is a set of possible values ​​of the design variables. A possible constraint on the design variables can be imposed by the desired throughput of the lithographic projection device. The lower limit of the desired throughput leads to an upper limit on the dose, and therefore has an impact on random variation (e.g., a lower limit is imposed on the random variation). Shorter exposure times and / or lower doses generally lead to higher throughput, but lead to larger random variations. Because random variation is a function of the design variables, considerations to minimize substrate throughput and random variation can constrain the possible values ​​of the design variables. In the absence of such constraints imposed by the desired throughput, optimization may lead to a set of unrealistic values ​​of the design variables. For example, if the dose is a design variable, then in the absence of this constraint, optimization may lead to dose values ​​that make the throughput impossible in terms of saving. However, the usefulness of the constraint should not be interpreted as necessity. For example, the throughput may be affected by the pupil filling ratio. For some irradiation designs, a lower pupil filling ratio may discard radiation, resulting in a lower throughput. The throughput may also be affected by the chemical reaction of the resist. Slower resists (e.g., resists that require proper exposure to higher amounts of radiation) lead to lower throughput.

[0131] Therefore, the optimization process is under the constraints (z1, z2, ..., z N )∈Z to find a set of values ​​of one or more design variables that optimize the cost function, for example to find:

[0132]

[0133] Fig.11A general method for optimization according to an example is described in . This method includes a step 302 of defining a multivariate cost function of a plurality of design variables. The design variables may include any suitable combination of design variables selected from one or more characteristics (300A) representing the illumination (e.g., pupil fill ratio, i.e., the percentage of the radiation of the illumination that passes through the pupil or aperture), one or more characteristics (300B) of the projection optics, and / or one or more characteristics (300C) of the design layout. For example, the design variables may include design variables representing one or more characteristics (300A) of the illumination and one or more characteristics (300C) of the design layout (e.g., global deviation), but not representing one or more characteristics (300B) of the projection optics, which results in SMO. Alternatively, the design variables may include design variables representing one or more characteristics (300A) of the illumination (optionally polarization), one or more characteristics (300B) of the projection optics, and one or more characteristics (300C) of the design layout, which results in illumination-patterning device (e.g., mask)-projection system (e.g., lens) optimization (SMLO). In step 304, the design variables are adjusted synchronously so that the cost function moves toward convergence. In step 306, it is determined whether a predefined termination condition is met. The predetermined termination condition may include various possibilities, for example, one or more selected from the following: the cost function can be minimized or maximized as required by the numerical technique used; the value of the cost function has equaled a threshold or exceeded a threshold, the value of the cost function has reached a preset error limit, and / or a preset number of iterations has been reached. If the condition is met in step 306, the method ends. If one or more conditions are not met in step 306, steps 304 and 306 are iteratively repeated until the desired result is obtained. Because there may be physical constraints caused by factors such as pupil filling factor, resist chemical reaction, throughput, etc., the optimization does not necessarily result in a single set of values ​​for one or more design variables. The optimization may provide multiple sets of values ​​for one or more design variables and associated performance characteristics (e.g., throughput), and allow a user of the lithographic apparatus to select one or more sets. Fig. 22Several relationships of throughput (in substrates per hour) in the horizontal axis and a measure of random variation (e.g., the average of the worst corner CDU and LER) in the vertical axis are shown for resist chemistry (resist chemistry can be represented by the dose required to expose the resist), pupil filling ratio (also referred to as "pupil filling factor"), illumination efficiency (e.g., the ratio of mirrors that direct radiation to the patterning device to the total mirrors available in the illuminator), and mask deviation. Trace 1811 shows these relationships using a pupil filling factor of 100% and a fast resist. Trace 1812 shows these relationships using a pupil filling factor of 100% and a slow resist. Trace 1821 shows these relationships using a pupil filling factor of 60% and a fast resist. Trace 1822 shows these relationships using a pupil filling factor of 60% and a slow resist. Trace 1831 shows these relationships using a pupil filling factor of 29% and a fast resist. Trace 1832 shows these relationships using a pupil fill factor of 29% and slow resist. The optimization can present all of these possibilities to the user, so the user can select pupil factors, resist chemistry based on his specific requirements for random variation and / or throughput. The optimization can also include calculating the relationship between throughput and pupil fill factor, resist chemistry, and mask bias. The optimization can also include calculating the relationship between a measure of random variation and pupil fill factor, resist chemistry, and mask bias.

[0134] According to the example, Fig.23 As schematically illustrated in the flowchart of , optimization can be performed (step 1910) at each of a set of values ​​of one or more design variables (e.g., an array, matrix, or a list of values ​​of global deviations and mask anchor deviations). In an example, the optimized cost function is a function of one or more metrics (e.g., LCDU) that vary randomly. Then, in step 1920, various characteristics of the process, aerial image and / or resist image (e.g., critical dimension uniformity (CDU), depth of focus (DOF), exposure margin (EL), mask error enhancement factor (MEEF), LCDU, throughput, etc.) can be presented (e.g., in a 3D curve) to the user who optimizes for each set of values ​​of one or more design variables. In an optional step 1930, the user selects a set of values ​​of one or more design variables based on one or more of his desired characteristics. The process can be implemented through an XML document or any scripting language.

[0135] The illumination, patterning device, and projection optics may be optimized alternately (referred to as alternating optimization) or simultaneously (referred to as simultaneous optimization). The terms "simultaneous," "synchronously," "jointly," and "jointly" as used herein mean that one or more design variables representing one or more characteristics of the illumination, patterning device, projection optics, and / or any other design variable are allowed to change simultaneously. The terms "alternating" and "alternatingly" as used herein mean that not all design variables are allowed to change simultaneously.

[0136] exist Fig.11 In the process, the optimization of all design variables is performed simultaneously. This process can be called a simultaneous process or a joint optimization process. Alternatively, the optimization of all design variables is performed alternately, such as Fig.12 In this process, in each step, some design variables are fixed and other design variables are optimized to optimize the cost function; then, in the next step, a different set of variables is fixed and other sets of variables are optimized to minimize or maximize the cost function. These steps are performed alternately until convergence or a termination condition is met. Fig.12 As shown in the non-limiting exemplary flow chart of , first, a design layout is obtained (step 402), and then, in step 404, a step of illumination optimization is performed, wherein one or more design variables of the illumination are optimized (SO) to minimize or maximize a cost function while fixing other design variables. Then, in the next step 406, a pattern forming device (e.g., mask) optimization (MO) is performed, wherein the design variables of the pattern forming device are optimized to minimize or maximize the cost function while fixing other design variables. These two steps are performed alternately until a certain termination condition is met in step 408. One or more various termination conditions may be used, such as the value of the cost function becomes equal to a threshold, the value of the cost function exceeds a threshold, the value of the cost function reaches within a preset error limit, a preset number of iterations is reached, etc. Note that SO-MO alternating optimization is used as an example for this alternative process. This alternative process can take many different forms, such as: SO-LO-MO alternating optimization, wherein SO, LO (projection optics optimization) and MO are performed alternately and iteratively; or the first SMO may be performed once, and then LO and MO are performed alternately and repeatedly, etc. Another alternative is SO-PO-MO (illumination optimization, polarization optimization and patterning device optimization). Finally, an output of the optimization results is obtained in step 410 and the process stops.

[0137] As discussed above, the pattern selection algorithm can be integrated with synchronous or alternating optimization. For example, when alternating optimization is employed, full chip SO can be performed first, one or more "hot spots" and / or "warm spots" can be identified, and then MO can be performed. In view of the present disclosure, numerous permutations and combinations of sub-optimizations are possible in order to achieve the desired optimization results.

[0138] Fig.13A An exemplary optimization method is shown in which a cost function is minimized or maximized. In step S502, initial values ​​of one or more design variables are obtained, including one or more associated tuning ranges (if any). In step S504, a multivariate cost function is set. In step S506, the cost function is expanded in a sufficiently small neighborhood around the starting value of one or more design variables for the first iteration step (i=0). In step S508, standard multivariate optimization techniques are applied to the cost function. Note that the optimization problem can apply constraints, such as one or more tuning ranges, during the optimization process in S508 or later in the optimization process. Step S520 indicates that each iteration is performed for one or more given test patterns (also referred to as "gauges") for the identified evaluation points that have been selected for optimizing the lithography process. In step S510, the lithography response is predicted. In step S512, the results of step S510 are compared with the expected or ideal lithography response value obtained in step S522. If the termination condition is met in step S514, that is, the optimization produces a lithographic response value that is close enough to the expected value, the final value of the design variable is output in step S518. The output step may also include outputting one or more other functions using the final value of the design variable, such as outputting a wavefront aberration adjusted map at the pupil plane (or other plane), an optimized illumination map, and / or an optimized design layout. If the termination condition is not met, in step S516, the value of one or more design variables is updated with the result of the i-th iteration, and the process returns to step S506. The following is a detailed description of the output step. Fig.13A technology.

[0139] In the exemplary optimization process, in addition to f p (z1,z2,…,z N ) is sufficiently smooth (e.g., there is a first-order derivative No assumptions or approximations are made about the design variables (z1, z2, …, z N ) and f p (z1,z2,…,z N ), f p (z1,z2,…,z N) is usually smooth enough to be effective in lithography projection equipment. Algorithms such as Gauss-Newton algorithm, Levenberg-Marquardt algorithm, Broyden-Fletcher-Goldfarb-Shanno algorithm, gradient descent algorithm, simulated annealing algorithm, intrinsic point algorithm and genetic algorithm can be applied to find

[0140] Here, the Gauss-Newton algorithm is used as an example. The Gauss-Newton algorithm is an iterative method applicable to general nonlinear multivariable optimization problems. N ) takes the value (z 1i , z 2i , …, z Ni ) in the i-th iteration, the Gauss-Newton algorithm linearizes (z 1i , z 2i , …, z Ni ) near f p (z1,z2,…,z N ), then calculate the given CF(z1, z2, …, z N ) of the minimum value (z 1i , z 2i , …, z Ni ) nearby (z 1(i+1) , z 2(i+1) , …, z N(i+1) ). The design variables (z1, z2, …, z N ) takes the value (z 1(i+1) , z 2(i+1) , …, z N(i+1) This iteration continues until convergence (i.e., CF(z1, z2, …, z N ) no longer decreases) or reaches a preset number of iterations.

