Method and apparatus for diffraction pattern guided source mask optimization

Abstract

A diffraction pattern guided source mask optimization (SMO) method is described. The method includes configuring a lithographic device. The method includes determining a source variable according to a diffraction pattern. The source variable region corresponds to one or more regions of the diffraction pattern of a pupil in which pupil variables are to be adjusted. The source variable region in the diffraction pattern includes a plurality of pixels in an image of a selected region of interest in the diffraction pattern. Determining the source variable region also includes binarizing the plurality of pixels in the image so that each individual pixel is included in the source variable region or excluded from the source variable region. The method includes: adjusting the pupil variable for the one or more regions of the pupil corresponding to the source variable region; and rendering a final pupil based on the adjusted pupil variable.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Application No. 62 / 815,854, filed on March 8, 2019, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present specification generally relates to improving and optimizing lithography processes. More particularly, apparatus, methods, and computer programs for diffraction pattern-guided source mask optimization are described, which are configured to increase illumination efficiency and scanner throughput. Background Art

[0004] A lithographic projection apparatus may be used, for example, in the manufacture of integrated circuits (ICs). In this case, a patterning device (e.g., a mask) may contain or provide a pattern corresponding to a single layer of the IC (a "design layout"). This pattern may be transferred to a target portion (e.g., comprising one or more dies) on a substrate (e.g., a silicon wafer) coated with a layer of radiation-sensitive material ("resist") by a variety of methods (such as irradiating the target portion through the pattern on the patterning device). Typically, a single substrate contains a plurality of adjacent target portions to which the pattern is transferred successively, one at a time, by the lithographic projection apparatus. In one type of lithographic projection apparatus, the pattern on the entire patterning device is transferred to one target portion in one operation. Such an apparatus is generally referred to as a stepper. In an alternative apparatus, generally referred to as a step-and-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 the reference direction. Different portions of the pattern on the patterning device are gradually transferred to one target portion. Typically, since the lithographic projection apparatus will have a reduction ratio M (e.g. 4) and the reduction ratios in the x- and y-directional features may be different, the speed F of the substrate movement will be 1 / M times the speed at which the projection beam scans the patterning device. More information on lithographic apparatus as described herein can be gleaned, for example, from US 6,046,792, which is incorporated herein by reference.

[0005] Before the pattern is transferred from the pattern forming device to the substrate, the substrate can undergo various processes, such as primer coating, resist coating and soft baking. After exposure, the substrate can undergo other processes ("post-exposure processes"), such as post-exposure baking (PEB), development, hard baking and measurement / detection of the transferred pattern. This array of processes is used as the basis for manufacturing a single layer of a device (e.g., IC). The substrate can then undergo various processes, such as etching, ion implantation (doping), metallization, oxidation, chemical-mechanical polishing, etc., which are intended to refine a single layer of the device. If multiple layers are required in the device, the entire process or its variant is repeated for each layer. Ultimately, there will be a device in each target portion on the substrate. These devices are then separated from each other by techniques such as dicing or sawing, whereby each device can be mounted on a carrier, connected to a pin, etc.

[0006] Therefore, manufacturing devices such as semiconductor devices generally involves using multiple manufacturing procedures to process a substrate (e.g., a semiconductor wafer) to form various features and multiple layers of the device. These layers and features are generally manufactured and processed using, for example, deposition, lithography, etching, chemical mechanical polishing, and ion implantation. Multiple devices can be made on multiple dies on a substrate and then separated into individual devices. The device manufacturing process can be considered a patterning process. The patterning process involves performing a patterning step using a pattern forming device in a lithography device, such as optical and / or nanoimprint lithography, to transfer the pattern on the pattern forming device to the substrate, and the patterning process generally but optionally involves one or more related pattern processing steps, such as resist development by a developer, baking the substrate using a baking tool, etching using a pattern using an etching device, etc.

[0007] As mentioned, photolithography is a central step in the fabrication of devices such as ICs, where patterns formed on a substrate define the functional elements of the device, such as microprocessors, memory chips, etc. Similar photolithography techniques are also used to form flat panel displays, microelectromechanical systems (MEMS), and other devices.

[0008] 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 often referred to as "Moore's Law." Under current technology, the layers of a device are fabricated using a lithographic projection apparatus that projects a design layout onto a substrate using illumination from a deep ultraviolet illumination source, thereby producing individual functional elements with dimensions well below 100 nm, i.e., less than half the wavelength of the radiation from the illumination source (e.g., a 193 nm illumination source).

[0009] This process of printing features with dimensions smaller than the classical resolution limit of a 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 a pattern on a substrate that resembles the shape and dimensions planned by the designer in order to achieve specific electrical functionality and performance. To overcome these difficulties, complex fine-tuning steps are applied to the lithographic projection apparatus, the design layout, or the patterning device. These steps include, for example, but are not limited to, optimization of NA and optical coherence settings, customized illumination schemes, use of phase-shifting patterning devices, optical proximity correction (OPC, sometimes also referred to as "optical and process correction") in the design layout, or other methods often defined as "resolution enhancement techniques" (RET). The term "projection optics" as used in the present invention should be broadly interpreted to cover various types of optical systems, including, for example, refractive optics, reflective optics, apertures, and reflective-refractive optics. The term "projection optics" may also include components that operate according to any of these design types for collectively or individually directing, shaping, or controlling a projection radiation beam. The term "projection optics" may include any optical component in a lithographic projection apparatus, regardless of where the optical component is located on the optical path of the lithographic projection apparatus. Projection optics may include optical components for shaping, adjusting, and / or projecting radiation from a source before the radiation passes through a patterning device, and / or optical components for shaping, adjusting, and / or projecting the radiation after the radiation passes through a patterning device. Projection optics typically exclude a source and a patterning device. Summary of the invention

[0010] According to an embodiment, a method for configuring a lithographic apparatus is provided, the method comprising determining a source variable region from a diffraction pattern, the source variable region corresponding to one or more regions of the diffraction pattern of a pupil in which a pupil variable is to be adjusted. The method comprises adjusting the pupil variable for the one or more regions of the pupil corresponding to the source variable region. The method comprises rendering a final pupil based on the adjusted pupil variable.

[0011] In an embodiment, the method further comprises determining the diffraction pattern and the pupil based on a pattern of a source mask design and an initial pupil.

[0012] In an embodiment, determining the source variable region for the diffraction pattern comprises selecting a region of interest in the diffraction pattern having different but overlapping diffraction orders.

[0013] In an embodiment, the source variable region in the diffraction pattern includes a plurality of pixels in an image of a selected region of interest in the diffraction pattern. Determining the source variable region also includes binarizing the plurality of pixels in the image so that each individual pixel is included in or excluded from the source variable region.

[0014] In an embodiment, the binarization is performed using a sigmoid or hyperbolic binarization function, or any other suitable function, or by sequentially applying the sigmoid and hyperbolic binarization functions (or any other suitable function).

[0015] In an embodiment, the source variable region includes one or more source variable regions.

[0016] In an embodiment, non-zero intensity pixels are included in the one or more source variable regions and zero intensity pixels are excluded from the one or more source variable regions.

[0017] In an embodiment, determining the one or more source variable regions further comprises dilating a shape formed by the individual pixels included in the one or more source variable regions.

[0018] In an embodiment, binarization and dilation transform the diffraction pattern such that the one or more source variable regions satisfy a minimum pupil filling ratio value.

[0019] In an embodiment, the diffraction pattern is determined using a full rigorous mask three-dimensional (M3D) or compact M3D effect or a thin mask model.

[0020] In an embodiment, the method further comprises determining one or more additional diffraction patterns based on the source, the design target, and the corresponding additional patterns of the optimized mask. One or more source variable regions are also determined for the diffraction pattern and the one or more additional diffraction patterns.

[0021] In an embodiment, the source variable region in the diffraction pattern includes a plurality of pixels in an image of a selected region of interest in the diffraction pattern. Individual pixels in the plurality of pixels have a given intensity. In an embodiment, the method further comprises adjusting a diffraction pattern amplitude constrained variable to optimize a diffraction pattern amplitude degree variable. Adjusting the diffraction pattern amplitude constrained variable to optimize the diffraction pattern amplitude degree variable causes pixels having a given intensity between a lower amplitude threshold and an upper amplitude threshold to change.

[0022] In an embodiment, adjusting the pupil variable comprises a pixel-based free-form, parameter-based source optimization.

[0023] In an embodiment, the method also includes determining a source variable region constrained by the diffraction pattern, adjusting the pupil variable in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variable in the source variable region constrained by the diffraction pattern to improve illumination efficiency and increase throughput in a source mask optimization (SMO) process.

[0024] In an embodiment, a source variable region constrained by the diffraction pattern is determined, the pupil variable in the source variable region constrained by the diffraction pattern is adjusted, and the final pupil is rendered based on the adjusted pupil variable in the source variable region constrained by the diffraction pattern to optimize the pupil for generating a spatial image.

[0025] In an embodiment, a source variable region constrained by the diffraction pattern is determined, the pupil variable in the source variable region constrained by the diffraction pattern is adjusted, and the final pupil is rendered based on the adjusted pupil variable in the source variable region constrained by the diffraction pattern to optimize the pupil for generating a resist image.

[0026] In an embodiment, at any numerical aperture and / or wavelength, the following are performed: determining a source variable region constrained by the diffraction pattern, adjusting the pupil variable in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variable in the source variable region constrained by the diffraction pattern.

[0027] In an embodiment, determining a source variable region constrained by the diffraction pattern, adjusting the pupil variables in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variables in the source variable region constrained by the diffraction pattern are performed as part of a memory chip, a logic chip, and / or a microprocessor chip manufacturing process.

[0028] In an embodiment, determining a source variable region constrained by the diffraction pattern, adjusting the pupil variables in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variables in the source variable region constrained by the diffraction pattern are performed as part of a source optimization process utilizing a fixed mask.

