Method for radiation spectrum aware source mask optimization for lithography

Abstract

And photoetching. Source mask optimized SMO for a plurality of laser spectra is performed. Training data is generated by determining performance metrics, such as EPE, CDU, LER, LWR, DOF, NILS, for different laser spectra for each optimized source mask combination. A machine learning ML model is trained to determine an optimal laser spectrum based on the generated training data. The trained ML model is used to predict an optimal spectrum for the production design layout.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. application 63 / 397,995, filed on August 15, 2022, and is incorporated herein by reference in its entirety. Technical Field

[0003] The description herein relates to a method and system for optimizing a spectrum for a radiation source for a photolithography process. Background Art

[0004] Lithographic projection equipment can be used, for example, in the manufacture of integrated circuits (ICs). A patterning device (e.g., a mask) can contain or provide a pattern corresponding to an individual layer of the IC (a "design layout"), and the pattern can be transferred to a target portion (e.g., including one or more dies) on a substrate (e.g., a silicon wafer) by methods such as irradiating the target portion through the pattern on the patterning device. The target portion has been coated with a layer of radiation-sensitive material ("resist"). Typically, a single substrate includes multiple adjacent target portions, and the pattern is transferred to the target portion continuously by the lithographic projection equipment, one target portion at a time. In one type of lithographic projection equipment, the pattern on the entire patterning device is transferred to one target portion in one operation. Such equipment is generally referred to as a stepper. In an alternative device, generally referred to as a step-and-scan device, the projection beam is scanned on the patterning device along a given reference direction (the "scanning" direction) while synchronously moving the substrate 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 factor M (e.g. 4), and the reduction factor may be different in the x and y directions, the speed F at which the substrate is moved will be 1 / M times the speed of the projection beam scanning patterning device. Further information on the lithographic apparatus described herein can be gathered, for example, from U.S. Pat. No. 6,046,792, which is incorporated herein by reference.

[0005] Before the pattern is transferred from the patterned device to the substrate, the substrate may undergo various processes such as priming, resist coating, and soft baking. After exposure, the substrate may undergo other processes ("post-exposure processes"), such as post-exposure baking (PEB), development, hard baking, and measurement / inspection of the transferred pattern. This array of processes is used as the basis for manufacturing individual layers of a device (e.g., an IC). The substrate may then undergo various processes such as etching, ion implantation (doping), metallization, oxidation, chemical mechanical polishing, etc., all of which are intended to complete the individual layers of the device. If several layers are required in the device, the entire process or a variation thereof is repeated for each layer. Ultimately, the device will be present in each target portion on the substrate. The devices are then separated from each other by techniques such as cutting or sawing, whereby the individual devices can be mounted on a carrier, connected to pins, etc.

[0006] Therefore, manufacturing devices such as semiconductor devices typically involves processing a substrate (e.g., a semiconductor wafer) using a number of fabrication processes to form various features and multiple layers of the device. Such layers and features are typically manufactured and processed using, for example, deposition, lithography, etching, chemical mechanical polishing, and ion implantation. Multiple devices can be fabricated 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 a patterning step, such as optical and / or nanoimprint lithography using a patterning device in a lithography apparatus to transfer the pattern on the patterned device to the substrate, and typically but optionally involves one or more related pattern processing steps, such as resist development by a developing device, baking of 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 manufacture of devices such as ICs, where patterns formed on a substrate define the functional elements of devices such as microprocessors, memory chips, etc. Similar photolithography techniques are also used in the formation of flat panel displays, microelectromechanical systems (MEMS), and other devices.

[0008] As semiconductor manufacturing processes continue to advance, following a trend generally referred to as "Moore's Law," the size of functional elements has been continuously reduced, while the number of functional elements (such as transistors) per device has steadily increased over the decades. At the current state of the art, the layers of a device are fabricated using a lithography projection apparatus that projects a design layout onto a substrate using illumination from a deep ultraviolet illumination source, thereby creating 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] According to the resolution formula CD = k 1×λ / NA, features with dimensions smaller than the classical resolution limit of lithographic projection equipment are printed. This process is generally referred to as low-k 1 lithography, where λ is the wavelength of the radiation employed (currently 248nm or 193nm in most cases), NA is the numerical aperture of the projection optics in the lithographic projection apparatus, CD is the “critical dimension”, usually the size of the smallest feature being printed, and k 1 is the empirical resolution factor. Usually, k 1 The smaller it gets, the harder it is to reproduce on a substrate a pattern similar in shape and size to that 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 lithography projection equipment, the design layout or the patterning device. These include, for example, but are not limited to, optimization of NA and optical coherence settings, customized illumination schemes, use of phase-shifted patterning devices, optical proximity correction (OPC, sometimes also called "optical and process correction") in the design layout or other methods generally defined as "resolution enhancement technology" (RET). Summary of the invention