[0141] Specifically, in the i-th iteration, in (z 1i , z 2i , …, z Ni )nearby,

[0142]

[0143] According to the approximation of Equation 3, the cost function becomes:

[0144]

[0145] It is the design variable (z1, z2, ..., z N ). In addition to the design variables (z1, z2, …, z N ), all other terms are constants.

[0146] If the design variables (z1, z2, ..., z N ) is not under any constraints, then we can solve N linear equations (where n = 1, 2, ..., N) is solved and (z 1(i+1) , z 2(i+1) , …, z N(i+1) ).

[0147] If the design variables (z1, z2, ..., z N ) is in the form of J inequalities (e.g., (z1, z2, …, z N ) tuning range (where j = 1, 2, ..., J), and K equations (e.g., dependencies between design variables) (where k = 1, 2, ..., K), the optimization process becomes a classic quadratic programming problem, where A nj , B j , C nk , D k is a constant. Additional constraints can be imposed for each iteration. For example, a “damping factor” Δ D To limit (z 1(i+1) , z 2(i+1) , …, z N(i+1) ) and (z 1i , z 2i , …, z Ni ) so that the approximation of Equation 3 holds. These constraints can be expressed as z ni -Δ D ≤z n ≤z ni +Δ D The method described in Digital Optimization (2nd ed.) by Jorge Nocedal and Stephen J. Wright (Berlin New York: Vandenberghe. Cambridge University Press) can be used to derive (z 1(i+1) , z 2(i+1) , …, z N(i+1) ).

[0148] Instead of minimizing f p (z1,z2,…,z N ), the optimization process can minimize the magnitude of the maximum deviation (worst defect) in the evaluation point to its expected value. In this method, the cost function can be alternatively expressed as:

[0149]

[0150] Among them, CL p is used for p (z1,z2,…,z N ). This cost function represents the worst defect in the evaluation point. Optimization using this cost function minimizes the magnitude of the worst defect. An iterative greedy algorithm can be used for this optimization.

[0151] The cost function of Equation 5 can be approximated as:

[0152]

[0153] Wherein, q is a positive even integer, such as at least 4, or at least 10. Equation 6 mimics the behavior of Equation 5 while allowing the optimization to be performed analytically and accelerated by using methods such as the deepest descent method, the conjugate gradient method, and the like.

[0154] Minimizing the worst defect size can also be related to f p (z1,z2,…,z N ). Specifically, f is approximated as in Equation 3 p (z1,z2,…,z N ). Then, the constraint on the worst defect size is written as inequality E Lp ≤f p (z1,z2,…,z N )≤E Up , where E Lp and E Up is specified for f p (z1,z2,…,z N ) are two constants for the minimum and maximum allowable deviations. Inserting these constraints into equation 3 transforms them into the following equations, (where p = 1, ... P),

[0155]

[0156] and

[0157]

[0158] Because Equation 3 is usually only valid for (z1, z2, …, z N ), so if the desired constraint E cannot be achieved in this vicinity Lp ≤f p (z1,z2,…,z N )≤E Up (which can be determined by any conflicts between the inequalities), then the constant E can be relaxed Lp and E UpUntil the constraints can be achieved. This optimization process minimizes (z1, z2, ..., z N ). Each step then progressively reduces the worst defect size, and each step is performed iteratively until some termination condition is met. This will result in an optimal reduction in the worst defect size.

[0159] Another way to minimize the worst defect is to adjust the weight w in each iteration p For example, after the i-th iteration, if the r-th evaluation point is the worst defect, then w may be increased in the (i+1)-th iteration. r , so that the reduction of the defect size of the evaluation point is given higher priority.

[0160] In addition, the cost functions in Equation 4 and Equation 5 can be modified by introducing Lagrange multipliers to achieve a trade-off between the optimization of the RMS of the defect size and the optimization of the worst defect size, that is,

[0161]

[0162] Wherein, λ is a preset constant that specifies the tradeoff between the optimization of the RMS of the defect size and the optimization of the worst defect size. Specifically, if λ=0, this equation becomes Equation 4, and only the RMS of the defect size is minimized; while if λ=1, this equation becomes Equation 5, and only the worst defect size is minimized; if 0<λ<1, both cases are considered in the optimization. A variety of methods can be used to solve this optimization. For example, similar to the method described previously, the weighting in each iteration can be adjusted. Alternatively, similar to minimizing the worst defect size according to the inequality, the inequalities of Equations 6' and 6" can be regarded as constraints on the design variables during the solution of the quadratic programming problem. Then, the bounds on the worst defect size can be incrementally relaxed, or the weights for the worst defect size can be incrementally increased, the cost function values ​​for each achievable worst defect size are calculated, and the design variable values ​​that minimize the total cost function are selected as the initial point for the next step. By doing this iteratively, minimization of this new cost function can be achieved.

[0163] Optimizing the lithography projection equipment can expand the process window. A larger process window provides more flexibility in process design and chip design. The process window can be defined as a set of focus and dose values ​​for which the resist image is within a certain limit of the design target of the resist image. Note that all methods discussed herein can also be extended to a generalized process window definition that can be established by different or additional base parameters other than exposure dose and defocus. These base parameters may include, but are not limited to, optical settings such as NA, standard deviation, aberration, polarization, or optical constants of the resist layer. For example, as described above, if the process window (PW) also includes different mask deviations, the optimization includes minimization of MEEF, which is defined as the ratio between the substrate EPE and the induced mask edge deviation. The process window defined by the focus and dose values ​​is used only as an example in the present invention. The following describes a method for maximizing the process window according to an example.

[0164] In the first step, starting from known conditions (f0, ε0) in the process window (where f0 is the nominal focus and ε0 is the nominal dose), minimize one of the cost functions below the neighborhood of (f0±Δf, ε0±ε):

[0165]

[0166] or

[0167]

[0168]

[0169] or

[0170]

[0171] If the nominal focus f0 and the nominal dose ε0 are allowed to shift, they can be related to the design variables (z1, z2, …, z N ) are jointly optimized. In the next step, if (z1, z2, …, z N , f, ε) such that the cost function is within preset limits, then accept (f0±Δf, ε0±ε) as part of the process window.

[0172] If the focus and dose are not allowed to shift, the design variables (z1, z2, ..., z N In an alternative example, if (z1, z2, ..., z N ) such that the cost function is within preset limits, then (f0±Δf, ε0±ε) is accepted as part of the process window.

[0173] The methods previously described in the present disclosure can be used to minimize the corresponding cost functions of Eq. 7, 7', or 7". If the design variables represent one or more characteristics of the projection optics, such as Zernike coefficients, then minimizing the cost function of Eq. 7, 7', or 7" results in maximizing the process window based on the projection optics optimization (i.e., LO). If the design variables represent one or more characteristics of the illumination and patterning devices in addition to those of the projection optics, then minimizing the cost function of Eq. 7, 7', or 7" results in maximizing the process window based on the SMLO, such as Fig.11 If the design variables represent one or more characteristics of the source and the pattern forming device, then minimizing the cost function of Equation 7, 7' or 7" results in maximizing the process window based on SMO. The cost function of Equation 7, 7' or 7" may also include at least one f such as described herein. p (z1,z2,…,z N ), that is, at least one f p (z1,z2,…,z N ) is a function of one or more random variations such as LWR, local CD variations of 2D features, and / or throughput.

[0174] Fig.14 A specific example of how a synchronous SMLO process can use the Gauss-Newton algorithm for optimization is shown. In step S702, starting values ​​for one or more design variables are identified. A tuning range for each variable may also be identified. In step S704, a cost function is defined using one or more design variables. In step S706, the cost function is expanded around starting values ​​for all evaluation points in the design layout. In optional step S710, a full chip simulation is performed to cover all critical patterns in the full chip design layout. In step S714, the desired lithography response metrics (such as CD or EPE) are obtained, and in step S712 the desired lithography response metrics are compared to predicted values ​​of those quantities. In step S716, the process window is determined. Steps S718, S720, and S722 are similar to those described with reference to FIG. Fig.13A The corresponding steps S514, S516 and S518 are described. As mentioned above, the final output can be, for example, a wavefront aberration map in the pupil plane, which is optimized to produce the desired imaging performance. The final output can be, for example, an optimized illumination map and / or an optimized design layout.

[0175] Fig. 13B An exemplary method for optimizing a cost function is shown, where the design variables (z1, z2, ..., z N ) includes design variables that can assume only discrete values.

[0176] The method begins (step 802) by defining pixel groups for illumination and patterning device pattern blocks for the patterning device. In general, pixel groups or patterning device pattern blocks may also be referred to as partitions of a lithography process component. In one exemplary method, illumination is divided into 117 pixel groups and 94 patterning device pattern blocks are defined for the patterning device (substantially as described above), resulting in a total of 211 partitions.

[0177] In step 804, a lithography model is selected as a basis for lithography simulation. The lithography simulation produces a result or response in the calculation of one or more lithography indicators. A specific lithography indicator is defined as the performance indicator to be optimized (step 806). In step 808, initial (pre-optimization) conditions for irradiation and patterning devices are set. The initial conditions include the initial states of the patterning device pattern blocks of the pixel groups and patterning devices for irradiation, so that the initial irradiation shape and the initial patterning device pattern can be referenced. The initial conditions can also include mask deviation, NA, and / or focus slope range. Although steps 802, 804, 806 and 808 are depicted as sequential steps, it will be appreciated that in other examples, these steps can be performed in other sequences.

[0178] In step 810, the pixel groups and patterning device blocks are sorted. The pixel groups and patterning device blocks may be interleaved in the sorting. Various sorting methods may be used, including: sequentially (e.g., from pixel group 1 to pixel group 117 and from patterning device block 1 to patterning device block 94), randomly, based on the physical location of the pixel groups and patterning device blocks (e.g., sorting pixel groups closer to the center of the irradiation higher), and / or based on how changes to the pixel groups or patterning device blocks affect performance indicators.

[0179] Once the pixel groups and patterning device pattern blocks are sorted, the illumination and patterning device are adjusted to improve the performance metric (step 812). In step 812, each of the pixel groups and patterning device pattern blocks is analyzed in the sorted order to determine whether a change to the pixel group or patterning device pattern block will result in an improvement in the performance metric. If it is determined that the performance metric will be improved, the pixel group or patterning device pattern block is changed accordingly, and the resulting improved performance metric and the modified illumination shape or modified patterning device pattern form a baseline for comparison for subsequent analysis of lower sorted pixel groups and patterning device pattern blocks. In other words, the changes that improve the performance metric are maintained. As changes to the states of the pixel groups and patterning device pattern blocks are made and maintained, the initial illumination shape and the initial patterning device pattern are changed accordingly, so that the modified illumination shape and the modified patterning device pattern result from the optimization process in step 812.