[0029] In an embodiment, determining a source variable region constrained by the diffraction pattern, adjusting the pupil variable in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variable in the source variable region constrained by the diffraction pattern are performed so that performance parameters are maintained at an acceptable level.

[0030] In an embodiment, the performance parameter is edge placement error (EPE), image log slope (ILS), normalized image log slope (NILS), random edge placement error (SEPE), and / or depth of focus (DOF).

[0031] In an embodiment, the method further comprises generating machine readable instructions which may be used to change a source and / or a mask associated with the lithographic apparatus based on the rendered final pupil.

[0032] According to another embodiment, a computer program product is provided, the computer program product comprising a non-transitory computer-readable medium having instructions recorded thereon, the instructions implementing the method described above when executed by a computer. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A block diagram showing the various subsystems of a lithography system.

[0034] Figure 2 is a flow chart of a method for determining a patterning device pattern or a target pattern to be printed on a substrate according to an embodiment.

[0035] Figure 3A The figures illustrate aspects of the diffraction pattern guided source mask optimization method according to an embodiment.

[0036] Figure 3B The figures illustrate additional aspects of the diffraction pattern guided source mask optimization method according to an embodiment.

[0037] Figure 4 The figure illustrates dilation of pixels included in a source variable region according to an embodiment, wherein the dilation includes a shape fitting operation and a shape transformation operation.

[0038] Figure 5 The diagram illustrates rendering a final pupil according to an embodiment, including determining a final discrete source (pupil) generated from a final free-form source (pupil).

[0039] Figure 6 The diagram illustrates how the operation of the present method according to an embodiment may be incorporated into a larger diffraction pattern guided source mask optimization flow.

[0040] Figure 7 is a block diagram of an exemplary computer system according to an embodiment.

[0041] Figure 8 is a schematic diagram of a lithographic projection apparatus according to an embodiment.

[0042] Fig. 9 is a schematic diagram of another lithography projection apparatus according to an embodiment.

[0043] Fig.10 According to the embodiment Fig. 9 A more detailed view of the devices in .

[0044] Fig.11 According to the embodiment Fig. 9 and Fig.10 A more detailed view of the source collector module SO of the device. DETAILED DESCRIPTION

[0045] Illumination efficiency, dose, and / or other parameters affect scanner throughput for imaging operations and / or other patterning processes related to extreme ultraviolet (EUV). Illumination efficiency is related to the total number of illuminator mirrors (e.g., where a value of 1 indicates an "on" mirror state and 0 indicates an "off" mirror state). Illumination efficiency is or is related to the ratio of mirrors that direct light to the reticle to all available mirrors in the illuminator.

[0046] Lower illumination efficiency is associated with lower scanner throughput. For example, scanner throughput is measured in wafers / hour. As such, the illumination efficiency is an important factor to consider during source mask optimization (SMO) and / or other operations. If the illumination efficiency can be increased, scanner throughput can also be increased, and the overall manufacturing turnaround time (TAT) and / or cost for manufacturing wafers can be reduced.

[0047] The continuous scaling of dynamic random access memory storage node half pitch (DRAMSN HP) has resulted in smaller diffraction pattern overlap during lithography operations. Due to this smaller diffraction pattern overlap and / or other factors, typical diffraction pattern shaping pupils and column rendering pupils exhibit low illumination efficiency, which reduces scanner throughput.

[0048] A new diffraction pattern guided SMO method is described herein. Currently, there is no such method, let alone a diffraction pattern guided SMO method configured to increase illumination efficiency and increase scanner throughput. The present device and method improve the existing free-form pupil generation method for SMO. The present device and method can facilitate the improved parameter source optimization for the pupil of leaf shape and / or ellipse, dipole and / or sextupole shape and / or other shape. As described below, the present device and method use a diffraction pattern to define the area and / or pupil and part of the source variable (e.g., the pixel in the image of the diffraction pattern). The present device and method are configured so that the pupil variable corresponding to the area of ​​the diffraction pattern in the source variable area can be adjusted (e.g., optimized), and the pupil variable corresponding to the area of ​​the diffraction pattern located outside the source variable area is not considered during adjustment (e.g., optimization). Optimization boundary conditions can be established for the source variable area, and used to perform free-form source mask optimization and / or other operations. This is a new diffraction pattern guided SMO method that can maintain typical edge placement error (EPE), image log slope (ILS), normalized image log slope (NILS), random edge placement error (SEPE), depth of focus (DOF) and / or other values. For example, this method is applicable to aerial images and resist models. This method is applicable to any numerical aperture (NA) and / or wavelength. This method is applicable to any chip design (e.g., memory chip, logic chip, microprocessor chip and / or other chip).

[0049] Although specific reference may be made herein to the manufacture of ICs, it should be understood that the description herein has many other possible applications. For example, the description herein 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. Those skilled in the art 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.

[0050] In this document, the terms "radiation" and "beam" are used to cover EUV (extreme ultraviolet radiation, e.g., having a wavelength in the range of about 3 nm to 100 nm) radiation. However, other applications of the present method are contemplated with other types of electromagnetic radiation, including ultraviolet radiation (e.g., having a wavelength of 365 nm, 248 nm, 193 nm, 157 nm, or 126 nm).

[0051] The pattern forming device may include or may form one or more design layouts. The design layout may be generated using a CAD (i.e., computer-aided design) process. This process is often referred to as EDA (i.e., electronic design automation). Most CAD programs follow a set of predetermined design rules to generate a functional design layout / pattern forming device. These rules are set based on processing and design constraints. For example, the design rules define the spatial tolerance between devices (such as gates, capacitors, etc.) or interconnects to ensure that the devices or lines do not interact with each other in an undesirable manner. One or more of the restrictions in the design rule restrictions may be referred to as a "critical dimension" (CD). The critical dimension of a device may be defined as the minimum width of a line or hole, or the minimum space / gap between two lines or two holes. Thus, CD regulates the total size and density of the designed device. One of the goals in device manufacturing is to faithfully reproduce the original design intent (via a pattern forming device) on a substrate.

[0052] The terms "reticle", "mask" or "patterning device" as used in the present invention 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 the 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-shifted, hybrid, etc.), other examples of such patterning devices include programmable mirror arrays. An example of such a device is a matrix-addressable surface with a viscoelastic control layer and a reflective surface. The basic principle underlying such an apparatus is, for example, that the addressed areas of the reflective surface reflect the incident radiation as diffracted radiation, while the non-addressed areas reflect the incident radiation 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 becomes patterned according to the addressing pattern of the matrix-addressable surface. Suitable electronics can be used to perform the desired matrix addressing. Other examples of such patterning devices also include programmable LCD arrays. An example of such a configuration is given in US Pat. No. 5,229,872, which is incorporated herein by reference.

[0053] As an introduction, Figure 1An exemplary lithographic projection apparatus 10A is illustrated. The main components are a radiation source 12A, which may be an extreme ultraviolet (EUV) source or another type of source (as discussed above, the lithographic projection apparatus need not have a radiation source itself); illumination optics, which, for example, define partial coherence (expressed as a standard deviation) and may include optics 14A, 16Aa, and 16Ab that shape the radiation from source 12A; a patterning device 18A; and transmission optics 16Ac that projects an image of the patterning device pattern onto a substrate plane 22A.

[0054] The pupil 20A may be included in the transmission optics 16Ac. In some embodiments, one or more pupils may be present before and / or after the mask 18A. As described in further detail herein, the pupil 20A may provide patterning of the light that ultimately reaches the substrate plane 22A. An adjustable filter or aperture at the pupil plane of the projection optics may limit the range of beam angles incident on the substrate plane 22A, where the maximum possible angle defines the numerical aperture NA=n sin(θ) of the projection optics. max ), where n is the refractive index of the medium between the substrate and the final element of the projection optics, θ max is the maximum angle of the beam emerging from the projection optics that can still impinge on the substrate plane 22A.

[0055] In a lithographic projection apparatus, a source providing illumination (i.e., radiation) to a pattern forming device and projection optics directs and shapes the illumination onto a substrate via the pattern forming device. The projection optics 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 model may be used to calculate a resist image based on the aerial image, and an example of this situation may be found in U.S. Patent Application Publication No. US2009-0157630, the entire disclosure of which is hereby incorporated by reference. The resist model is only related to the properties of the resist layer (e.g., the effects of chemical processes occurring during exposure, post-exposure baking (PEB), and development). The optical properties of the lithographic projection apparatus (e.g., the properties of the illumination, pattern forming device, and projection optics) specify the aerial image and may be defined in an optical model. Since the patterning device used in a lithographic projection apparatus can be varied, it is desirable to decouple 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 the projection optics. Details of techniques and models for transforming a design layout into various lithographic images (e.g., aerial images, resist images, etc.), using which OPC is applied and performance (e.g., in terms of process windows) is evaluated are described in U.S. Patent Application Publication Nos. US 2008-0301620, 2007-0050749, 2007-0031745, 2008-0309897, 2010-0162197, and 2010-0180251, the disclosure of each of which is hereby incorporated by reference in its entirety.

[0056] One aspect of understanding the lithographic process is understanding the interaction of the radiation with the patterning device. The electromagnetic field of the radiation after it passes through the patterning device can be determined based on the electromagnetic field of the radiation before it reaches the patterning device and a function that characterizes the interaction. Such a function can be called a mask transmission function (which can be used to describe the interaction performed by the transmission patterning device and / or the reflection patterning device).

[0057] The mask transmission function can have a variety of different forms. One form is binary. A binary mask transmission function has any of two values ​​(e.g., zero and a positive constant) at any given location on the pattern forming device. A mask transmission function in binary form can be referred to as a binary mask. Another form is continuous. That is, the modulus of the transmittance (or reflectance) of the pattern forming device is a continuous function of the location on the pattern forming device. The phase of the transmittance (or reflectance) can also be a continuous function of the location on the pattern forming device. A mask transmission function in continuous form can be referred to as a continuous tone mask or a continuous transmission mask (CTM). For example, a CTM can be represented as a pixelated image, where each pixel can be assigned a value between 0 and 1 (e.g., 0.1, 0.2, 0.3, etc.) instead of a binary value of 0 or 1. In an embodiment, the CTM may be a pixelated grayscale image where each pixel has multiple values ​​(eg, values ​​in the range [-255, 255], normalized values ​​in the range [0, 1] or [-1, 1] or other suitable range).