[0010] According to one embodiment, a method is provided for: obtaining, by a computer system, a plurality of design variables representing at least one of characteristics of a lithography process, geometric characteristics of a design layout, or a combination thereof; and determining, by the computer system, a spectrum of a radiation source for a lithography device based on the plurality of design variables, characteristics of the lithography process, geometric characteristics of a design layout, or at least one of a combination thereof.

[0011] In an embodiment, determining comprises determining, by a computer system, one or more parameters for a spectrum of the radiation source.

[0012] In an embodiment, one or more parameters of the spectrum for the radiation source include at least one of the number of components, the wavelength spacing between two or more components, the frequency spacing between two or more components, the component shape, the component distribution, the component intensity, the relative intensity of two components, the component nominal focus, the component focal center, the component displacement, or a combination thereof.

[0013] In an embodiment, determining comprises determining, by a computer system, a spectrum for the radiation source based on a latitude of at least one design variable of a plurality of design variables.

[0014] In an embodiment, the latitude includes latitude of at least one of depth of focus, exposure, common process window, image contrast, image log slope (ILS), normalized image log slope (NILS), or a combination thereof.

[0015] In an embodiment, determining comprises determining, by a computer system, a spectrum for the radiation source based on at least one lithography metric.

[0016] In an embodiment, at least one lithography metric includes at least one of edge placement error (EPE), critical dimension (CD), critical dimension uniformity (CDU), local critical dimension uniformity (LCDU), line edge roughness (LER), line width roughness (LWR), resist contour characteristics, maximum defect size, exposure latitude, image shift, mask error enhancement factor, focus, depth of focus (DOF), DOF tolerance, critical dimension depth of focus, process window tolerance, common process window tolerance, image logarithmic slope (ILS), normalized image logarithmic slope (NILS), image contrast, image contrast latitude, available process window above a specific NILS threshold (nDOF), or a combination thereof.

[0017] In an embodiment, determining includes: determining, by a computer system, a spectrum for a radiation source from a plurality of spectra based on at least one of a lithography metric, a design variable tolerance, or a combination thereof; wherein the plurality of spectra includes a spectrum for which at least one value of the lithography metric, the design variable tolerance, or a combination thereof has been pre-calculated.

[0018] In an embodiment, determining includes: selecting, by a computer system, a plurality of spectra from a plurality of spectra; and determining a spectrum for the radiation source based on a combination of the plurality of spectra.

[0019] In an embodiment, the determining includes: determining, by a computer system, a spectrum for a radiation source based on a machine learning model, the machine learning model being trained to determine an optimal spectrum based on at least one of a plurality of design variables, characteristics of a lithography process, geometric characteristics of a design layout, or a combination thereof.

[0020] In an embodiment, a machine learning model is trained to determine an optimal spectrum based on at least one lithography metric.

[0021] In an embodiment, the machine learning model is further trained to generate an optimal spectrum based on a combination of multiple contributing components.

[0022] In an embodiment, the combining comprises a linear combination.

[0023] In an embodiment, the combining comprises a convolution of at least two of the plurality of contributing components.

[0024] In an embodiment, combining comprises interpolation.

[0025] In an embodiment, at least one contributing component of the plurality of contributing components is of Lorentzian type.

[0026] In an embodiment, at least one contributing component of the plurality of contributing components is Gaussian.

[0027] In an embodiment, at least one contribution component of the plurality of contribution components comprises a Focke profile.

[0028] In an embodiment, the optimal spectrum is a free spectrum.

[0029] In an embodiment, the optimal spectrum has at least one of a smooth profile, a discontinuous profile, a non-differentiable profile, or a piecewise combination thereof.