[0180] In other approaches, patterning device polygon shape adjustments and pairwise polling of pixel groups and / or patterning device pattern blocks are also performed within the optimization process of 812 .

[0181] In an example, the staggered simultaneous optimization process can include changing the pixel groups that are illuminated, and if an improvement in the performance metric is found, then stepping up and / or down the dose or intensity to look for further improvement. In another example, the stepping up and / or down of the dose or intensity can be replaced by a deviation change in the patterning device pattern to look for further improvement in the simultaneous optimization process.

[0182] In step 814, a determination is made as to whether the performance metric has converged. For example, the performance metric may be considered to have converged if little or no improvement in the performance metric has been demonstrated in the last few iterations of steps 810 and 812. If the performance metric has not converged, steps 810 and 812 are repeated in the next iteration, wherein the modified illumination shape and the modified patterning device according to the current iteration are used as the initial illumination shape and the initial patterning device for the next iteration (step 816).

[0183] The optimization method described above may be used to increase the throughput of a lithographic projection apparatus. For example, the cost function may include f as a function of exposure time. p (z1,z2,…,z N ). In an example, the optimization of this cost function is constrained or influenced by a measure of random variation or other indicator. Specifically, a computer-implemented method for increasing the throughput of a lithography process may include optimizing a cost function as a function of one or more random variations of the lithography process and an exposure time of the substrate so as to reduce or minimize the exposure time.

[0184] In one example, the cost function includes at least one f that is a function of one or more randomly varying p (z1,z2,…,z N ). The one or more random variations may include LWR and / or local CD variations of 2D features. In one example, the one or more random variations include one or more random variations of one or more characteristics of an aerial image or a resist image. For example, such random variations may include line edge roughness (LER), line width roughness (LWR), and / or local critical dimension uniformity (LCDU). Including one or more random variations in the cost function allows finding values ​​of one or more design variables that minimize the one or more random variations, thereby reducing the risk of defects due to random variations.

[0185] Fig.15A A flow chart of a method for identifying hot spots on an aerial image or a resist image based on a random variation of a characteristic (e.g., LER) or based on a variable (e.g., bl_ILS, ILS, or NILS) that is a function of or affects a random variation according to an example is shown. In optional step 2510, a variable that is a function of a random variation (e.g., LER) of a characteristic (e.g., edge position) for an aerial image or a resist image or a variable (e.g., bl_ILS, ILS, or NILS) that affects the random variation is obtained. In step 2520, the value of the random variation (e.g., LER) of the characteristic is obtained (e.g., according to the value of the variable). In step 2530, a range of the characteristic is obtained. The range may be due to any suitable restriction. For example, when the random variation is LER, the range may be specified by the geometry of the pattern of the design layout. For example, the maximum value of LER may not exceed the width of the gap from the edge to its adjacent edge. In step 2540, the value of the random variation is compared with the range. If the random variation exceeds the range, the feature is identified as a hotspot in step 2550. The feature identified as a hotspot may be further processed, such as an optimization to reduce the random variation.

[0186] Fig. 15BA flow chart of a method for identifying hot spots of an aerial image or resist image based on random variations (e.g., LER) of a characteristic (e.g., edge position) of the aerial image or resist image or based on a variable (e.g., bl_ILS, ILS, or NILS) that is a function of or affects the random variations is shown according to an example. In step 2610, a range of the characteristic is obtained. In step 2620, a range of the random variation (e.g., LER) or a range of the variable (e.g., bl_ILS, ILS, or NILS) is obtained based on the range of the characteristic. In step 2630, a value of the random variation or a value of the variable is obtained. In step 2640, the value of the random variation or the value of the variable is compared with its respective range. If the value of the random variation or the value of the variable exceeds its respective range, the characteristic is identified as a hot spot in step 2650. Further processing may be performed on the characteristic identified as a hot spot, such as optimization for reducing random variation.

[0187] Fig.16 A flow chart showing a method for reducing random variations (eg, LER) of one or more characteristics (eg, edge location) of an aerial image or a resist image according to an example. In step 2710, for example, using Fig.15A or Fig. 15B A method of obtaining one or more characteristics by identifying one or more characteristics as hot spots from a portion of a design layout. In step 2720, the random variation of the one or more characteristics is reduced, for example, by using a cost function that at least represents the random variation or a variable that is a function of the random variation or a variable that affects the random variation (e.g., bl_ILS, ILS, or NILS). In step 2730, a hot spot from a portion of the design layout is re-identified. In step 2740, it is determined whether a hot spot is identified. If a hot spot is identified, step 2750 is continued; if no hot spot is identified, the method stops. In step 2750, one or more parameters of the optimization are changed (e.g., δ and / or a user-selected offset), and the method is repeated to step 2720 and the optimization is performed using the changed one or more parameters. In an alternative embodiment, the one or more parameters can be part of the design layout, and steps 2740 and 2750 can be eliminated.

[0188] Fig.17A block diagram of a computer system 100 is provided to illustrate a method and process for implementing the optimization disclosed herein. The computer system 100 includes a bus 102 or other communication mechanism for communicating information and a processor 104 (or multiple processors 104 and 105) coupled to the bus 102 for processing information. The computer system 100 also includes a main memory 106, such as a random access memory (RAM) or other dynamic memory, coupled to the bus 102 for storing information and instructions to be executed by the processor 104. The main memory 106 may also be used to store temporary variables or other intermediate information during the execution of instructions to be executed by the processor 104. The computer system 100 also includes a read-only memory (ROM) 108 or other static storage device coupled to the bus 102 for storing static information and instructions for the processor 104. A memory 110, such as a magnetic disk or optical disk, is provided and coupled to the bus 102 for storing information and instructions.

[0189] The computer system 100 can be connected to a display 112 for displaying information to a computer user, such as a cathode ray tube (CRT) or a flat panel display or a touch panel display, via a bus 102. An input device 114 including letter keys and other keys is connected to the bus 102 for communicating information and command selections to the processor 104. Another type of user input device is a cursor control 116, such as a mouse, trackball, or cursor direction keys, for communicating direction information and command selections to the processor 104 and for controlling cursor movement on the display 112. This input device typically has two degrees of freedom on two axes (a first axis (e.g., x) and a second axis (e.g., y)), which allow the device to specify a position in a plane. A touch panel (screen) display can also be used as an input device.

[0190] According to one example, portions of the optimization process may be performed by computer system 100 in response to processor 104 executing one or more sequences of one or more instructions contained in main memory 106. Such instructions may be read into main memory 106 from another computer-readable medium, such as memory 110. Execution of the sequences of instructions contained in main memory 106 causes processor 104 to perform the process steps described herein. One or more processors in a multi-processing configuration may also be used to execute the sequences of instructions contained in main memory 106. In alternative examples, hard-wired circuitry may be used instead of or in combination with software instructions. Therefore, the description herein is not limited to any specific combination of hardware circuitry and software.

[0191] The term "computer-readable medium" as used herein refers to any medium that participates in providing instructions to the processor 104 for execution. This medium can take many forms, including but not limited to non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks, such as memory 110. Volatile media include dynamic memory, such as main memory 106. Transmission media include coaxial cables, copper wires, and optical fibers, including lines containing bus 102. Transmission media can also take the form of sound waves or light waves, such as sound waves or light waves generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media include, for example, floppy disks, floppy disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, DVDs, any other optical media, punch cards, paper tapes, any other physical media with hole patterns, RAM, PROMs and EPROMs, FLASH-EPROMs, any other memory chips or cartridges, carrier waves as described below, or any other media that can be read by a computer.

[0192] Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to processor 104 for execution. For example, the instructions may be initially carried on a disk of a remote computer. The remote computer may load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to computer system 100 may receive data on the telephone line and convert the data into an infrared signal using an infrared transmitter. An infrared detector connected to bus 102 may receive the data carried in the infrared signal and place the data on bus 102. Bus 102 carries the data to main memory 106, from which processor 104 retrieves and executes instructions. The instructions received by main memory 106 may optionally be stored on storage device 110 before or after being executed by processor 104.

[0193] The computer system 100 may also include a communication interface 118 coupled to the bus 102. The communication interface 118 provides a bidirectional data communication connection to a network link 120, which is connected to a local area network 122. For example, the communication interface 118 may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line. As another example, the communication interface 118 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. A wireless link may also be implemented. In any such implementation, the communication interface 118 sends and receives electrical, electromagnetic, or optical signals that carry digital data streams representing various types of information.

[0194] The network link 120 typically provides data communication to other data devices through one or more networks. For example, the network link 120 may provide a connection through a local area network 122 to a host computer 124 or to data equipment operated by an Internet Service Provider (ISP) 126. The ISP 126, in turn, provides data communication services through a global packet data communication network, now commonly referred to as the "Internet" 128. Both the local area network 122 and the Internet 128 use electrical, electromagnetic, or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 120 and through the communication interface 118 are exemplary forms of carrier waves that transport the information, carrying the digital data to and from the computer system 100.

[0195] The computer system 100 may send messages and receive data including program code through one or more networks, network link 120, and communication interface 118. In the example of the Internet, server 130 may transmit the requested code for an application through Internet 128, ISP 126, local area network 122, and communication interface 118. One such downloaded application may provide, for example, irradiation optimization. The received code may be executed by processor 104 as it is received, and / or stored in memory 110 or other non-volatile storage for later execution. In this way, computer system 100 may obtain application code in the form of a carrier wave.

[0196] Fig.18 An exemplary lithographic projection apparatus is schematically depicted in which the illumination can be optimized using the method described herein. The apparatus comprises:

[0197] - an illumination system IL for conditioning the radiation beam B. In this particular case, the illumination system further comprises a radiation source SO;

[0198] a first platform (e.g. patterning device table) MT provided with a patterning device holder for holding a patterning device MA (e.g. a reticle) and connected to a first positioner for accurately positioning the patterning device with respect to the item PS;

[0199] a second platform (substrate table) WT provided with a substrate holder for holding a substrate W (e.g. a silicon wafer coated with resist) and connected to a second positioner for accurately positioning the substrate relative to the item PS;

[0200] - A projection system ("lens") PS (eg, a refractive, reflective or catadioptric optical system) for imaging the illuminated portion of the patterning device MA onto a target portion C of the substrate W (eg, comprising one or more dies).