[0058] The thin mask approximation, also known as the Kirchhoff boundary condition, is widely used to simplify the determination of the interaction of the radiation with the patterning device. The thin mask approximation assumes that the thickness of the structures on the patterning device is very small compared to the wavelength, and the width of the structures on the mask is very large compared to the wavelength. Therefore, the thin mask approximation assumes that the electromagnetic field after the patterning device is the product of the incident electromagnetic field and the mask transmission function. However, as the lithography process uses radiation with shorter and shorter wavelengths, and the structures on the patterning device become smaller and smaller, the assumption of the thin mask approximation breaks down. For example, due to the finite thickness of the structure (e.g., the edge between the top surface and the sidewall), the interaction of the radiation with the structure ("mask 3D effect" or "M3D") may become significant. Including this scattering in the mask transmission function can enable the mask transmission function to preferably capture the interaction of the radiation with the patterning device. The mask transmission function under the thin mask approximation can be referred to as a thin mask transmission function. A mask transmission function covering M3D may be referred to as an M3D mask transmission function.

[0059] Figure 2 2 is a flow chart of a method 200 for determining a patterning device pattern (or mask pattern hereinafter) from an image (e.g., a continuous transmission mask image, a binary mask image, a curvilinear mask image, etc.), the image corresponding to a target pattern to be printed onto a substrate via a patterning process involving a photolithography process. In embodiments, the design layout or the target pattern may be a binary design layout, a continuous tone design layout, or a design layout having another suitable form.

[0060] Method 200 is an iterative process in which an initial image (e.g., an enhanced image, mask variables initialized from a CTM image, etc.) is progressively modified to produce different types of images according to different processes of the present disclosure to ultimately produce information including a mask pattern or image that is further used to make / manufacture a mask (e.g., mask variables corresponding to a final curvilinear mask). The iterative modification of the initial image may be based on a cost function, wherein during iterations, the initial image may be modified such that the cost function is reduced, and in an embodiment minimized. In an embodiment, the method 200 may also be referred to as a binarized CTM process, wherein the initial image is an optimized CTM image, and the optimized CTM image is further processed according to the present disclosure to produce a curvilinear mask pattern (e.g., a geometric shape or polygonal representation of a curvilinear pattern or curvilinear mask). In an embodiment, the initial image may be an enhanced image of a CTM image. The curvilinear mask pattern may be in the form of a vector, a table, a mathematical equation, or other form representing a geometric / polygonal shape.

[0061] In an embodiment, process P201 may involve obtaining an initial image (e.g., a CTM image or an optimized CTM image, or a binary mask image). In an embodiment, the initial image 201 may be a CTM image generated by a CTM generation process based on a target pattern to be printed on a substrate. The CTM image may then be received by the process P201. In an embodiment, the process P201 may be configured to generate a CTM image. For example, in CTM generation technology, inverse lithography, i.e., inversion lithography problem, is reduced to an optimization problem. The variables involve the values ​​of pixels in the mask image, and lithography indicators or metrics such as EPE or sidelobe printing may be used as a cost function. In an iteration of optimization, the mask image is constructed according to the variables, and then a process model (e.g., a Tachyon model) is applied to obtain an optical or resist image and a cost function is calculated. The cost calculation then gives a gradient value, which is used to optimize the solver to update variables (e.g., pixel intensity). After several iterations during optimization, a final mask image is generated, which is further used as a guide map for pattern extraction (e.g., as implemented in Tachyon SMO software). Such an initial image (e.g., a CTM image) may include one or more features corresponding to the target pattern to be printed onto the substrate via the patterning process (e.g., features of the target pattern, SRIF, wiring, etc.).

[0062] In an embodiment, the CTM image (or an enhanced version of a CTM image) may be used to initialize the mask variables, which may be used as the initial image 201, which is modified in an iterative manner as discussed below.

[0063] The process P201 may involve generating an enhanced image 202 based on the initial image 201. The enhanced image 202 may be an image in which certain selected pixels within the initial image 201 are amplified. The selected pixels may be pixels with relatively low values ​​(or weak signals) within the initial image 201. In an embodiment, the selected pixels are pixels with signal values ​​lower than, for example, the average intensity of pixels throughout the entire initial image or a given threshold. In other words, pixels with weaker signals within the initial image 201 are amplified, thereby enhancing one or more features within the initial image 201. For example, a second-order SRAF around a target feature may have a weak signal that may be amplified. Thus, the enhanced image 202 may highlight or identify additional features (or structures) that may be included in a mask image (generated later in the method). In conventional methods for determining a mask image (e.g., a CTM method), weak signals within the initial image may be ignored, and thus the mask image may not include features that may be formed by weak signals in the initial image 201.

[0064] The generation of the enhanced image 202 involves applying an image processing operation such as a filter (e.g., an edge detection filter) to amplify weak signals within the initial image 201. Alternatively or in addition, the image processing operation may be deblurring, averaging, and / or feature extraction or other similar operations. Examples of edge detection filters include Prewitt operators, Laplasian operators, Laplacian of Gaussian (LoG) filters, etc. The generation step may also involve combining the amplified signal of the initial image 201 with the original image of the initial image 201 with or without modifying the original strong signal of the initial image 201. For example, in an embodiment, for one or more pixel values ​​at one or more locations across the initial image 201 (e.g., at a contact hole), the original signal may be relatively strong (e.g., above a threshold such as 150 or below -50), then the original signal at the one or more locations (e.g., at the contact hole) may not be modified or combined with the amplified signal for that location.

[0065] In an embodiment, noise (e.g., random variations in brightness or color or pixel values) in the initial image 201 may also be amplified. Therefore, alternatively or additionally, a smoothing process may be applied to reduce noise (e.g., random variations in brightness or color or pixel values) in the combined image. Examples of image smoothing methods include Gaussian blur, moving average, low-pass filter, etc.

[0066] In an embodiment, an enhanced image 202 may be generated using an edge detection filter. For example, an edge detection filter may be applied to the initial image 201 to generate a filtered image that highlights the edges of one or more features within the initial image 201. The resulting filtered image may be further combined with the original image (i.e., the initial image 201) to generate the enhanced image 202. In an embodiment, the combination of the initial image 201 and the image obtained after edge filtering may involve modifying only those parts of the initial image 201 with weak signals without modifying areas with strong signals, and the combination process may be weighted based on signal strength. In an embodiment, amplifying the weak signals may also amplify the noise within the filtered image. Therefore, according to an embodiment, a smoothing process may be performed on the combined image. Smoothing of an image may refer to an approximation function, i.e., an approximation function, which attempts to capture important patterns in an image (e.g., target patterns, SRAFs) while omitting noise or other fine-scale structures / fast phenomena. In smoothing, the data points of the signal may be modified so that individual points (generally due to noise) may be reduced, and points that may be lower than adjacent points may be increased, resulting in a smoother signal or a smoother image. Thus, after the smoothing operation, a further smoothed version of the enhanced image 202 with reduced noise may be obtained according to an embodiment of the present disclosure.

[0067] The method may involve, in process P203, generating mask variables 203 based on the enhanced image 202. In a first iteration, the enhanced image 202 may be used to initialize the mask variables 203. In subsequent iterations, the mask variables 203 may be updated in an iterative manner.

[0068] The contour extraction of a real-valued function f of n real variables is a set of the following form:

[0069] L c (f) = {(x1, x2, … x n )|f(x1,x2,…x n )=c}

[0070] In two-dimensional space, the set defines the points on the surface where the function f is equal to a given value c. In two-dimensional space, the function f is able to extract closed contours that will be presented to the mask image.

[0071] In the above equation, x1,x2,…x n Refers to a mask variable, such as the intensity of an individual pixel, that determines where a curved mask edge exists at a given constant value c (eg, a critical plane as discussed below in process P205).

[0072] In an embodiment, at an iteration, the generation of the mask variable 203 may involve modifying one or more values ​​of a variable (e.g., pixel values ​​at one or more locations) within the enhanced image 202 based on, for example, initialization conditions or a gradient map (which may be generated later in the method). For example, one or more pixel values ​​may be increased or decreased. In other words, the amplitude of one or more signals within the enhanced image 202 may be increased or decreased. Such modified amplitudes of signals may enable the generation of different curve patterns depending on the amount of change in the amplitude of the signal. Thus, the curve pattern develops step by step until the cost function is reduced (minimized in one embodiment). In an embodiment, further smoothing may be performed on the horizontal mask variable 203.

[0073] In addition, process P205 involves generating a curved mask pattern 205 (e.g., having a polygonal shape represented in vector form) based on the mask variables 203. The generation of the curved mask pattern 205 may involve thresholding of the mask variables 203 to draw or generate a curved (or curved) pattern according to the mask variables 203. For example, thresholding may be performed using a critical plane (e.g., an xy plane) having a fixed value, the critical plane intersecting the signal of the mask variables 203. The intersection of the critical plane with the signal of the mask variables 203 generates a trace or contour (i.e., a curved polygonal shape), the trace or contour forming a polygonal shape that serves as a curved pattern for the curved mask pattern 205. For example, the mask variables 203 may intersect with a zero plane parallel to the (x, y) plane. Thus, the curved mask pattern 205 may be any curved pattern generated as above. In an embodiment, the curved pattern drawn or generated according to the mask variables 203 depends on the signal of the enhanced image 202. As such, the image enhancement process P203 results in an improvement in the pattern produced for the final curvilinear mask pattern. The final curvilinear mask pattern may also be used by a mask manufacturer to fabricate a mask for use in a lithography process.