[0030] According to another aspect, a method is provided, comprising: acquiring a plurality of target patterns; acquiring a plurality of design variables representing characteristics of a lithography process for the plurality of target patterns; acquiring a plurality of spectra of a radiation source for the lithography process; determining lithography metrics for the plurality of spectra for the plurality of target patterns based on the plurality of design variables representing characteristics of the lithography process for the plurality of target patterns; and training a model, by using the lithography metrics for the plurality of spectra, to determine one or more optimal spectra for the lithography process based on input target patterns, a set of design variables representing characteristics of the lithography process for the input target patterns, or a combination thereof.

[0031] In an embodiment, the model comprises a machine learning model.

[0032] In an embodiment, the machine learning model comprises a neural network.

[0033] In an embodiment, the machine learning model comprises a convolutional neural network.

[0034] In an embodiment, the model comprises a regression model.

[0035] In an embodiment, the model comprises an ensemble of machine learning models.

[0036] In an embodiment, training comprises: training a model to determine an optimal spectrum.

[0037] In an embodiment, the training comprises: training the model to determine the optimal spectrum based on a combination of one or more optimal spectra.

[0038] In an embodiment, the lithography metrics include edge placement error (EPE), critical dimension (CD), critical dimension uniformity (CDU), local critical dimension uniformity (LCDU), line edge roughness (LER), line width roughness (LWR), resist profile characteristics, maximum defect size, exposure latitude, image shift, mask error enhancement factor, focus, depth of focus (DOF), DOF tolerance, critical dimension depth of focus, process window tolerance, common process window tolerance, image log slope (ILS), normalized image log slope (NILS), available process window (nDOF) above a specific NILS threshold, or at least one of a combination thereof.

[0039] In an embodiment, training the model to determine one or more optimal spectra for a lithography process includes training the model to determine one or more optimal parameters for the spectra for the lithography process.

[0040] In an embodiment, the one or more optimal parameters include at least one of the number of components, the wavelength spacing between two or more components, the frequency spacing between two or more components, the component shape, the component distribution, the component intensity, the relative intensity of two components, the component nominal focus, the component focal center, the component displacement, or a combination thereof.

[0041] In an embodiment, a method further comprises: obtaining a production target pattern, a set of design variables representing characteristics of a lithography process producing the target pattern, or a combination thereof; and determining one or more optimal spectra for the lithography process used to produce the target pattern based on the trained model.

[0042] In an embodiment, training comprises training a model to determine one or more optimal spectra based on an input target pattern type.

[0043] In an embodiment, training includes training a model to determine an input target pattern type.

[0044] According to another aspect, a method is provided, comprising: calculating, by a computer system, a multivariate cost function of multiple design variables representing characteristics of a lithography process, the multivariate cost function being a function of a spectrum of a radiation source used for a lithography device, or being a function of the spectrum or a function of variables that affect the spectrum; and reconfiguring, by the computer system, one or more characteristics of the lithography process by adjusting one or more of the multiple design variables until a termination criterion is met.

[0045] According to another aspect, a method is provided, comprising: calculating, by a computer system, a multivariate cost function, which is a function of multiple design variables representing characteristics of a lithography process; and reconfiguring, by the computer system, one or more characteristics of the lithography process by adjusting one or more of the multiple design variables or recalculating the multivariate cost function based on the adjusted one or more of the multiple design variables until a specific termination condition is met, wherein a spectrum of a radiation source of the lithography device is adjustable during the reconfiguration.

[0046] In an embodiment, the reconfiguration is performed under the constraint that the geometrical properties of the design layout of the patterned device, including the lithographic process, remain substantially unchanged.

[0047] In an embodiment, the reconfiguring comprises adjusting a spectrum for the radiation source.

[0048] In an embodiment, reconfiguring comprises selecting the spectrum of the radiation source from a plurality of spectra of the radiation source.

[0049] In an embodiment, the spectrum of the radiation source comprises a plurality of contributing components.

[0050] In an embodiment, the spectrum comprises a combination of contributing components.

[0051] In an embodiment, at least one contributing component of the plurality of contributing components is of Lorentzian type.

[0052] In an embodiment, at least one contributing component of the plurality of contributing components is Gaussian.

[0053] In an embodiment, at least one contribution component of the plurality of contribution components comprises a Focke profile.

[0054] In an embodiment, the spectrum for the radiation source has at least one of a smooth profile, a discontinuous profile, a non-differentiable profile, or a piecewise combination thereof.

[0055] In an embodiment, the reconfiguration improves image quality characteristics of a lithography process.

[0056] In an embodiment, the reconfiguration increases the tolerance of at least one of the plurality of design variables.