[0201] As depicted herein, the device is of the transmissive type (i.e., has a transmissive patterning device). However, in general, it may also be of the reflective type, for example (has a reflective patterning device). The device may use a different kind of patterning device than a classical mask; examples include a programmable mirror array or an LCD matrix.

[0202] A source SO (e.g. a mercury lamp or an excimer laser, an LPP (laser produced plasma) EUV source) generates a radiation beam. This beam is fed into an illumination system (illuminator) IL, for example directly or after having passed through a conditioning device such as a beam expander Ex. The illuminator IL may comprise an adjustment device AD ​​for setting the outer radial extent and / or the inner radial extent (commonly referred to as σ outer and σ inner, respectively) of the intensity distribution in the beam. In addition, the illuminator IL will typically comprise various other components, such as an integrator IN and a condenser CO. In this way, the beam B incident on the patterning device MA has a desired uniformity and intensity distribution in its cross section.

[0203] about Fig.18 , note that the source SO can be inside the housing of the lithographic projection apparatus (this is often the case when the source SO is, for example, a mercury lamp), but the source SO can also be remote from the lithographic projection apparatus, into which the radiation beam generated by the source SO is guided (for example, with the aid of suitable guiding mirrors); this latter situation is often the case when the source SO is an excimer laser (for example, based on KrF, ArF or F2 laser action).

[0204] The beam PB then intercepts the patterning device MA which is held on the patterning device table MT. Having passed the patterning device MA, the beam B passes through a lens PL which focuses the beam B onto a target portion C of the substrate W. With the aid of the second positioning device (and the interferometry device IF), the substrate table WT can be accurately moved, for example, so that a different target portion C is positioned in the path of the beam PB. Similarly, the first positioning device can be used to accurately position the patterning device MA relative to the path of the beam B, for example after mechanical acquisition of the patterning device MA from a patterning device library or during scanning. Typically, the patterning device MA will be accurately positioned with the aid of the second positioning device (and the interferometry device IF) without being moved in the substrate table. Fig.18 Movement of the target platform MT, WT is achieved by a long-stroke module (coarse positioning) and a short-stroke module (fine positioning) explicitly depicted in FIG. 2. However, in the case of a stepper (as opposed to a step-and-scan tool), the patterning device table MT may be connected to the short-stroke actuator only, or may be fixed.

[0205] The depicted tools can be used in two different modes:

[0206] - in step mode, the patterning device table MT is kept substantially fixed and the entire patterning device image is projected once (i.e. a single "flash") onto a target portion C. The substrate table WT is then shifted in the x and / or y direction so that a different target portion C can be illuminated by the beam PB;

[0207] - In scan mode, essentially the same scenario applies, except that a given target portion C is not exposed in a single "flash". Alternatively, the patterning device table MT can be moved in a given direction (the so-called "scanning direction", e.g. the y-direction) at a speed v, so that the projection beam B is scanned across the patterning device image; while the substrate table WT is simultaneously moved in the same or opposite direction at a speed V=Mv, where M is the magnification of the lens PL (typically M=1 / 4 or 1 / 5). In this way, a relatively large target portion C can be exposed without compromising resolution.

[0208] Fig.19 Another exemplary lithographic projection apparatus 1000 is schematically depicted, in which illumination may be optimized using the methods described herein.

[0209] The lithography projection apparatus 1000 comprises:

[0210] -Source Collector Module SO

[0211] - an illumination system (illuminator) IL configured to condition a radiation beam B (eg EUV radiation).

[0212] a support structure (e.g., patterning device table) MT configured to support a patterning device (e.g., mask or reticle) MA and connected to a first positioner PM configured to accurately position the patterning device;

[0213] a substrate table (e.g. wafer stage) WT configured to hold a substrate (e.g. resist coated wafer) W and connected to a second positioner PW configured to accurately position the substrate; and

[0214] - a projection system (eg, a reflective projection system) PS configured to project the pattern imparted to the radiation beam B by the patterning device MA onto a target portion C of the substrate W (eg, comprising one or more dies).

[0215] As depicted here, the apparatus 1000 is of the reflective type (e.g., employing a reflective patterning device). Note that because most materials are absorptive in the EUV wavelength range, the patterning device can have a multilayer reflector comprising, for example, multiple stacks of molybdenum and silicon. In one example, the multilayer reflector has 40 layer pairs of molybdenum and silicon, where each layer is a quarter wavelength thick. Even smaller wavelengths can be produced by x-ray lithography. Since most materials are absorptive at EUV and x-ray wavelengths, a thin piece of patterned absorbing material on the patterning device topography (e.g., a TaN absorber on top of a multilayer reflector) defines where features will be printed (positive resist) or not printed (negative resist).

[0216] refer to Fig.19 , the illuminator IL receives the extreme ultraviolet radiation beam from the source collector module SO. Methods for generating EUV radiation include, but are not necessarily limited to, converting a material into a plasma state having at least one element, such as xenon, lithium, or tin, through one or more emission lines in the EUV range. In one such method, a plasma, often referred to as laser produced plasma ("LPP"), can be generated by irradiating a fuel (such as a droplet, stream, or cluster of a material having a line emitting element) with a laser beam. The source collector module SO can be a laser ( Fig.19 The laser is used to provide a laser beam for exciting the fuel. The resulting plasma emits output radiation (e.g., EUV radiation) which is collected using a radiation collector disposed in a source collector module. For example, when a CO2 laser is used to provide a laser beam for fuel excitation, the laser and the source collector module may be separate entities.

[0217] In these cases, the laser is not considered to form part of the lithographic apparatus, and the radiation beam is delivered from the laser to the source collector module by means of a beam delivery system comprising, for example, suitable guiding mirrors and / or a beam expander. In other cases, for example, when the source is a discharge produced plasma EUV generator (often referred to as a DPP source), the source may be an integral part of the source collector module.

[0218] The illuminator IL may include an adjuster for adjusting the angular intensity distribution of the radiation beam. Typically, at least the outer radial extent and / or the inner radial extent (commonly referred to as σ outer and σ inner, respectively) of the intensity distribution in a pupil plane of the illuminator may be adjusted. In addition, the illuminator IL may include various other components, such as a faceted field mirror arrangement and a faceted pupil mirror arrangement. The illuminator may be used to adjust the radiation beam to have a desired uniformity and intensity distribution in its cross-section.

[0219] The radiation beam B is incident on a patterning device (e.g. a mask) MA which is held on a support structure (e.g. a patterning device table) MT and patterned by the patterning device. After reflection from the patterning device (e.g. a mask) MA, the radiation beam B passes through a projection system PS which focuses the beam onto a target portion C of a substrate W. With the aid of a second positioner PW and a position sensor PS2 (e.g. an interferometric device, a linear encoder or a capacitive sensor), the substrate table WT can be precisely moved, for example so as to position a different target portion C in the path of the radiation beam B. Similarly, a first positioner PM and a further position sensor PS1 can be used to accurately position the patterning device (e.g. a mask) MA relative to the path of the radiation beam B. The patterning device (e.g. a mask) MA and the substrate W can be aligned using patterning device alignment marks M1, M2 and substrate alignment marks P1, P2.

[0220] The depicted device 1000 may be used in at least one of the following modes:

[0221] 1. In step mode, the support structure (e.g. patterning device table) MT and substrate table WT are kept substantially stationary (i.e. single static exposure) while an entire pattern imparted to the radiation beam is projected at one time onto a target portion C. The substrate table WT is then shifted in the X and / or Y direction so that a different target portion C can be exposed.

[0222] 2. In scan mode, the support structure (e.g. patterning device table) MT and substrate table WT are scanned synchronously (i.e. single dynamic exposure) as a pattern imparted to the radiation beam is projected onto a target portion C. The speed and direction of the substrate table WT relative to the support structure (e.g. patterning device table) MT may be determined by the (de-)magnification and image reversal characteristics of the projection system PS.

[0223] 3. In another mode, the support structure (e.g. patterning device table) MT holding the programmable patterning device is kept substantially stationary and the substrate table WT is moved or scanned while a pattern imparted to the radiation beam is projected onto a target portion C. In this mode, a pulsed radiation source is typically used and the programmable patterning device is updated as necessary after each movement of the substrate table WT or between successive radiation pulses during a scan. This mode of operation can be readily applied to maskless lithography utilizing a programmable patterning device, such as a programmable mirror array of the type mentioned above.

[0224] Fig. 20The apparatus 1000 is shown in more detail, and includes a source collector module SO, an irradiation system IL, and a projection system PS. The source collector module SO is constructed and configured so that a vacuum environment can be maintained in an enclosure 220 of the source collector module SO. An EUV radiation emitting plasma 210 can be formed by a discharge-generated plasma source. EUV radiation can be generated by a gas or vapor (e.g., Xe gas, Li vapor, or Sn vapor), wherein an extremely hot plasma 210 is generated to emit radiation in the EUV range of the electromagnetic spectrum. For example, an extremely hot plasma 210 is generated by causing a discharge that ionizes the plasma at least in part. For efficient generation of radiation, a partial pressure of, for example, 10 Pascals of Xe, Li, Sn vapor, or any other suitable gas or vapor may be required. In an example, an excited tin (Sn) plasma is provided to generate EUV radiation.

[0225] The radiation emitted by the hot plasma 210 is transferred from the source chamber 211 to the collector chamber 212 through an optionally selected gas barrier or contaminant trap 230 (also referred to as a contaminant barrier or foil trap in some cases) positioned in or behind an opening in the source chamber 211. The contaminant trap 230 may include a channel structure. The contaminant trap 230 may also include a gas barrier or a combination of a gas barrier and a channel structure. As is known in the art, the contaminant trap or contaminant barrier 230 further indicated in the present invention includes at least a channel structure.

[0226] The collector chamber 211 may include a radiation collector CO which may be a so-called grazing incidence collector. The radiation collector CO has an upstream radiation collector side 251 and a downstream radiation collector side 252. Radiation passing through the collector CO may be reflected from the grating spectral filter 240 to be focused into a virtual source point IF along the optical axis indicated by the dotted line "O". The virtual source point IF is often referred to as an intermediate focus, and the source collector module is arranged so that the intermediate focus IF is located at or near an opening 221 in the enclosure structure 220. The virtual source point IF is an image of the radiation emitting plasma 210.