[0074] Process P207 may involve rendering the curved mask pattern 205 to generate a mask image 207. Rendering is an operation performed on the curved mask pattern, which is a process similar to converting a rectangular mask polygon into a discrete grayscale image representation. This process can generally be understood as sampling a box function or box function of continuous coordinates (polygons) into values ​​at each point of an image pixel.

[0075] The method also involves a look-ahead or forward simulation of a patterning process using a process model that generates or predicts a pattern 209 that can be printed on a substrate based on the mask image 207. For example, process P209 can involve executing and / or simulating the process model using the mask image 207 as an input, and generating a process image 209 (e.g., an aerial image, a resist image, an etch image, etc.) on a substrate. In an embodiment, the process model can include a mask transmission model coupled to an optics model, which is further coupled to a resist model and / or an etch model. The output of the process model can be a process image 209 that factors in different process variations during the simulation process. The process image can be further used to determine parameters of the patterning process (e.g., EPE, CD, overlap, side lobes, etc.) by, for example, outlining a pattern within the process image. The parameters can also be used to define a cost function, which is further used to optimize the mask image 207 so that the cost function is reduced, or minimized in an embodiment.

[0076] In process P211, a cost function may be evaluated based on a process model image 209 (also referred to as a simulated substrate image or substrate image or wafer image). Thus, the cost function may be considered process-aware at locations where the patterning process varies, thereby enabling the generation of a curved mask pattern to be considered. For example, the cost function may be an edge placement error (EPE), a side lobe, a mean square error (MSE), a pattern placement error (PPE), a normalized image logarithmic slope (NILS), or other appropriate variables defined based on the contour of the pattern in the process image. The EPE may be an edge placement error associated with one or more patterns and / or the sum of all edge placement errors associated with all patterns of the process model image 209 and the corresponding target pattern. In an embodiment, the cost function may include more than one condition that may be reduced or minimized simultaneously. For example, in addition to the probability of MRC violation, the number of defects, EPE, overlap, CD, or other parameters may be included, and all conditions may be reduced (or minimized) simultaneously.

[0077] Furthermore, one or more gradient maps may be generated based on the cost function (eg, EPE), and mask variables may be modified based on such gradient maps. Therefore, the gradient calculation can be expressed as Furthermore, by capturing the inverse mathematical relationship from the mask image (MI) to the curvilinear mask polygon to the mask variables, the gradient values ​​are updated. Thus, from the mask image to the curvilinear mask polygon, and from the curvilinear mask polygon to the mask variables, a chain of derivatives of the cost function with respect to the mask image can be calculated, which allows modification of the mask variable values ​​at the mask variables.

[0078] In an embodiment, image regularization may be added to reduce the complexity of the mask pattern that may be produced. Such image regularization may be a mask rule check (MRC). MRC refers to the constraints of a mask manufacturing process or device. Thus, the cost function may include different components (e.g., based on EPE and MRC violation penalties). The penalty may be a term of the cost function that depends on the amount of violation, such as the difference between a mask measurement and a given MRC or mask parameter (e.g., a mask pattern width and an allowed (e.g., minimum or maximum) mask pattern width). Thus, according to an embodiment of the present disclosure, a mask pattern may be designed, and the corresponding mask may be made not only based on a forward simulation or forward modeling of the patterning process, but also based on manufacturing constraints of a mask manufacturing device / process. Thus, a curve mask that can be manufactured with high yield (i.e., minimum defects) and high accuracy based on, for example, EPE or overlap on a printed pattern may be obtained.

[0079] The pattern corresponding to the process image should be exactly or exactly the same as the target pattern, however, such an accurate target pattern may not be feasible (e.g., typically sharp corners), and some conflicts are introduced due to variations in the patterning process itself and / or approximations in the model of the patterning process. In the first iteration of the method, the mask image 207 may not produce a pattern (in the resist image) that is similar to the target pattern. The determination of the accuracy or acceptance of the printed pattern in the resist image (or etching image) can be based on a cost function such as EPE. For example, if the EPE of the resist pattern is high, it indicates that the printed pattern using the mask image 207 is unacceptable and the pattern in the mask variable 203 must be modified.

[0080] To determine whether the mask image 207 is acceptable, process P213 may involve determining whether the cost function is reduced or minimized, or whether a given number of iterations has been reached. For example, the EPE value of the previous iteration may be compared with the EPE value of the current iteration to determine whether the EPE has been reduced, minimized, or converged (i.e., no substantial improvement in the printed pattern is observed). When the cost function is minimized, the method may stop, and the resulting curve mask pattern information is considered to be the optimized result.

[0081] However, if the cost function is not reduced or minimized, the mask-related variables or enhanced image-related variables (e.g., pixel values) may be updated. In an embodiment, the update may be based on a gradient-based approach. For example, if the cost function is not reduced, the method 200 continues to the next iteration of generating the mask image after performing processes P215 and P217, which indicates how to further modify the mask variables 203.

[0082] Process P215 may involve generating a gradient map 215 based on the cost function. The gradient map may be a derivative and / or partial derivative of the cost function. In an embodiment, the partial derivative of the cost function may be determined using the corresponding pixels of the mask image, and the derivatives may also be linked to determine the partial derivatives with respect to the mask variable 203. Such gradient calculations may involve determining the reciprocal relationship between the mask image 207 and the mask variable 203. In addition, the inverse relationship of any smoothing operation (or function) performed in processes P205 and P203 must be considered.

[0083] The gradient map 215 may provide recommendations for increasing or decreasing the value of the mask variable so that the value of the cost function is reduced (minimized in one embodiment). In an embodiment, an optimization algorithm may be applied to the gradient map 215 to determine the mask variable value. In an embodiment, an optimization solver may be used to perform gradient-based calculations (in process P217).

[0084] In an embodiment, for iterations, mask variables may be changed while the critical plane may be kept fixed or unchanged so as to gradually reduce or minimize the cost function. Thus, the resulting curve pattern may be progressively developed during iterations so that the cost function is reduced, or minimized in one embodiment. In another embodiment, both mask variables and critical planes may be changed to achieve faster convergence of the optimization process. A final set of binarized CTM results (i.e., a modified version of an enhanced image, mask image, or curve mask) may result after several iterations and / or minimizations of the cost function.

[0085] In an embodiment of the present disclosure, the transition from CTM optimization performed using a grayscale image to a binarized CTM optimization performed using a curved mask can be simplified by replacing the threshold setting process (i.e., P203 and P205) by a different process, where a sigmoid transform is applied to the enhanced image 202 and a corresponding change in the gradient calculation is performed. The sigmoid transform of the enhanced image 202 produces a transformed image that is gradually developed into a curved pattern during the optimization process (e.g., minimizing the cost function). During the iteration or optimization step, variables associated with the sigmoid function (e.g., steepness and / or threshold) can be modified based on the gradient calculation. Since the sigmoid transform becomes steeper during successive iterations (e.g., an increase in the steepness of the slope of the sigmoid transform), a gradual transition from the CTM image to the final binarized CTM image can be achieved, thereby allowing an improved result of the final binarized CTM optimization using a curved mask pattern to be achieved.

[0086] In embodiments of the present disclosure, additional steps / processes may be inserted into the iterative loop of the optimization to enhance the result to have selected or desired properties. For example, smoothness may be ensured by adding a smoothing step, or other filters may be used to enhance the image to favor horizontal / vertical structure.

[0087] The present method has several features or aspects. For example, an optimized CTM mask image realized using an image enhancement method is used to improve the signal, which can also be used as a seeding in an optimization process. In another aspect, the generation of a curve mask pattern can be enabled using a threshold setting method (called a binary CTM) utilizing CTM technology. In another aspect, the complete formula (i.e., a closed-loop formula) for gradient calculation also allows the use of a gradient-based solver for mask variable optimization. The binary CTM result can be used as a local solution (as a hotspot repair) or as a full chip solution. The binary CTM result can be used as an input together with machine learning. This situation can allow the use of machine learning to accelerate the binary CTM. In another aspect, the method includes an image regularization method to improve the result. In another aspect, the method involves a continuous optimization stage to achieve a smoother transition from a grayscale image CTM to a binary curve mask binary CTM. The method includes additional transformations to the optimized iteration to strengthen the good properties of the result (requiring smoothness in the binary CTM image).

[0088] As lithography nodes keep shrinking, more and more complex masks are needed. The method can be used in key layers with EUV scanners and / or other scanners. The method according to the present disclosure can be included in different aspects of the mask optimization process, including source mask optimization (SMO), mask optimization, and / or OPC.

[0089] For example, a prior art source mask optimization process is described in U.S. Patent No. 9,588,438, entitled “Optimization Flows of Source, Mask and Projection Optics,” the entire contents of which are incorporated by reference. Source mask optimization can take into account imaging variations across multiple locations of a mask design layout. The design layout can include one or more of the following: an entire design layout, a fragment, or one or more key features of the design layout, and / or other layouts. For example, the design layout can be a set of fragments selected by a pattern selection method based on diffraction signature analysis or any other method. Alternatively, a full-chip simulation can be performed, “hot spots” and / or “warm spots” can be identified from the full-chip simulation, and then a pattern selection step is performed.

[0090] Simulated lithography for a lithographic projection apparatus may utilize a source model representing the optical properties of the source (including light intensity distribution and / or phase distribution), representing the optical properties of the projection optics (in some embodiments, the source model and the projection optics model may be combined into a transfer cross coefficient or cross transfer function (TCC) model) (including changes in light intensity distribution and / or phase distribution caused by the projection optics), representing the optical properties of a design layout (which is a representation of the arrangement of features on a mask) (including changes in light intensity distribution and / or phase distribution caused by a given design layout), and / or other models. An aerial image is simulated based on the transfer cross coefficient and the design layout model. A resist model may be used to simulate a resist image based on the aerial image. The simulation of lithography may, for example, predict the contour and CD in the resist image.