[0057] In an embodiment, the latitude includes latitude of at least one of depth of focus, exposure, common process window, image contrast, image log slope (ILS), normalized image log slope (NILS), or a combination thereof.

[0058] In an embodiment, the multivariate cost function evaluates at least one of edge placement error (EPE), critical dimension (CD), critical dimension uniformity (CDU), local critical dimension uniformity (LCDU), line edge roughness (LER), line width roughness (LWR), resist profile characteristics, maximum defect size, exposure latitude, image shift, mask error enhancement factor, focus, depth of focus (DOF), DOF tolerance, critical dimension depth of focus, process window latitude, common process window latitude, image logarithmic slope (ILS), normalized image logarithmic slope (NILS), available process window (nDOF) above a specific NILS threshold, or a combination thereof.

[0059] In an embodiment, the reconfiguration comprises optimization of a multivariate cost function.

[0060] In an embodiment, the reconfiguration comprises gradient based optimization.

[0061] According to another embodiment, one or more non-transitory machine-readable media having instructions thereon are provided, which when executed by a processor are configured to perform a method according to any one of the other embodiments.

[0062] According to another embodiment, a system is provided, including: a processor; and one or more non-transitory machine-readable media having instructions thereon, which when executed by the processor are configured to perform the method according to any one of the other embodiments.

[0063] According to another embodiment, a system is provided, comprising: a processor; a laser; and one or more non-transitory machine-readable media having instructions thereon, which when executed by the processor are configured to perform a method according to any one of the other embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate certain aspects of the subject matter disclosed herein and, together with the description, help explain some principles associated with the disclosed implementations.

[0065] Figure 1 is a block diagram of various subsystems of a lithographic projection apparatus according to an embodiment.

[0066] Figure 2 An exemplary flow chart for simulating lithography in a lithographic projection apparatus according to an embodiment is illustrated.

[0067] Figure 3 is a schematic overview of spectrally aware optimization for a lithography process according to an embodiment.

[0068] Figure 4 An exemplary method for spectrally aware optimization of a lithography process according to an embodiment is illustrated.

[0069] Figure 5 An example spectrum for spectrally aware optimization of a lithography process according to an embodiment is illustrated.

[0070] Figure 6 is a schematic overview of generating training data for spectrally-aware optimization of a lithography process according to an embodiment.

[0071] Figure 7 An exemplary method for training a model for spectrally aware optimization according to an embodiment is illustrated.

[0072] Figure 8 is a schematic overview of spectrally-aware optimization of a lithography process based on a trained model according to an embodiment.

[0073] Fig.9AAn exemplary method for spectral adjustment using a model during spectrally-aware optimization of a lithography process according to an embodiment is illustrated.

[0074] Fig. 9B An exemplary method for using a model for spectral determination during spectrally-aware optimization of a lithography process according to an embodiment is illustrated.

[0075] Fig.10 is a schematic overview of spectrally perceptual optimization with pattern classification according to an embodiment.

[0076] Fig.11A Depicted are example spectra for spectrally-aware optimization of an example lithography process, in accordance with an embodiment.

[0077] Fig. 11B Depicted is a diagram illustrating a method according to an embodiment of the present invention. Fig.11A FIG. 5 is a graph showing process window improvement resulting from spectral-aware optimization of an example lithography process.

[0078] Fig.12 Depicted are example spectra determined by spectrally-aware optimization of an example lithography process, according to an embodiment.

[0079] Fig.13 is a block diagram of an example computer system according to an embodiment.

[0080] Fig.14 is a schematic diagram of a lithographic projection apparatus according to an embodiment.

[0081] Fig.15 is a schematic diagram of another lithography projection apparatus according to an embodiment.

[0082] Fig.16 is a detailed view of a lithographic projection apparatus according to an embodiment.

[0083] Fig.17 is a detailed view of a source collector module of a lithographic projection apparatus according to an embodiment. DETAILED DESCRIPTION

[0084] The present disclosure describes source mask optimization, which includes determining a radiation spectrum of a radiation source for a lithography process. The determination of the radiation spectrum may include determination of an optimal spectrum (e.g., optimization of the radiation spectrum). The radiation spectrum may be determined based on multiple spectra, based on a model selected from a library including a machine learning model, interpolation, etc. The radiation spectrum may be a free spectrum generated from one or more constituent components. The determination of the radiation spectrum may be part of the source mask optimization of the lithography process. The determination of the radiation spectrum may be iterative. The radiation spectrum may be determined based on a cost function.