[0227] The radiation then passes through an illumination system IL which may include a faceted field mirror arrangement 22 and a faceted pupil mirror arrangement 24 arranged to provide a desired angular distribution of the radiation beam 21 at the patterning device MA, and a desired uniformity of the radiation intensity at the patterning device MA. After reflection of the radiation beam 21 at the patterning device MA held by the support structure MT, a patterned beam 26 is formed and is imaged by reflective components 28, 30 by the projection system PS onto a substrate W held by a substrate table WT.

[0228] More elements than shown may typically be present in the illumination optics unit IL and the projection system PS. Depending on the type of lithographic apparatus, a grating spectral filter 240 may optionally be present. Additionally, more mirrors may be present than shown in the figures, for example, more than 100 mirrors may be present in the projection system PS. Fig. 20 The reflective elements shown have 1 to 6 additional reflective elements.

[0229] like Fig. 20 The collector optics CO illustrated in FIG. 1 is depicted as a nested collector with grazing incidence reflectors 253, 254 and 255, which is merely an example of a collector (or collector mirror). The grazing incidence reflectors 253, 254 and 255 are arranged axially symmetrically around the optical axis O, and this type of collector optics CO can be used in combination with a discharge produced plasma source, which is often referred to as a DPP source.

[0230] Alternatively, the source collector module SO may be as follows Fig.21 Components of the LPP radiation system shown. The laser LA is arranged to deposit laser energy into a fuel such as xenon (Xe), tin (Sn) or lithium (Li), thereby forming a highly ionized plasma 210 with an electron temperature of several 10 eV. The high-energy radiation generated during the deexcitation and recombination of these ions is emitted from the plasma, collected by the near normal incidence collector optics CO, and focused onto an opening 221 in the enclosure structure 220.

[0231] The entire contents of U.S. Patent Application Publication No. US 2013-0179847 are incorporated herein by reference.

[0232] The concepts disclosed herein can simulate or mathematically model any general imaging system for imaging sub-wavelength features, and can be used in particular for emerging imaging technologies that can produce shorter and shorter wavelengths. Emerging technologies already in use include EUV (extreme ultraviolet), DUV lithography that can produce wavelengths of 193nm by using ArF lasers and even 157nm by using fluorine lasers. In addition, EUV lithography can produce wavelengths in the range of 5nm to 20nm by using a synchrotron or by using high energy electrons to irradiate a material (solid or plasma) so as to produce photons in this range.

[0233] Although the concepts disclosed herein may be used for imaging on substrates such as silicon wafers, it should be understood that the disclosed concepts may be used with any type of lithography imaging system, such as those used for imaging on substrates other than silicon wafers.

[0234] The above techniques have been described for the specific application of improving a particular lithography process of imaging a portion of a design layout onto a substrate using a lithography apparatus.

[0235] Embodiments generally provide techniques for using image-related metrics to improve any of the manufacturing, testing, measurement, and other processes of semiconductor structures on substrates. Specifically, new image-related metrics are generated. In this document, the new image-related metrics are referred to as overlay or overlay margin. The overlay margin provides an indication of the tolerance of overlay error in the feature being manufactured.

[0236] Embodiments also provide techniques for improving determination of control parameters in any of processes performed during fabrication, testing, measurement, and other processes that may rely on overlay margins for semiconductor structures on a substrate.

[0237] The overlay margin may be determined from multiple images of different layers and portions of the substrate. Each image may be obtained by an imaging device such as an electron beam based metrology device or any type of scanning electron microscope. An electron beam device (e.g., manufactured by HMI) may have a field of view of 10 μm x 10 μm.

[0238] The processes that can be improved by the techniques of the embodiments include any of the following: photolithography, scanning, primer coating, resist coating, soft baking, post-exposure baking, developing, hard baking, measurement / detection, etching, ion implantation, metallization, oxidation, and chemical mechanical polishing. Overlap margin can be used to determine control parameters for any of these processes and any combination of these processes.

[0239] Embodiments may include performing both computational measurement and control processes. The computational process includes obtaining one or more images of a portion of a substrate on each of a plurality of layers of the substrate. Each obtained image includes features contained by a structure being fabricated on the substrate. Overlay margins are calculated depending on properties of the features, such as the profile of the features. Overlay margins may then be relied upon to determine control parameters for processes in the fabrication process and other processes for controlling the features.

[0240] Fig.24 An image showing features on a portion of a substrate. For example, the image may represent a 10 μm x 10 μm area on the substrate. The thick line in the image is the target outline of one of the features. The thin lines in the image are the actual outline of the feature being manufactured. Although the ideal shape of the feature may be a rectangle, the target outline is curved / rounded because this is the closest possible shape to a rectangle that can be manufactured, and is therefore the optimal outline that can be obtained in practice. The ideal shape may alternatively be used as the target shape.

[0241] Fig.25 A plurality of superimposed images are shown. The superimposed images may be obtained from one or more corresponding images of the same feature in different layers of a substrate and / or images of multiple features on the same layer of a substrate, respectively. The images may additionally or alternatively be images of features on multiple different substrates and / or images of the same feature on the same layer of a substrate but acquired by different imaging devices.

[0242] When the images are superimposed, an alignment process has been performed. The alignment process may be based on one or more reference positions in or superimposed on each of the images to align the images so that there is no overlap error between the images. For example, the alignment process may include aligning target designs of features in the images so that there is no overlap error between the target designs. The alignment process may be based on aligning the images in dependence on gds data. The effect of performing the alignment process is to remove the effects of any overlap errors between different images.

[0243] The overlay margin is a measure of the random variation of a feature in a stack of aligned images. The overlay margin can be calculated in dependence on the difference between the profiles of corresponding features in the aligned versions of the images. The overlay margin can also be calculated in dependence on a target profile of the feature. For example, for each of the images, the overlay margin can be calculated in dependence on a comparison of the feature in the image with a target for the feature. The difference between the profile of the feature in the image and the profiles of the features in other images and the target profile of the feature can be determined by a number of known specific image-related metrics, such as critical dimension uniformity (CDU), line width roughness (LWR), critical dimension amplitude, and placement error.

[0244] Overlap margin is related to the known image metric edge placement error (EPE). EPE is an image metric that provides an overall representation of the difference between the outline of one or more images of a feature and the target outline of the feature. EPE includes the overlay error between the image of the feature and the target outline for the feature.

[0245] The overlay margin differs from the EPE in that the overlay margin does not include the overlay error between images of the features because the overlay error is removed by the alignment process described above.

[0246] The method for determining the overlap margin is shown in Equation 8.

[0247] Overlap margin = EPE - Overlap error (Equation 8)

[0248] Therefore, the overlay margin can be calculated by calculating the EPE and the overlay error. The overlay error can be calculated depending on the alignment performed on the images. The overlay margin can then be calculated by subtracting the overlay error from the calculation of the EPE.

[0249] Note that the overlay error in Equation 8 can be calculated as a combination of the actual amount of overlap and the design specification. This is because failure conditions may occur when overlap is expected between features in different layers of the structure, but even if overlap occurs, the desired overlap area cannot be achieved. Similarly, failure conditions may occur when separation of features is expected in different layers of the structure, but even if the features are separated, the desired amount of separation cannot be achieved. The design specification includes the required feature overlap area and / or the required amount of feature separation. Therefore, it is appropriate to rely on a combination of the actual amount of overlap and the design specification to calculate the overlay error.

[0250] The overlay margin may alternatively be determined relying on a combination of contributions to the overlay margin in the alignment images. This is shown in Equation 9.

[0251]

[0252] In equation 9:

[0253] HR OPC Reliance on errors caused by optical proximity effect correction;

[0254] σ PBA Reliance on the error caused by averaging of proximity deviations;

[0255] σ LWR Reliance on the error caused by line width roughness; and

[0256] σ CDU Depends on the error caused by critical dimension uniformity.

[0257] In Equation 9, the contributions to the determined overlap margin are OPC, PBA, LWR, and CDU. Embodiments include alternative constructions of the equation for determining overlap margin that include one or more other contributions to overlap margin and / or do not include one or more of the contributions to overlap margin included in Equation 9. Overlap margin may be calculated relying on all contributions to the EPE calculation except for the overlap error.

[0258] Each of the images is typically only a small portion of the substrate. For example, each image may represent a 10 μm x 10 μm area on the substrate. The overlay margin may be calculated relying on multiple images of different layers of the same portion of the substrate. This is the local overlay margin of that portion of the substrate.

[0259] A plurality of local overlay margins may be calculated for each of a plurality of different portions of a substrate, wherein each of the local overlay margins is calculated relying on a plurality of images of different layers of the same portion of the substrate. The local overlay margins may be obtained at all locations on the substrate or at only some locations on the substrate. When the local overlay margins are obtained at only some locations on the substrate, the locations may be selected to provide a distinctive identification of the substrate.

[0260] Each image may additionally or alternatively be considered as comprising a plurality of segments.A local overlap margin may be calculated for each of the segments of an image, such that there are a plurality of local overlap margins for each image.

[0261] The overlay margin of the substrate may include a plurality of local overlay margins, wherein each of the local overlay margins is calculated in dependence on an image and / or a segment of an image of a different portion of the substrate.

[0262] The overlap margin may be defined as a minimum overlap margin of features within an image and / or a segment of an image.

[0263] The overlay margin may be represented as an overlay margin map, which shows the local variation of the overlay margin across the substrate.

[0264] The overlay margin may alternatively be represented as an overlap overlay margin graph, which shows the local variation of the overlap overlay margin across the substrate.

[0265] A global overlay margin may be calculated, the global overlay margin being an average of local overlay margins and / or cross-overlay margins of the substrate.

[0266] The overlap margin and a representation of the overlap margin may be calculated for each of a plurality of values ​​of each parameter that may contribute to the overlap margin. The dependence of the overlap margin on each parameter may be calculated or inferred from the overlap margin calculated for the value of the parameter. The dependence of the overlap margin on a plurality of parameters may also be determined.

[0267] For example, an overlap margin map can be generated that shows the variation of the overlap margin between two or more layers of a substrate across the surface of the substrate. The overlap margin map can be determined based on the critical dimension (CD). Thus, a multi-dimensional metric is generated that can be used for co-optimization of overlay and CD.