[0091] In an embodiment, the source model may represent the optical properties of the source, including but not limited to numerical aperture-mean square deviation or standard deviation (σ) settings and any specific illumination source shape (e.g., off-axis light sources such as annular, quadrupole, and dipole, etc.). The projection optics model may represent the optical properties of the projection optics, including aberrations, distortions, refractive indices, physical sizes, physical dimensions, etc. The design layout model may also represent the physical properties of a physical mask, such as described in U.S. Patent No. 7,587,704, the entire contents of which are incorporated by reference. The purpose of the simulation is to accurately predict, for example, edge placement and CD, which can then be compared to the intended design. The intended design is typically defined as a pre-OPC design layout that can be provided in a standardized digital file format such as GDSII or OASIS or another file format.

[0092] refer to Figure 3A and Figure 3B As described above, the apparatus and method use a diffraction pattern to define the location and / or region of a pupil, and a source variable (e.g., a pixel in an image of the diffraction pattern). The apparatus and method are configured such that a pupil variable corresponding to a region of the diffraction pattern in the source variable region can be adjusted (e.g., optimized), and a pupil variable corresponding to a region of the diffraction pattern outside the source variable region is not considered during adjustment (e.g., optimization). Pupil variables may include pupil shape, the position and / or angle of one or more pupil (or pupil-related) components (e.g., a reflector), and / or other variables. Pupil variables may be represented as locations and / or intensities of individual pixels. For example, the system may continuously change pixel intensities in different pixel locations. Adjusting pupil variables may change, for example, the intensity of one or more pixels in an image of a diffraction pattern, and / or have other effects. Optimization boundary conditions may be established for the source variable region and used to perform freeform source mask optimization and / or other operations. Figure 3A and Figure 3B Several aspects of this approach are outlined.

[0093] The method is a method for configuring a lithographic apparatus. The method is a method for diffraction pattern guided source mask optimization, which is configured to increase scanner throughput and illumination efficiency for a lithographic process. Figure 3A and Figure 3B As shown in FIG. 3 , method 300 includes designing a source mask 304 ( Figure 3A ) pattern, initial pupil 306 ( Figure 3A ), and / or other information to determine 302 ( Figure 3A ) diffraction pattern and pupil. In an embodiment, the diffraction pattern is determined using a fully rigorous mask three-dimensional (M3D) or compact M3D effect or thin mask model.

[0094] refer to Figure 3A , the method 300 includes determining 310 a source variable region 311 based on a diffraction pattern 312 (e.g., which in this case is determined using a thin mask model). The source variable region 311 corresponds to one or more regions of the diffraction pattern 312 in the pupil for which pupil variables are to be adjusted. Determining the source variable region 311 for the diffraction pattern 312 includes selecting a region of interest in the diffraction pattern 312 using different but overlapping diffraction orders (e.g., 0th order and 1st order in this example). In an embodiment, the source variable region 311 in the diffraction pattern 312 includes a plurality of pixels in an image 313 of the selected region of interest in the diffraction pattern 312.

[0095] Determining the source variable region 311 further includes binarizing 314 a plurality of pixels in the image 313 so that individual pixels are included in the source variable region 311 or excluded from the source variable region 311. Figure 3A , an example of performing binarization for only four beam areas 316 and for combined three and four beam areas 318 is shown. Binarization 314 can increase the contrast between pixels, for example to facilitate dividing the pixels into two groups. For example, the two groups can be formed by pixels showing some minimum intensity level, and pixels not showing the minimum intensity level. The minimum intensity threshold is further described below.

[0096] In an embodiment, binarization 314 is performed using a sigmoid binarization function, a hyperbolic binarization function, and / or any other binarization function that allows the present apparatus and method to operate as described herein. In an embodiment, binarization 314 is performed by sequentially applying the sigmoid binarization function, the hyperbolic binarization function, and / or any other binarization function. In an embodiment, binarization 314 is configured such that non-zero intensity pixels (and / or pixels having a minimum intensity threshold level and a maximum intensity threshold level) are included in the source variable region 311, and zero intensity pixels (and / or pixels that do not break through the minimum intensity threshold level) are excluded from the source variable region 311. In examples 316 and 318, binarization 314 has been performed such that pixels in the lobe 320 are included in the source variable region, and pixels not in the lobe 320 are excluded from the source variable region.

[0097] like Figure 3B , determining 310 the source variable region 311 further comprises: dilating 350 a shape 352 formed by individual pixels included in the source variable region 311, and locking 354 the shape 352 so that the source variable region 311 becomes fixed. In an embodiment, the binarization 314 and dilation 350 transform the diffraction pattern 312 so that the source variable region 311 satisfies a minimum pupil filling ratio value. The pupil filling ratio may be or relate to the area of ​​the pupil covered by the diffraction pattern relative to the total possible pupil area. For a continuous pupil, the pupil filling ratio (PFR) is given as follows:

[0098]

[0099] where I(δ) is the source intensity profile in standard deviation σ space, and Imax is the maximum source intensity within the pupil, and PFR is the illuminated fraction of the pupil.

[0100] In an embodiment, the dilation 350 includes dilation and / or enlargement of the binarized pixels included in the source variable region 311. As an example, Figure 3BBinarized pixel 315 and enlarged pixel 317 are shown as shape 352. In an embodiment, dilation 350 includes a shape fitting operation, a shape transformation operation, and / or other operations.

[0101] For example, Figure 4 As shown, dilation 350 includes a shape fitting operation 400 and a shape transformation operation 402. Figure 4 As shown in the example of FIG. 4 (other shape fits are contemplated), the shape fitting operation 400 includes ellipse / lobe parameter fitting, where r is determined for the ellipse / lobe shape of the binarized source variable region 311. a and r b .exist Figure 4 In the example, ra = 0.049 and r b =0.0308, so that the corresponding pupil filling ratio (PFR) is 0.090. In this example, the PFR of 0.090 does not meet the minimum PFR threshold, and the dilation 350 is continued using the shape transformation operation 402. In the shape transformation operation 402, the elliptical / leaf shape is scaled (e.g., enlarged) to meet the minimum PFR threshold of 0.2 (for example). In this example, r a is scaled to 0.109 and r b is scaled to 0.31. In this example, r a and r b is scaled until the minimum PFR threshold is met. In an embodiment, the minimum pupil filling ratio (minimum PFR) is a user input specification for source mask optimization (SMO), and / or may be determined in other ways.

[0102] In response to meeting the minimum PFR threshold, shape 352 ( Figure 3B ) is locked 354( Figure 3B ), so that the source variable region 311 becomes fixed (e.g., it can be labeled as a variable "srcloc" or source variable location, for example, as described below). Locking sets a region in which the pupil variable is allowed to be adjusted (e.g., optimized). In the locked (or srcloc) region, non-zero intensity pixels are allowed to be adjusted / optimized, and locations with zero pixel intensity are forced to be dark during the adjustment / optimization process.

[0103] The method includes adjusting the pupil variable for one or more regions of the pupil corresponding to the source variable region 311. In an embodiment, the method includes adjusting the pupil variable only for one or more regions of the pupil corresponding to the source variable region and not adjusting the pupil variable for any region of the pupil outside the source variable region.

[0104] In an embodiment, the source variable region in the diffraction pattern includes a plurality of pixels in an image of a selected region of interest in the diffraction pattern. Individual pixels of the plurality of pixels have a given intensity. In an embodiment, the method further comprises adjusting a variable constrained by the diffraction pattern amplitude to optimize a diffraction pattern amplitude degree variable (e.g., where intensity = (amplitude)^2). Adjusting the variable constrained by the diffraction pattern amplitude to optimize the diffraction pattern amplitude degree variable causes pixels having a given intensity above a lower (e.g., minimum) amplitude threshold, or between a lower amplitude threshold and an upper amplitude threshold (as described above) to change. For example, a lock (or srcloc) region (defined by x and y coordinates) may be determined using an S-shaped method according to the following formula:

[0105]

[0106] Where k is a tunable slope parameter for the sigmoid function describing the steepness of the transition near the thres value, DP(x,y) is a description of the x and y location of the pixel amplitude in the diffraction pattern, and thres is a lower limit (e.g., a minimum amplitude threshold). In embodiments, the lower amplitude threshold and / or other thresholds described herein are determined when the apparatus and / or method are manufactured and / or generated, determined and / or adjusted by a user of the apparatus and method, determined based on information generated during previous operation of the method, and / or determined in other ways. In embodiments, the lower amplitude threshold and / or other thresholds described herein are tunable so that the lock zone can be adjusted by the user.

[0107] By way of non-limiting practical examples of how the operations described above may be implemented by software and / or other computer programming, the software may generate a diffraction pattern image (e.g., as described above). The diffraction pattern image may be normalized, for example, between [0,255]. An intensity histogram of binary size with a value of 1 may be determined. The sum of intensity counts for intensities between 0 and 255 may be determined, and the fill ratio may be determined according to the following formula.

[0108]

[0109] Once the extracted fill ratio is greater than or equal to the extracted fill ratio threshold, the process is stopped and the extracted source is generated (e.g., according to the above description of Figure 3A and Figure 3B The diffraction pattern 312 described is used to determine 310 the source variable region 311).

[0110] The extracted source is then expanded until the expanded extracted source satisfies a source fill ratio threshold (e.g., as described above with respect to Figure 4The expansion 350 described above is ended. A source location file (e.g., srcloc.src, locking 354 described above) can be generated, a source constraint file (e.g., srcmax.src, srcmin.src) can be generated, and an initial source file (initsrc.src) can be generated. In an embodiment, the initial source file (initsrc.src) can be considered as the 0th and 1st order diffraction pattern images multiplied by the srcloc.src file, which is then normalized to [0,1], for example.

[0111] As described above, the srcloc file sets the allowed area of ​​the pupil for free-form source optimization. Non-zero intensity pixels (and / or pixels between a minimum pixel intensity threshold and a maximum pixel intensity threshold) are allowed to be adjusted / optimized, and positions with zero pixel intensity (and / or pixels with an intensity lower than the minimum pixel intensity threshold) are forced to dim during the entire adjustment / optimization process. The srcmax file and srcmin file are source (src) files that set lower and upper limits for individual pixels in free-form source optimization. In an embodiment, adjusting the pupil variables includes pixel-based free-form, parameter-based source optimization.