[0268] Parameters that may contribute to the overlay margin may include focus, dose, illumination pupil shape (e.g., ellipticity), aberrations (e.g., coma, sphericity, astigmatism), etch rate, overlay, contrast, critical dimension, chuck temperature, gas flow, and RF power distribution. The dependence of the overlay margin on one or more of these parameters may be determined.

[0269] The yield of semiconductor manufacturing process depends on the occurrence of manufacturing errors. Manufacturing errors occur when the required area of ​​overlap between features in different layers of the structure does not occur. Manufacturing errors also occur when the required minimum separation of features in different layers of the structure is not achieved. EPE is a measure of the positional variation of features and feature profiles, and can be used to determine the expected yield of correctly manufactured structures and / or the probability that the structure is incorrectly manufactured. Due to the relationship between overlap margin and EPE, as shown in equation 8, overlap margin can be used to determine the allowable amount of overlap error, so as to achieve the expected yield of correctly manufactured structures and / or the probability that the structure is incorrectly manufactured.

[0270] Overlay error depends on a number of controllable parameters. Therefore, the values ​​and possible value ranges of the parameters affecting the overlay error can be determined depending on the overlay margin so that the overlay error is within the range expected to achieve an expected yield. The expected yield can be the expected yield according to the manufacturing specification.

[0271] Embodiments include determining parameters for controlling a manufacturing, inspection, and / or testing process of a structure on a substrate in dependence on the overlap margin. Parameters that may be controlled in dependence on the overlap margin include focus, dose, illumination pupil shape (e.g., ellipticity), aberrations (e.g., coma, sphericity, astigmatism), etch rate, overlap, contrast, critical dimension, chuck temperature, gas flow, and RF power distribution. The process controlled by the parameters may be a photolithography process, a primer coating process, a resist coating process, a soft bake process, a post-exposure bake process, a development process, a hard bake process, a measurement / inspection process, an etching process, an ion implantation process, a metallization process, an oxidation process, and / or a chemical mechanical polishing process.

[0272] The permissible level of EPE depends on manufacturing specifications. Manufacturing specifications may depend on one or more of the following: the expected yield, the maximum probability that a feature is incorrectly manufactured, the determined maximum allowable value of EPE, the determined maximum allowable overlay error; and the expected yield of the semiconductor device.

[0273] As described above, the EPE depends on the overlap margin and the overlap error. Therefore, the overlap margin allows the determination of a constraint on the overlap error so that the EPE is at a specific level. The dependency of the overlap error on each parameter can be determined. Therefore, the value and / or value range of each of the parameters can be determined in dependence on the overlap margin.

[0274] Each parameter may be determined in dependence on one or more of an overlay margin map, one or more local overlay margins, and a global overlay margin.

[0275] Parameters influencing the overlay error may also be jointly determined such that an applied value of one of the control parameters depends on an applied value of another control parameter. Jointly determining at least two of the control parameters may depend on a combined effect of the at least two control parameters and / or a mutual dependency of the at least two control parameters. By jointly determining the control parameters, the combined effect of the control parameters and / or the mutual dependency of the effects of the control parameters may be used to advantageously improve the determination of the control parameters for improving yield or for optimization with respect to any other objective.

[0276] Constraints on the rate of change and range of control parameters during the process can be determined. For example, during the manufacture of the device, the extent to which the focus can be changed between two different positions on the substrate will be limited due to the rate at which the focus can be changed and the speed of movement. An embodiment includes using the determined constraints on the control parameters to perform an optimization process on the control parameters given an allowable overlay error. For example, given constraints on the parameter values ​​that can be applied, the parameters can be set at a level that results in an increased contribution to the overlay error. This can be made possible by controlling another parameter to reduce the contribution of the other parameter to the overlay error, while the total overlay error remains within an acceptable range.

[0277] The overlay error may be dependent on at least one of the commonly determined control parameters; and a size of a feature fabricated on the semiconductor device may be dependent on at least one other of the commonly determined control parameters.

[0278] Commonly determined control parameters may include focus, dose, illumination pupil shape, aberrations, etch rate, overlay, contrast, critical dimension, chuck temperature, gas flow, and RF power distribution.

[0279] As described above, the relationship between the overlap margin and the applied parameters can be determined. The applied values ​​and applied ranges of the parameters can be determined depending on how the parameters affect the overlap margin.

[0280] Determination of applicable values ​​and applicable ranges for the plurality of parameters may be made depending on the effects of the parameters on both the overlay margin and the overlay error.

[0281] The joint determination of applicable values ​​and applicable ranges of the plurality of parameters may be performed depending on the impact of the plurality of parameters on both the overlay margin and the overlay error.

[0282] For example, one or more parameters may be determined so as to minimize the overlay margin, thereby reducing the constraints on the overlay error. This may allow other parameters to be set to values ​​that increase their contribution to the overlay error. Specifically, an overlay margin map may be determined as a function of the critical dimension (CD). This may then be used for overlay and CD co-optimization.

[0283] Fig.26 is a flow chart of a process for determining image metrics of features on a substrate according to an embodiment.

[0284] In step 2601, the process begins.

[0285] In step 2603, a first image of a plurality of features on a substrate is obtained.

[0286] In step 2605, one or more other images corresponding to a plurality of features on the substrate are obtained, wherein at least one of the one or more other images is of a different layer of the substrate than the first image.

[0287] In step 2607, aligned versions of the first image and the one or more other images are generated by performing an alignment process on the first image and the one or more other images, wherein the alignment process substantially removes the effects of any overlap errors between features in the first image and corresponding features in each of the one or more other images.

[0288] In step 2609, image metrics are calculated relying on comparison of features in the aligned version of the first image with corresponding features in aligned versions of one or more other images.

[0289] In step 2611, the process ends.

[0290] The embodiments include numerous modifications and variations of known processes.

[0291] Any of the techniques described throughout this document may be used to determine and optimize image-related metrics of an embodiment.

[0292] Embodiments determine control parameters for controlling a process in the manufacture of a semiconductor device. The process includes any process, including a measurement process, and can be performed by any known device. The process according to the embodiment can be controlled by a computing system executing instructions stored on a non-transitory computer readable medium for performing the process.

[0293] As explained above, control parameters may be determined in dependence on metrology data. The metrology data may be obtained, for example, from an image of features on a substrate produced by an electron beam inspection tool or an optical inspection tool. The metrology data may include statistical properties of the features measured. A problem with obtaining metrology data over the entire surface of a substrate is that this requires excessively long measurement times.

[0294] According to another embodiment, a technique for reducing the measurement time required to obtain metrology data is provided. A sampling scheme and / or one or more fields of view FOVs for obtaining the metrology data are determined such that the number of measurements required to obtain the metrology data is reduced, and thus the measurement time is reduced. The sampling scheme and / or one or more fields of view are determined such that the metrology data is still determined at the most critical locations on the surface of the substrate, such that the accuracy of the metrology data is not significantly reduced by the reduced number of measurements.

[0295] Specifically, the allowable tolerance level of EPE can be determined across the entire substrate or across one or more portions of the substrate, which may correspond to a field, a die, or a functional block within a die. This can be used to determine an EPE criticality map for the substrate, which identifies where the most critical locations on the substrate are for EPE performance.

[0296] The EPE criticality map can then be used to determine a sampling scheme and / or field of view for obtaining metrology data. For example, the EPE criticality map can be used to determine the locations on the substrate at which measurements are obtained, the size of the field of view used at each location, and / or the density of measurements at each location.

[0297] The technique according to the present embodiment can reduce the number of measurements required to obtain metrology data to about 5% of the number of measurements required to measure the corresponding entire substrate or portion of the substrate according to the known technique. Therefore, the embodiment significantly reduces the time required to obtain metrology data.

[0298] Embodiments also include generating other types of criticality maps and using the criticality maps to determine sampling schemes and / or fields of view. Criticality maps can be based on other metrics besides EPE, such as overlap or LWR. However, EPE is a preferred metric for criticality maps because EPE is a good indicator of yield.

[0299] The technology according to the present embodiment is described in more detail below.

[0300] Obtain design data, such as mask design data / GDS data, which represents the expected design and / or shape of features of some or all layers manufactured on the substrate. The design data can be used to determine EPE criticality, that is, the EPE tolerance that should be observed. For example, EPE criticality can define the allowable minimum spacing between features in the same and / or different layers, and / or the allowable minimum overlap error of features in different layers. An EPE criticality of 15nm can mean that if the edge displacement of a feature such as a contact hole exceeds 15nm, this will result in a higher probability that the device including the contact hole is a defective product.

[0301] The EPE criticality can be used to generate an EPE criticality map. The EPE criticality map provides predicted process margin distributions for features distributed across the entire substrate or across an area of ​​the substrate.

[0302] Data is also obtained regarding the capabilities of the metrology / inspection tools to be used, such as possible field of view sizes and / or measurement densities that can be used.

[0303] Data is also obtained on the number of occurrences of the measurement results for each feature, which number of occurrences is used to determine the EPE of the feature with sufficient accuracy. The number of occurrences of the measurement results for a feature may be less than 10,000. However, the number of occurrences of the measurement results for a feature is typically at least 10,000 and can be 100,000 or more. The number of occurrences of each feature can be predetermined or set by the user.

[0304] Data about the locations of overlay targets is also obtained. Each overlay target is a location where overlay measurements can be made. Overlay targets include intra-die targets and scribe targets. A die is a portion of a substrate in which features of a device can be formed, and an intra-die target is a location within the die. The dies on the substrate are separated from each other by one or more scribe lines, and the scribe targets are located within the scribe lines.

[0305] The EPE criticality diagram may be generated further in dependence upon at least some of the acquired data identified above in addition to the design data.

[0306] An example of an EPE criticality map for a region of a substrate is shown in Figure 27a. Figures 27b to 27d show one or more fields of view that may be used to obtain metrology data at one or more sub-regions using the region of the substrate shown in Figure 27a.

[0307] Figure 27a depicts the variation in EPE criticality of features distributed across an area 2700 on the substrate. This area 2700 is slightly larger than the maximum field of view 2710 of the inspection tool, as shown in Figure 27b.

[0308] In Figure 27a, regions 2702 shown in light grey are associated with features having relatively large process margins, such as an EPE criticality (i.e., tolerance) of 25 nm. In cases where the process corresponding to the EPE criticality map has a maximum EPE of less than 25 nm, the probability of yield loss due to an edge placement error occurring in one of the regions 2702 is low.

[0309] Region 2704, shown in darker grey, includes features having an EPE criticality of 18 nm.

[0310] Region 2706, shown in black, includes features with an EPE criticality of 14 nm. These features are the most critical features identified by the EPE criticality map.

[0311] The remainder of region 2700 includes only features with an EPE criticality greater than 25 nm. For example, the EPE criticality may be 30 nm or greater. These features are the least critical features identified by the EPE criticality map.

[0312] FIG. 27 b depicts a known method of obtaining metrology data associated with region 2700. Field of view 2710 is selected to have a size large enough to encompass all of regions 2702, 2704, and 2706. Because the larger field of view size results in a large number of features being measured, a disadvantage of this method is that the measurement acquisition time is relatively long. This is inefficient because more than 95% of the measurement area is associated with non-critical features. Therefore, the number of measurements that contribute to the most critical features accounts for only a small proportion of the total number of measurements.

[0313] FIG. 27 c depicts a method of determining a field of view setting according to this embodiment.

[0314] The field of view setting may include the number of fields of view, the size of each field of view, and the location of each field of view. The size of the field of view may vary. For example, the field of view size may vary from as much as about 40 μm×40 μm to as low as about 0.1 μm×0.1 μm. The maximum field of view size may be greater than or less than 40 μm×40 μm. For example, the maximum field of view size may be 36 μm×36 μm or 15 μm×15 μm. Similarly, the minimum field of view size may be greater than or less than 0.1 μm×0.1 μm. For example, the minimum field of view size may be 1 μm×1 μm. Although the field of view may be square, it may alternatively have other shapes.

[0315] The sampling scheme may include the field of view settings and / or measurement densities within each field of view described above.

[0316] In this embodiment, the field of view size is reduced from that of the field of view in FIG. 27b, and the number of fields of view used to obtain measurements is increased from one to two. By obtaining measurements only at locations 2712 and 2714, sufficient information is obtained to obtain accurate metrology data for critical features on the substrate in an appropriate manner. The field of view size at the measurement locations is substantially smaller than the field of view size used in FIG. 27b, such that the combined size of field of view 2712 and field of view 2714 is smaller than the size of field of view 2710.

[0317] Figure 27d shows an alternative implementation of the method according to the present embodiment, wherein the size of the field of view is further reduced to field of view 2718 and field of view 2716. This further reduction in the size of the field of view further reduces the measurement time for each field of view.

[0318] Increasing the number of fields of view used to obtain metrology data may require additional operations, such as moving the substrate within the inspection tool to change between fields of view. However, the increase in measurement time caused by this may be substantially less than the decrease in measurement time caused by a decrease in the size of the field of view, because reducing the size of the field of view reduces the number of measurements. In addition, when the inspection tool is an electron beam tool, measurements can be performed faster when the size of the field of view is reduced, because the electron beam offset (i.e., the scan amplitude) can be reduced, and this reduces the measurement time per field of view.

[0319] When determining the size and number of fields of view to be used in order to reduce measurement time, an embodiment includes determining whether the time required to implement the field of view change is less than the reduction in measurement time resulting from the field of view change. The determination to change the size and number of fields of view can only be made if the change should result in a total time saving.

[0320] Additionally or alternatively, to change the field of view size, the sampling scheme can be changed by changing the measurement density within each field of view. The accuracy of the measurement data and the measurement time both increase with increasing measurement density. The measurement density can be varied so that the measurement density is highest when measuring areas with the most critical characteristics (i.e., the most critical areas) and decreases when measuring areas with lower critical characteristics (i.e., the lower critical areas). For example, in the most critical area 2706, at least four measurements can be made per unit area; in the medium critical area 2704, at least two measurements can be made per unit area; and in the lower critical area 2702, at least one measurement can be made per unit area.

[0321] Embodiments also include obtaining an allowed maximum measurement time for obtaining metrology data of features on at least a portion of the substrate, and determining a sampling scheme and / or a field of view setting in dependence on the obtained allowed maximum measurement time, such that a time required to obtain metrology data according to the determined sampling scheme and / or field of view setting is less than or equal to the allowed maximum measurement time.

[0322] Thus, this embodiment allows a substantial reduction in measurement time.The sampling scheme and / or field of view is determined such that the sampling scheme and / or field of view is still suitable for obtaining adequate measurements of critical features.

[0323] Techniques for using an EPE criticality map to determine a sampling scheme and / or one or more fields of view are described in more detail below.

[0324] The EPE criticality diagram shows the variation of the EPE tolerance over the entire substrate. A tolerance level, i.e., a threshold, can be set, and all areas of the EPE criticality diagram that have tolerances below the set tolerance level are determined to be measured or may be measured. For example, the tolerance level can be 17.25nm, so that it is determined that only all areas on the substrate with an allowable EPE of 17.25mm or less are measured or may be measured. It can be determined that all areas on the substrate with an allowable EPE exceeding 17.25mm are not measured. This can reduce the total measurement area to approximately 5% of the total area that can be measured. The tolerance level can be predetermined or set by the user. The total area measured thereon decreases as the tolerance level decreases.

[0325] Therefore, one or more fields of view to be used are initially determined so that the area including the most critical features (i.e., the area with the lowest EPE tolerance) is measured. For each of the initially determined fields of view, small changes in the position and / or size of each field of view can be made to increase the number and / or type of critical features in the field of view.

[0326] As described above, data on the number of occurrences of the measurement results of each feature of interest required to determine the EPE or other performance indicators of the feature of interest with sufficient accuracy has been obtained. The feature of interest may include a single feature or an arrangement of multiple features. The number of occurrences of the measurement results of the feature of interest may be less than 10,000. However, the number of occurrences of the measurement results of the feature is typically at least 10,000 and may be 100,000 or more.

[0327] The sampling scheme is arranged so that a sufficient number of occurrences of each feature of interest are measured.For each of the fields of view, small changes in its position and / or size may be made in order to increase the number and / or type of occurrences of features of interest (ie, critical features).

[0328] For each type of criticality feature, if there are not enough occurrences of the criticality feature in a field of view, a determination can be made to increase the number of fields of view to increase the number of occurrences to a desired amount. The number of fields of view can be increased by using one or more additional fields of view within a die, one or more other dies on the same substrate, and / or one or more other dies on one or more other substrates.

[0329] For each type of criticality feature, if more than one field of view includes the same arrangement of criticality features, as might be expected for fields of view of a DRAM memory pad, it can be determined that essentially no more fields of view are used, a minimum number of such fields of view being necessary in order to achieve the desired number of occurrences for the criticality feature.

[0330] The determination of which fields of view to use may be dependent upon the requirement to ensure that the fields of view provide adequate coverage of the die. This is to ensure that there are not substantial areas on the die where field of view measurements are not performed. Additionally or alternatively, this requirement may be met by using fields of view that would result in exceeding the required minimum number of occurrences of a class of criticality features being measured and / or using fields of view that do not include criticality features for the area.

[0331] Alternatively or in addition, the field of view may be determined so that the measurements of the field include at least one measurement from all significant areas of the die. A field is an area of ​​the substrate that includes multiple identical designs of the die. Different arrangements of the fields of view may be used for two or more of the dies. The arrangement of the fields of view may be configured so that the arrangement of the fields of view is different between the dies. Although no separate field of view arrangement for a single die has a field of view in every significant area of ​​the die's design, by using different arrangements of the fields of view between dies that may all have the same design, the fields of view may be selected so that at least one field of view is located in every significant area of ​​the die's design across the substrate.

[0332] As described above, data about the position of the overlay target including the in-die target and the scribing target is obtained. The present embodiment includes automatically or manually selecting the overlay target to be used by the user so that the overlay target used is close to the area where the field of view on the substrate is located, because the EPE of the feature is the most critical, that is, the EPE tolerance is low. Using an overlay target close to the field of view used reduces the overlay measurement error of the field of view on which the EPE is relied upon.

[0333] Accordingly, embodiments allow for determining a sampling scheme and / or one or more fields of view for a region of a substrate faster than obtaining measurements of that region of the substrate as a whole. Embodiments include determining a sampling scheme that reduces the size of one or more fields of view used and / or increases the number of fields of view used. The measurement density may be the same for all fields of view, or the measurement density may vary between fields of view depending on at least the EPE criticality of features within each field of view.

[0334] The present embodiment includes numerous modifications and variations of the techniques described above.

[0335] Specifically, the EPE criticality map may be generated relying on modeling data and / or actual measurement data in addition to or instead of design data (ie, reticle design data / GDS data).

[0336] For example, for one or more of the determined fields of view, at least some of the design data may be replaced by data generated by a lithography manufacturing check (LMC) simulation of one or more of the multiple layers. The simulation may include the effects of focus and dose changes.

[0337] Alternatively or additionally, for one or more of the determined fields of view, at least some of the design data may be replaced by data generated by stochastic EPE (SEPE) simulations of one or more of the multiple layers.

[0338] Alternatively or in addition, a physical model of any of the development step, deposition step, or etching step (or any related process step) can be used to determine more realistic feature characteristics, such as edge locations thereof. This model can be calibrated to feature layout data actually measured, for example, by an electron beam metrology tool (such as a SEM). For example, for one or more of the determined fields of view, at least some of the design data can be replaced by data generated by overlaying profiles of existing measurements of one or more of the multiple layers. For example, a measured overlay profile of a first layer can be used together with GDS data for a second layer to determine tolerances, specifications, and / or control parameters for the second layer.

[0339] Alternatively or additionally, for one or more of the determined fields of view, a source mask optimization (SMO) simulation may be performed on one or more of the multiple layers to determine whether corrections may be made to the source or wavefront to improve one or more processes.

[0340] Simulation data only needs to be generated according to an embodiment for data within the reduced size of the field of view, and therefore simulation times are much faster than the time required to perform simulations for larger fields of view.

[0341] Although embodiments have been described primarily with reference to the generation of EPE criticality maps, embodiments more generally include generating criticality maps and using the criticality maps to determine sampling schemes and / or fields of view, wherein the criticality maps are based on any of a plurality of metrics. For example, embodiments include generating and using an overlap or LWR criticality map in place of an EPE criticality map. A criticality map according to an embodiment may be referred to as a process margin distribution because the criticality map provides an indication of changes in a process margin (such as an EPE) across an entire region.