[0112] The method includes rendering a final pupil based on the adjusted pupil variable. Rendering the final pupil may include determining a final discrete source (pupil) based on a final free-form source (pupil), such as Figure 5 As shown in . Figure 5 The figure shows a representation 500 of an initial source file (initsrc.src) and a representation 502 of a source lock file (srcloc.src) used to generate the initial source file. As shown in representation 502, the srcloc file sets the allowed region of the pupil for free-form source optimization 503. Non-zero intensity pixels (and / or pixels between a minimum pixel intensity threshold and a maximum pixel intensity threshold) 504 are allowed to be adjusted / optimized, and locations with zero pixel intensity (and / or pixels with an intensity below the minimum pixel intensity threshold) 506 are forced to be dark during the entire adjustment / optimization process. Figure 5 The figure shows a final free-form source (pupil) 510 (e.g., after performance of the operations described above) having a PFR of 0.298. The final free-form source (pupil) 510 is rendered 512 into a final discrete source (pupil) 514. Figure 5 In the example shown in , the final discrete source (pupil) 514 has an illumination efficiency of 0.93 (which is higher than the illumination efficiency of the previous final discrete pupil), but has a DOF (10% EL) of 105.4 and a NILS of 2.3 (which is still comparable to the same indicators used for the previous method).

[0113] In an embodiment, the method further comprises determining one or more additional diffraction patterns based on the source, the design target, and the corresponding additional pattern of the optimized mask. In an embodiment, the source variable region comprises one or more source variable regions. In an embodiment, the one or more source variable regions may be determined for the diffraction pattern and / or the one or more additional diffraction patterns. In an embodiment, the operation of the method described above may be applied to a plurality of different diffraction patterns.

[0114] For example, the method may include determining multiple diffraction patterns based on multiple corresponding patterns of a source mask design. The multiple corresponding patterns may include different fragments from the source mask design (e.g., fragment 0, fragment 1, fragment 2, etc.), and / or other patterns. The multiple diffraction patterns can be used to define the pupil, and the location and / or area of ​​the source variable (e.g., overlapping pixels in the images of different fragments of the diffraction pattern can be combined into a single image / area to form the source variable area-"single srcloc file"). The present apparatus and method are configured so that the pupil variable corresponding to the overlapping area of ​​the diffraction pattern located in the source variable area can be adjusted (e.g., optimized), and the pupil variable corresponding to the area outside the source variable area of ​​the overlapping diffraction pattern is not considered during adjustment (e.g., optimization). Optimization boundary conditions can be established for overlapping source variable areas and used to perform free-form source mask optimization (e.g., as described above).

[0115] In an embodiment, the method further comprises determining a source variable region of the diffraction pattern constraint (e.g., a region in which pixels may be adjusted / optimized), adjusting the pupil variable in the source variable region of the diffraction pattern constraint, and rendering the final pupil based on the adjusted pupil variable in the source variable region of the diffraction pattern constraint to improve illumination efficiency and increase throughput in a source mask optimization (SMO) process. In an embodiment, determining the source variable region of the diffraction pattern constraint, adjusting the pupil variable in the source variable region of the diffraction pattern constraint, and rendering the final pupil based on the adjusted pupil variable in the source variable region of the diffraction pattern constraint optimizes the pupil for generating an aerial image. In an embodiment, determining the source variable region of the diffraction pattern constraint, adjusting the pupil variable in the source variable region of the diffraction pattern constraint, and rendering the final pupil based on the adjusted pupil variable in the source variable region of the diffraction pattern constraint optimizes the pupil for generating a resist image. In an embodiment, determining the source variable region constrained by the diffraction pattern, adjusting the pupil variable in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variable in the source variable region constrained by the diffraction pattern are performed at any numerical aperture and / or wavelength. In an embodiment, determining the source variable region constrained by the diffraction pattern, adjusting the pupil variable in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variable in the source variable region constrained by the diffraction pattern are performed as part of a memory chip, a logic chip, and / or a microprocessor chip manufacturing process. In an embodiment, determining the source variable region constrained by the diffraction pattern, adjusting the pupil variable in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variable in the source variable region constrained by the diffraction pattern are performed as part of a source optimization process using a fixed mask.

[0116] In an embodiment, determining a source variable region constrained by the diffraction pattern, adjusting the pupil variable in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variable in the source variable region constrained by the diffraction pattern are performed so that a performance parameter is maintained at an acceptable level. In an embodiment, the performance parameter is an edge placement error (EPE), an image log slope (ILS), a normalized image log slope (NILS), a random edge placement error (SEPE), and / or a depth of focus (DOF). As described above, in Figure 5 In the example shown in , the final discrete source (pupil) 514 has an illumination efficiency of 0.93 (which is higher than the illumination efficiency of the previous final discrete pupil), but has a DOF (10% EL) of 105.4 and a NILS of 2.3 (which is still comparable to the same indicators used for the previous method).

[0117] In an embodiment, the method further comprises generating machine readable instructions that can be used to generate a final pupil based on the rendered image (e.g., Figure 5 514 shown in ) to change the source and / or mask associated with the lithographic apparatus. This may include generating the machine-readable instructions based on programming code drafted by a user, writing the instructions to a storage device (e.g., a non-transitory storage device), storing the instructions on a server, and / or other operations. The machine-readable instructions may include lines of code, databases, and / or other information arrangements stored in files and / or other electronic formats. These examples are not intended to be limiting.

[0118] By way of non-limiting example, Figure 6 The diagram illustrates how the operations of the present method described above may be incorporated into a larger diffraction pattern guided SMO process 600 (eg, a lobe / elliptical / dipole / hexapole source optimization process). Figure 6 Some and / or all of the operations shown in flow 600 in can be considered as part of the present method. Flow 600 begins with generating 602 a wafer and / or pattern mask model (design) and M3D (segment) (eg, using FEM+, Tachyon).

[0119] Flow 600 continues by determining 302 (also in Figure 3A Flow 600 includes determining the source variable region 311 based on the diffraction pattern 312 (e.g., determined using a thin mask model in this case). As described above, this includes determining the source variable region 311 based on the diffraction pattern and pupil fill ratio (PFR) threshold, the initial source (srcinit.src), the source variable location (srcloc.src), the source variable minimum value (srcmin.src), the source variable maximum value (srcmax.src), and / or other information (e.g., in Figure 3B , Figure 4 and Figure 5 The operations shown in and described above are used to determine (and generate corresponding electronic files).

[0120] In an embodiment, the process 600 includes continuous transmission mask (CTM) optimization 604 (e.g., optimize 95), and tuning 606 the dose and the mask with an anchor pattern. In an embodiment, the process 600 includes CTM source-mask co-optimization 608 (e.g., optimize 100, optimize 110 (power4)). In an embodiment, the process 600 includes sub-resolution assist feature (SRAF) extraction 610 and mask simplification 612 (e.g., srafgen300, maskclean, pgm2mask3xx). In an embodiment, the process 600 includes polygon source-mask co-optimization 614 (opc500, opc600, opc601 (power4) without mask rule checking (MRC). In an embodiment, the process 600 includes polygon mask cleaning and simplification 616 (maskclean700, pgm2mask701, 710, 720). In an embodiment, the process 600 includes focus and dose optimization 618 (opc800). It should be noted that for a SBAR (sub-resolution assist grating)-free embodiment of the process 600, operations 604 to 618 described in this paragraph can be skipped or ignored.

[0121] Flow 600 includes polygonal source mask co-optimization 620 (opc900, opc910) with MRC. In an embodiment, flow 600 includes mask depth of focus tuning 622 and dose / focus optimization 624 (opc918, opc920). In an embodiment, flow 600 includes extreme ultraviolet (EUV) source discrete rendering 626 (srcrender970), dose and focus optimization 628 (opc972), and discrete source and polygonal mask co-optimization 630 (opc974, opc976). It should be noted that for EUV-only embodiments of flow 600, operations 622 to 630 described in this paragraph may be necessary.

[0122] Finally, the process 600 includes: generating 632 a Hopkin's model (modelgen1200), a dose optimization 634 (opc1800), a polygon optimization 636 (opc1910); and updating the Hopkin's model (modelgen2000). The Hopkin's model is an imaging model that can calculate the imaging intensity at a given location using a given source profile, a pupil profile, and a given mask profile. Compared to the standard model (Abbe's model), the Hopkin's model is a fast method with flexible accuracy settings, and is particularly useful when there is a large area mask and both the source profile and the pupil profile remain unchanged (e.g., in the opc1910 stage).

[0123] Figure 71 is a block diagram illustrating a computer system 100 that can assist in implementing the methods, processes, or apparatus 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 storage device, coupled to the bus 102 for storing information and instructions to be executed by the processor 104. The main memory 106 can 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.

[0124] The computer system 100 can be coupled to a display 112 for displaying information to a computer user via the bus 102, such as a cathode ray tube (CRT) or a flat panel display or a touch panel display. An input device 114 including alphanumeric keys and other keys is coupled 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 for communicating direction information and command selections to the processor 104 and for controlling cursor movement on the display 112, such as a mouse, trackball, or cursor direction keys. This input device typically has two degrees of freedom in two axes, a first axis (e.g., x) and a second axis (e.g., y), which allows the device to specify a position in a plane. A touch panel (screen) display can also be used as an input device.

[0125] According to one embodiment, a portion of one or more methods described in the present invention 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 embodiments, 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.

[0126] The term "computer-readable medium" as used herein refers to any medium that participates in providing instructions to the processor 104 for execution. The 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 volatile memory, such as main memory 106. Transmission media include coaxial cables, copper wires, and optical fibers, including wires including bus 102. Transmission media can also take the form of sound waves or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media include, for example, floppy disks, flexible 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 medium that can be read by a computer.