[0342] Fig.28 is a flow chart of a process for determining a field of view setting for an inspection tool having a configurable field of view FOV, according to an embodiment.

[0343] In step 2801, the process begins.

[0344] In step 2803, a process margin distribution of features on at least a portion of a substrate is obtained.

[0345] In step 2805, a threshold is obtained.

[0346] In step 2807, one or more regions on at least a portion of the substrate are identified depending on the obtained process margin distribution and the threshold value.

[0347] In step 2809, a field of view setting is determined depending on the identified one or more regions.

[0348] In step 2811, the process ends.

[0349] Other embodiments of the present invention are disclosed in the following numbered aspects:

[0350] 1. A method of determining a field of view setting for an inspection tool having a configurable field of view (FOV), the method comprising:

[0351] obtaining a process margin distribution of features on at least a portion of a substrate;

[0352] Get the threshold value;

[0353] identifying one or more regions on at least a portion of the substrate depending on the obtained process margin distribution and the threshold; and

[0354] A field of view setting is determined in dependence upon the identified one or more regions.

[0355] 2. The method according to aspect 1, wherein the method comprises determining a sampling scheme, wherein determining the sampling scheme comprises one or more of the following:

[0356] jointly determine the sampling scheme and field of view setting;

[0357] determining the sampling scheme dependent on the field of view setting;

[0358] determining the field of view setting dependent on the sampling scheme;

[0359] Determining the sampling scheme depending on the process margin distribution;

[0360] determining the sampling scheme in dependence on the threshold; and

[0361] The sampling scheme is determined in dependence upon the identified one or more zones.

[0362] 3. The method of aspect 2, wherein determining the sampling scheme comprises determining one or more of the following:

[0363] The number of fields of view used;

[0364] The location of each field of view;

[0365] The size of each field of view; and

[0366] The density of the measurements performed using the images obtained from each field of view.

[0367] 3. A method according to any of the preceding aspects, wherein determining the field of view settings comprises determining the number of fields of view, the size of each field of view and / or the position of each field of view.

[0368] 4. A method according to any preceding aspect, wherein the size of each field of view is configurable at the location of each field of view; and

[0369] Determines the use of more than one size of field of view.

[0370] 5. A method according to any of the preceding aspects, wherein the field of view setting and / or sampling scheme is further determined based on an expected measurement time associated with implementation of the sampling scheme on one or more regions of the at least a portion of the substrate, such that the determined sampling scheme provides a total measurement time that is less than a sampling scheme for measuring all of the features on the at least a portion of the substrate.

[0371] 6. A method according to any preceding aspect, wherein the process margin distribution comprises data on variations in tolerance levels of indicators of features on at least a portion of a substrate; and

[0372] The identified one or more regions on the at least a portion of the substrate are regions where the process margin distribution includes a tolerance level less than the threshold value.

[0373] 7. A method according to any of the preceding aspects, wherein the determined field of view settings and / or sampling schemes depend on a determined minimum number of occurrences of at least one feature required to determine an indicator of the at least one feature.

[0374] 8. A method according to any of the preceding aspects, wherein determining the field of view setting and / or sampling scheme comprises determining a number of fields of view to be increased in order to achieve the minimum number of occurrences of at least one feature.

[0375] 9. A method according to any of the preceding aspects, wherein determining the field of view setting and / or sampling scheme includes: determining to reduce the number of fields of view so as to avoid using significantly more fields of view than are required to achieve the minimum number of occurrences of at least one feature.

[0376] 10. The method according to any of the preceding aspects, further comprising: obtaining an allowed maximum measurement time for obtaining metrology data of a feature on at least a portion of the substrate;

[0377] The field of view setting and / or sampling scheme is determined depending on the obtained maximum allowable measurement time, so that the time required to obtain the measurement result according to the determined field of view setting and / or sampling scheme is less than or equal to the maximum allowable measurement time.

[0378] 11. The method according to any of the preceding aspects, wherein the process margin distribution comprises a value of an indicator of a feature; and

[0379] The indicators are edge placement error EPE; local overlap LOVL; or local critical dimension uniformity LCDU.

[0380] 12. The method according to any of the preceding aspects, wherein the process margin distribution is an EPE criticality map.

[0381] 13. The method according to any of the preceding aspects, wherein the inspection tool is an electron beam based inspection tool or an optical inspection tool.

[0382] 14. A method according to any preceding aspect, wherein the inspection tool is constituted by a metrology device.

[0383] 15. The method according to any of the preceding aspects, wherein the process margin distribution depends on design data of one or more layers on the substrate.

[0384] 16. The method of clause 16, wherein the design data for one or more layers on a substrate is dependent on design data for one or more masks used to process the one or more layers on a substrate.

[0385] 17. A method according to any preceding aspect, wherein the process margin distribution relies on a modelling step of the process on one or more layers.

[0386] 18. The method of clause 18, wherein the modeling step involves use of a physical model that predicts one or more properties of actual device features formed after exposing the mask, and one or more other process steps performed after exposing the mask.

[0387] 19. A method according to any preceding aspect, wherein the process margin distribution relies on actual measurement data on one or more layers.

[0388] 20. The method of any preceding aspect, wherein the threshold is based on a process yield requirement, such as a required minimum probability that a manufacturing process will produce a functional device on the at least a portion of the substrate.

[0389] 21. The method according to any of the preceding aspects, wherein the field of view is in the range of 0.1 μm x 0.1 μm to 40 μm x 40 μm.

[0390] 22. A method of obtaining metrology data of a feature on at least a portion of a substrate, the method comprising:

[0391] Determining a sampling plan and / or a field of view (FOV) setting for an inspection tool according to a method according to any of the preceding aspects;

[0392] obtaining one or more images of corresponding one or more regions on at least a portion of the substrate using the determined sampling scheme and / or field of view setting; and

[0393] Metrology data of features on at least a portion of the substrate is determined in dependence upon the acquired one or more images.

[0394] 23. A computing system and an inspection tool, wherein:

[0395] The computing system is arranged to determine a sampling scheme and / or field of view (FOV) setting for an inspection tool according to the method of any one of aspects 1 to 22; and

[0396] The inspection tool is arranged to obtain an image of one or more regions of at least a portion of the substrate in dependence on the determined sampling scheme and / or field of view setting.

[0397] 24. A metrology device comprising the computing system according to aspect 24 and an inspection tool.

[0398] 25. A non-transitory computer-readable medium comprising instructions which, when executed, cause a computing system to perform the method of any one of clauses 1 to 22.

[0399] A system according to an embodiment may comprise a computing system and an electron beam apparatus, wherein the electron beam apparatus is arranged to obtain an image of one or more substrates.The system may comprise a lithographic apparatus and / or a metrology apparatus.

[0400] Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. It is intended that the specification and examples be considered exemplary only, with the true scope and spirit of the invention being indicated by the following claims. In addition, where the application has listed the steps of a method or process in a particular order, it is possible or even advantageous in certain circumstances to change the order in which some steps are performed, and it is intended that the specific steps of a method or process in the claims set forth below are not to be considered order-specific unless such order specificity is explicitly stated in the claims.

Claims

1. A method of determining a field of view setting for an inspection tool having a configurable field of view (FOV), the method comprising: obtaining a process margin distribution of features on at least a portion of a substrate; Get the threshold value; identifying one or more regions on at least a portion of the substrate depending on the obtained process margin distribution and the threshold; as well as A field of view setting is determined in dependence upon the identified one or more regions.

2. The method according to claim 1, wherein: The method includes determining a sampling scheme, wherein determining the sampling scheme includes one or more of the following: jointly determine the sampling scheme and field of view setting; determining the sampling scheme in dependence on the field of view setting; determining the field of view setting in dependence upon the sampling scheme; determining the sampling scheme depending on the process margin distribution; determining the sampling scheme in dependence on the threshold; and The sampling scheme is determined in dependence upon the one or more zones identified.

3. The method according to claim 2, wherein: Determining the sampling plan includes determining one or more of the following: The number of fields of view used; The location of each field of view; The size of each field of view; and The density of the measurements performed using the images obtained from each field of view.

4. The method according to claim 1, wherein: Determining the field of view settings includes determining the number of fields of view, the size of each field of view, and / or the position of each field of view.

5. The method according to claim 1, wherein: The size of each field of view is configurable at the location of each field of view; and Determines the use of more than one size of field of view.

6. The method according to claim 1, wherein: The field of view settings and / or sampling scheme are further determined based on an expected measurement time associated with implementation of the sampling scheme on one or more regions of the at least a portion of the substrate, such that the determined sampling scheme provides a total measurement time that is less than a sampling scheme that measures all of the features on the at least a portion of the substrate.

7. The method according to claim 1, wherein: The process margin profile includes data regarding variations in tolerance levels of indicators of features on at least a portion of the substrate; and The one or more regions identified on the at least a portion of the substrate are regions where the process margin distribution includes a tolerance level that is less than the threshold value.

8. The method according to claim 1, wherein: The determined field of view setting and / or sampling scheme depends on a determined minimum number of occurrences of at least one feature required to determine an index of the at least one feature.

9. The method according to claim 8, wherein: Determining the field of view setting and / or sampling scheme includes determining the number of fields of view to be increased so as to achieve the minimum number of occurrences of at least one feature.

10. The method according to claim 8, wherein: Determining the field of view settings and / or sampling scheme includes determining to reduce the number of fields of view so as to avoid using substantially more fields of view than are required to achieve a minimum number of occurrences of at least one feature.

11. The method according to claim 1, further comprising: obtaining a maximum measurement time allowed for obtaining metrology data of a feature on at least a portion of a substrate; The field of view setting and / or sampling scheme is determined depending on the obtained maximum allowable measurement time, so that the time required to obtain the measurement result according to the determined field of view setting and / or sampling scheme is less than or equal to the maximum allowable measurement time.

12. The method according to claim 1, wherein: The process margin distribution includes a value of an indicator of a feature; and The indicators are edge placement error EPE, local overlap LOVL, or local critical dimension uniformity LCDU.

13. The method according to claim 1, wherein: The inspection tool is an electron beam based inspection tool.

14. A computing system arranged to determine a sampling plan and / or field of view (FOV) setting for an inspection tool according to the method of claim 1.

15. A non-transitory computer readable medium comprising instructions that, when executed, cause a computing system to perform the method of claim 1.

Images