[0127] Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to the processor 104 for execution. For example, the instructions may initially be carried on a disk, solid-state storage device, and / or other location of a remote computer (e.g., a server and / or other computing device). The remote computer may load the instructions into its dynamic memory and send the instructions using a modem via a telephone line over a wireless communication network (e.g., the Internet, a cellular communication network, etc.) and / or by other methods. A modem and / or other data receiving component local to the computer system 100 may receive data from a telephone line, etc., via a wireless communication network, and use an infrared transmitter to convert the data into an infrared signal. An infrared detector coupled to the bus 102 may receive the data carried in the infrared signal and place the data on the bus 102. The bus 102 carries the data to the main memory 106, from which the processor 104 retrieves and executes the instructions. The instructions received by the main memory 106 may optionally be stored on the storage device 110 before or after being executed by the processor 104.

[0128] 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 coupling 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 carrying digital data streams representing various types of information.

[0129] The network link 120 typically provides data communication to other data devices via one or more networks. For example, the network link 120 may provide a connection to a host computer 124 or to data equipment operated by an Internet Service Provider (ISP) 126 via a local area network 122. The ISP 126, in turn, provides data communication services via 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, which carry the digital data to and from the computer system 100, are exemplary forms of carrier waves that transport the information.

[0130] The computer system 100 can send information and receive data, including code, via the network, network link 120, and communication interface 118. In the Internet example, server 130 may transmit requested code for an application via Internet 128, ISP 126, local area network 122, and communication interface 118. For example, one such downloaded application may provide all or part of the methods described herein. The received code may be executed by processor 104 as it is received and / or stored in storage device 110 or other non-volatile memory for later execution. In this way, computer system 100 may obtain application code in the form of a carrier wave.

[0131] Figure 8 An exemplary lithographic projection apparatus that can be utilized in conjunction with the techniques described herein is schematically depicted. The apparatus comprises:

[0132] - an illumination system IL for conditioning the radiation beam B. In this particular case, the illumination system also comprises a radiation source SO;

[0133] a first stage (e.g. patterning device table) MT having 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 relative to the apparatus PS;

[0134] a second stage (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 apparatus PS; and

[0135] - 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).

[0136] As described in the present invention, the device is of the transmissive type (i.e., with a transmissive patterning device). However, in general, it may also be of the reflective type, for example (with a reflective patterning device). The device may use different kinds of patterning devices relative to classical masks; examples include programmable mirror arrays or LCD matrices.

[0137] A source SO (e.g. a mercury lamp or an excimer laser, a laser produced plasma (LPP) free electron laser or other EUV source) generates a radiation beam. The beam is fed into an illumination system (illuminator) IL, for example directly or after having traversed an adjustment member such as a beam expander Ex. The illuminator IL may include adjustment members 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 include various other components, such as an integrator IN and a condenser CO. In this way, the beam B impinging on the patterning device MA has a desired uniformity and intensity distribution in its cross-section.

[0138] about Figure 8 It should be noted that the source SO may be within the housing of the lithographic projection apparatus (which is usually the case when the source SO is, for example, a mercury lamp), but it may also be remote from the lithographic projection apparatus, the radiation beam it generates being guided into the apparatus (for example with the aid of appropriate guiding mirrors). This latter case is often the case when the source SO is an excimer laser (for example based on KrF, ArF or F2 laser action).

[0139] The beam B then intercepts the patterning device MA which is held on the patterning device table MT. Having traversed the patterning device MA, the beam B passes through a lens PS which focuses the beam B onto a target portion C of the substrate W. With the aid of the second positioning means (and the interferometry means IF) the substrate table WT can be accurately moved, for example in order to position a different target portion C in the path of the beam B. Similarly, the first positioning means can be used to accurately position the patterning device MA relative to the path of the beam B, for example after mechanically obtaining 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 interferometry means not in the patterning device library. Figure 8 Movement of the stage MT, WT is achieved by a long-stroke module (coarse positioning) and a short-stroke module (fine positioning) explicitly depicted in the figure. 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.

[0140] The depicted tools can be used in two different modes:

[0141] - in step mode, the patterning device table MT is kept substantially stationary 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-direction and / or y-direction so that a different target portion C can be illuminated by the beam PB;

[0142] - In scan mode, essentially the same situation applies, but a given target portion C is not exposed in a single "flash". Instead, the patterning device table MT is movable 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; at the same time, 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 having to compromise resolution.

[0143] Fig. 9 Another exemplary lithographic projection apparatus 1000 that may be utilized in conjunction with the techniques described herein is schematically depicted.

[0144] The lithography projection device 1000 comprises:

[0145] - Source collector module SO;

[0146] - an illumination system (illuminator) IL configured to condition a radiation beam B (e.g. EUV radiation);

[0147] 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;

[0148] a substrate table (e.g. wafer stage) WT configured to hold a substrate (e.g. a resist-coated wafer) W and connected to a second positioner PW configured to accurately position the substrate; and

[0149] - 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 Cl of the substrate W (eg, comprising one or more dies).

[0150] like Fig. 9 As depicted in , the apparatus 1000 is of the reflective type (e.g., using a reflective patterning device). It should be noted that since most materials are absorptive in the EUV wavelength range, the patterning device can have a multilayer reflector comprising multiple stacks of, for example, 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. X-ray lithography can be used to produce smaller wavelengths. Since most materials are absorptive at EUV and X-ray wavelengths, a thin segment 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 (negative resist).

[0151] The illuminator IL receives a beam of extreme ultraviolet radiation from a 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 (e.g., xenon, lithium, or tin) using one or more emission lines in the EUV range. In one such method, often referred to as laser produced plasma "LPP", a plasma may be generated by irradiating a fuel (such as a droplet, stream, or cluster of material having the line emitting element) with a laser beam. The source collector module SO may be a laser ( Fig. 9 The laser is used to provide a laser beam for exciting the fuel. The resulting plasma emits output radiation, such as EUV radiation, which is collected using a radiation collector placed in a source collector module. For example, when a CO2 laser is used to provide the laser beam for fuel excitation, the laser and the source collector module can be separate entities.

[0152] In this case, 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, appropriate guide 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. In an embodiment, a DUV laser source may be used.

[0153] 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 condition the radiation beam to have a desired uniformity and intensity distribution in its cross-section.

[0154] The radiation beam B is incident on a patterning device (e.g. a mask) MA held on a support structure (e.g. a patterning device table) MT and is patterned by the patterning device. After being reflected by 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 the substrate W. With the aid of a second positioner PW and a position sensor PS2 (e.g. an interferometer device, a linear encoder or a capacitive sensor), the substrate table WT can be accurately moved, for example so that different target portions C are positioned 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.

[0155] The depicted device 1000 may be used in at least one of the following modes:

[0156] In step mode, the support structure (e.g. patterning device table) MT and the substrate table WT are kept substantially stationary while the entire pattern imparted to the radiation beam is projected onto the target portion Cl at one time (i.e. a single static exposure). Then, the substrate table WT is shifted in the X and / or Y direction so that a different target portion Cl can be exposed.

[0157] In scan mode, the support structure (e.g. patterning device table) MT and the substrate table WT are scanned synchronously as a pattern imparted to the radiation beam is projected onto a target portion Cl (i.e. a single dynamic exposure). The velocity 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.

[0158] In another mode, the support structure (e.g., patterning device table) MT is kept substantially stationary, thereby holding the programmable patterning device, and the substrate table WT is moved or scanned, while a pattern imparted to the radiation beam is projected onto the target portion C1. In this mode, a pulsed radiation source is typically used, and the programmable patterning device is updated as required after each movement of the substrate table WT or between successive radiation pulses during a scan. This mode of operation may be readily applicable to maskless lithography using a programmable patterning device, such as a programmable mirror array of the type mentioned above.

[0159] Fig.10 The apparatus 1000 is shown in more detail, comprising a source collector module SO, an illumination system IL and a projection system PS. The source collector module SO is 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 (and / or other sources described above). EUV radiation can be generated by a gas or vapor (e.g., Xe gas, Li vapor, or Sn vapor), wherein a thermal plasma 210 is generated to emit radiation in the EUV range of the electromagnetic spectrum. For example, the thermal plasma 210 is generated by a discharge that causes at least a partially ionized plasma. For efficient generation of radiation, a partial pressure of, for example, 10 Pa of Xe, Li, Sn vapor, or any other suitable gas or vapor may be required. In an embodiment, an excited tin (Sn) plasma is provided to generate EUV radiation.

[0160] The radiation emitted by the hot plasma 210 is transferred from the source chamber 211 to the collector chamber 212 via an optional 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 herein includes at least a channel structure.

[0161] 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 traversing 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 configured 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.

[0162] The radiation then traverses an illumination system IL which may include a faceted field mirror arrangement 22 and a faceted pupil mirror arrangement 24 which are configured to provide a desired angular distribution of the radiation beam 21 at the patterning device MA, and a desired uniformity of 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 the projection system PS via reflective elements 28, 30 onto a substrate W held by a substrate table WT.

[0163] 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.10 The reflective elements shown may have from 1 to 10 or more additional reflective elements.

[0164] like Fig.10 The collector optics CO further illustrated is depicted as a nested collector with grazing incidence reflectors 253, 254 and 255, merely as an example of a collector (or collector mirror). The grazing incidence reflectors 253, 254 and 255 are positioned 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, often referred to as a DPP source.

[0165] Alternatively, the source collector module SO may be as follows Fig.11 Components of the LPP radiation system shown in . The laser LA is arranged to deposit laser energy into a fuel such as xenon (Xe), tin (Sn) or lithium (Li), thereby producing a highly ionized plasma 210 with an electron temperature of tens of electron volts. The energetic radiation produced 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.

[0166] The following aspects may be used to further describe the embodiments:

[0167] 1. A method for configuring a lithographic apparatus, the method comprising:

[0168] determining a source variable region based on the diffraction pattern, the source variable region corresponding to one or more regions of the diffraction pattern of the pupil in which the pupil variable is to be adjusted;

[0169] adjusting the pupil variable for the one or more regions of the pupil corresponding to the source variable region; and

[0170] A final pupil is rendered based on the adjusted pupil variables.

[0171] 2. The method according to clause 1, further comprising: determining the diffraction pattern and the pupil based on a pattern of a source mask design and an initial pupil.

[0172] 3. A method according to aspects 1 or 2, wherein determining the source variable region for the diffraction pattern comprises selecting a region of interest in the diffraction pattern having different but overlapping diffraction orders.

[0173] 4. A method according to aspect 3, wherein the source variable region in the diffraction pattern includes a plurality of pixels in an image of a selected region of interest in the diffraction pattern, and wherein determining the source variable region further includes binarizing the plurality of pixels in the image so that each individual pixel is included in or excluded from the source variable region.

[0174] 5. A method according to aspect 4, wherein the binarization is performed using a step function, a sigmoid or a hyperbolic binarization function, by sequentially applying the sigmoid and hyperbolic binarization functions, or by applying a clustering-based image thresholding method.

[0175] 6. A method according to aspect 4 or 5, wherein the source variable region includes one or more source variable regions.

[0176] 7. The method of clause 6, wherein non-zero intensity pixels are included in the one or more source variable regions and zero intensity pixels are excluded from the one or more source variable regions.

[0177] 8. A method according to any one of aspects 6 to 7, wherein determining the one or more source variable regions further comprises dilating a shape formed by the individual pixels included in the one or more source variable regions.

[0178] 9. The method of clause 8, wherein binarization and dilation transform the diffraction pattern such that the one or more source variable regions satisfy a minimum pupil filling ratio value.

[0179] 10. The method of any one of clauses 1 to 9, wherein the diffraction pattern is determined using a full rigorous mask three-dimensional (M3D) or compact mask three-dimensional (M3D) effect or a thin mask model.

[0180] 11. The method according to any one of Aspects 1 to 10 further includes determining one or more additional diffraction patterns based on the source, the design target, and the corresponding additional patterns of the optimized mask, and wherein one or more source variable regions are also determined for the diffraction pattern and the one or more additional diffraction patterns.

[0181] 12. A method according to aspect 1, wherein the source variable region in the diffraction pattern includes a plurality of pixels in an image of a selected region of interest in the diffraction pattern, each individual pixel in the plurality of pixels having a given intensity, and the method further comprises adjusting a variable constrained in the diffraction pattern amplitude to optimize a variable of a diffraction pattern amplitude degree, wherein adjusting the variable constrained in the diffraction pattern amplitude to optimize the variable of the diffraction pattern amplitude degree causes pixels having a given intensity between a lower amplitude threshold and an upper amplitude threshold to change.

[0182] 13. The method of any one of clauses 1 to 12, wherein adjusting the pupil variable comprises a pixel-based free-form, parameter-based source optimization.

[0183] 14. The method according to any one of Aspects 1 to 13 further includes: determining a source variable region constrained by the diffraction pattern, adjusting the pupil variable in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variable in the source variable region constrained by the diffraction pattern to improve illumination efficiency and increase throughput in the source mask optimization (SMO) process.

[0184] 15. A method according to any one of Aspects 1 to 14, wherein a source variable region constrained by the diffraction pattern is determined, the pupil variables in the source variable region constrained by the diffraction pattern are adjusted, and the final pupil is rendered based on the adjusted pupil variables in the source variable region constrained by the diffraction pattern to optimize the pupil for generating a spatial image.

[0185] 16. A method according to any one of Aspects 1 to 15, wherein a source variable region constrained by the diffraction pattern is determined, the pupil variables in the source variable region constrained by the diffraction pattern are adjusted, and the final pupil is rendered based on the adjusted pupil variables in the source variable region constrained by the diffraction pattern to optimize the pupil for producing a resist image.

[0186] 17. A method according to any one of Aspects 1 to 16, wherein at any numerical aperture and / or wavelength: determining a source variable region constrained by the diffraction pattern, adjusting the pupil variable in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variable in the source variable region constrained by the diffraction pattern are performed.

[0187] 18. A method according to any one of Aspects 1 to 17, wherein determining the source variable region constrained by the diffraction pattern, adjusting the pupil variables in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variables in the source variable region constrained by the diffraction pattern are performed as part of a memory chip, a logic chip, and / or a microprocessor chip manufacturing process.

[0188] 19. A method according to any one of Aspects 1 to 18, wherein determining a source variable region constrained by the diffraction pattern, adjusting the pupil variables in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variables in the source variable region constrained by the diffraction pattern are performed as part of a source optimization process utilizing a fixed mask.

[0189] 20. A method according to any one of Aspects 1 to 18, wherein determining a source variable region constrained by the diffraction pattern, adjusting the pupil variables in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variables in the source variable region constrained by the diffraction pattern are performed so that performance parameters are maintained at an acceptable level.

[0190] 21. The method of clause 20, wherein the performance parameter is edge placement error (EPE), image log slope (ILS), normalized image log slope (NILS), random edge placement error (SEPE), and / or depth of focus (DOF).

[0191] 22. A method according to clauses 1 to 21, further comprising generating machine readable instructions which may be used to change a source and / or mask associated with the lithographic apparatus based on the rendered final pupil.

[0192] 23. A computer program product comprising a non-transitory computer readable medium having recorded thereon instructions, the instructions when executed by a computer implementing the method according to any one of aspects 1 to 22.

[0193] The concepts disclosed herein can simulate or mathematically model any general imaging system for imaging sub-wavelength features, and can be used in particular with emerging imaging technologies that can produce shorter and shorter wavelengths. Emerging technologies already in use include extreme ultraviolet (EUV) patterning processes. EUV lithography can produce wavelengths in the range of 5 to 20 nm by using a synchrotron or by using high energy electrons to impinge on a material (solid or plasma) in order to produce photons in this range.

[0194] 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 lithography imaging systems for imaging on substrates other than silicon wafers.

[0195] The above description is intended to be illustrative, and not restrictive. It will therefore be apparent to the skilled person that modifications may be made as described without departing from the scope of the claims set forth hereinafter.

Claims

1. A method for configuring a lithographic apparatus, the method comprising: determining a source variable region based on the diffraction pattern, the source variable region corresponding to one or more regions of the diffraction pattern of the pupil in which the pupil variable is to be adjusted; adjusting the pupil variable for the one or more regions of the pupil corresponding to the source variable region; as well as A final pupil is rendered based on the adjusted pupil variables.

2. The method according to claim 1, further comprising: The diffraction pattern and the pupil are determined based on a pattern of a source mask design and an initial pupil.

3. The method of claim 1 , wherein determining the source variable region for the diffraction pattern comprises selecting a region of interest in the diffraction pattern having different but overlapping diffraction orders; and / or Wherein the source variable region in the diffraction pattern includes a plurality of pixels in an image of a selected region of interest in the diffraction pattern, and wherein determining the source variable region further includes binarizing the plurality of pixels in the image so that each individual pixel is included in or excluded from the source variable region.

4. The method of claim 3, wherein the binarization is performed using a step function, a sigmoid or a hyperbolic binarization function, by sequentially applying the sigmoid and hyperbolic binarization functions, or by applying a clustering-based image thresholding method.

5. The method according to claim 3, wherein the source variable region comprises one or more source variable regions; and / or Wherein non-zero intensity pixels are included in the one or more source variable regions and zero intensity pixels are excluded from the one or more source variable regions.

6. The method of claim 5, wherein determining the one or more source variable regions further comprises dilating a shape formed by the individual pixels included in the one or more source variable regions; and / or The binarization and dilation transform the diffraction pattern so that the one or more source variable regions satisfy a minimum pupil filling ratio value.

7. The method of claim 1, wherein the diffraction pattern is determined using a full rigorous mask three-dimensional (M3D) or compact mask three-dimensional (M3D) effect or a thin mask model.

8. The method according to claim 1 also includes determining one or more additional diffraction patterns based on the source, the design target, and the corresponding additional patterns of the optimized mask, and wherein one or more source variable regions are also determined for the diffraction pattern and the one or more additional diffraction patterns.

9. A method according to claim 1, wherein the source variable region in the diffraction pattern includes a plurality of pixels in an image of a selected region of interest in the diffraction pattern, each individual pixel in the plurality of pixels having a given intensity, and the method further comprises adjusting a diffraction pattern amplitude constrained variable to optimize a diffraction pattern amplitude degree variable, adjusting the diffraction pattern amplitude constrained variable to optimize the diffraction pattern amplitude degree variable to change pixels having a given intensity between a lower amplitude threshold and an upper amplitude threshold.

10. The method of claim 1, wherein adjusting the pupil variable comprises a pixel-based free-form, parameter-based source optimization.

11. The method according to claim 1, further comprising: Determining a source variable region constrained by the diffraction pattern, adjusting the pupil variable in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variable in the source variable region constrained by the diffraction pattern to improve illumination efficiency and increase throughput in a source mask optimization (SMO) process.

12. The method of claim 1 , wherein a source variable region constrained by the diffraction pattern is determined, the pupil variables in the source variable region constrained by the diffraction pattern are adjusted, and the final pupil is rendered based on the adjusted pupil variables in the source variable region constrained by the diffraction pattern to optimize the pupil for generating a spatial image.

13. The method of claim 1 , wherein a source variable region constrained by the diffraction pattern is determined, the pupil variable in the source variable region constrained by the diffraction pattern is adjusted, and the final pupil is rendered based on the adjusted pupil variable in the source variable region constrained by the diffraction pattern to optimize the pupil for producing a resist image.

14. The method according to claim 1, wherein at any numerical aperture and / or wavelength, the following is performed: determining a source variable region constrained by the diffraction pattern, adjusting the pupil variable in the source variable region constrained by the diffraction pattern, and rendering the final pupil based on the adjusted pupil variable in the source variable region constrained by the diffraction pattern.

15. A computer program product comprising a non-transitory computer-readable medium having recorded thereon instructions, the instructions implementing the method according to claim 1 when executed by a computer.

Images