[0085] Although specific reference may be made herein to the manufacture of ICs, it should be clearly understood that the description herein has many other possible applications. For example, it may be used in the manufacture of 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.

[0086] As used herein, the terms "radiation" and "beam" are used to encompass all types of electromagnetic radiation, including ultraviolet radiation (e.g., wavelengths of 365, 248, 193, 157, or 126 nm) and EUV (extreme ultraviolet radiation, e.g., wavelengths in the range of about 5 to 100 nm). As used herein, the terms "radiation source" or "source" are used to encompass all types of radiation sources, including laser sources, incandescent sources, and the like, and it may include processing of the radiation between the radiation source and the target or other parts of the optics, including filtering, collimation, focusing, and the like. A source may include multiple sources that contribute to the generation of radiation for lithography, including a source that combines multiple contributions from one or more sources, where the contributions may be altered relative to each other, including by filtering, wavelength shifting, and the like. Contributions to the radiation of a source may include excitation responses from elements, compounds, mixtures, and the like, and the contributions may be combined in one or more ways, such as additively, subtractively, convolutedly, and the like.

[0087] The patterning device may include or may form one or more design layouts. The design layout may be generated using a CAD (computer-aided design) program, including general CAD programs such as AutoCAD, Solidworks, etc., or may be a layout-specific CAD program such as LayoutEditor, KLayout, etc. This process is generally referred to as EDA (electronic design automation). Most CAD programs follow a predetermined set of design rules to create a functional design layout / patterning device. These rules are set based on processing and design constraints. For example, the design rule defines 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 design rule restrictions may be referred to as "critical dimensions" (CD). The critical dimension of a device may be defined as the minimum width of a line or hole or the minimum space between two lines or two holes. Therefore, CD regulates the total size and density of the designed device. One of the goals in device fabrication is to reproduce the original design intent on a substrate faithfully (via a patterning device).

[0088] The term "mask" or "patterning device" as used herein may be broadly interpreted as referring to a general patterning device that can be used to impart a patterned cross-section to an incoming radiation beam, corresponding to the pattern to be created in the target portion of the substrate. In this context, the term "light valve" may also be used. In addition to the classical masks (transmission or reflection masks; binary, phase-shifting, hybrid masks, etc.), other examples of such patterning devices include programmable mirror arrays. An example of such a device is a matrix-addressable surface having a viscoelastic control layer and a reflective surface. The basic principle behind such a device is that (for example) the addressed areas of the reflective surface reflect the incident radiation as diffracted radiation, while the unaddressed areas reflect the incident radiation as undiffracted radiation. Using appropriate filters, the undiffracted radiation can be filtered out of the reflected beam, leaving only the diffracted radiation; in this way, the beam is patterned according to the addressing pattern of the matrix-addressable surface. The required matrix addressing can be performed using suitable electronic components. 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.

[0089] The term "projection optics" as used herein 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 to collectively or individually guide, shape, or control a projection beam of radiation. The term "projection optics" may include any optical component in a lithographic projection apparatus, regardless of where the optical component is located in 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 radiation after the radiation passes through a patterning device.

[0090] Figure 1 1 is a block diagram of various subsystems of a lithographic projection apparatus 10A according to an embodiment. The main components are a radiation source 12A, which may be a deep ultraviolet excimer laser source or other types of sources, including: an extreme ultraviolet (EUV) source (the lithographic projection apparatus itself need not have a radiation source); illumination optics, which, for example, define partial coherence (denoted as σ) and may include optics 14A, 16Aa, and 16Ab that shape the radiation from source 12A; a patterning device (or mask) 18A; and transmission optics 16Ac that projects an image of the patterning device pattern onto a substrate plane 22A.

[0091] According to an embodiment of the present disclosure, a radiation source may include a system for providing and modifying the spectrum of radiation provided by the radiation source. Modifying the spectrum may include adding or subtracting components of the spectrum, including a spectrum composed of multiple components, modifying the wavelength distribution of the contributing components (e.g., from Gaussian to Lorentzian), changing the dispersion of the components, or otherwise changing the intensity of the radiation source with respect to the wavelength.

[0092] A pupil 20A may be included with the transmission optics 16Ac. In some embodiments, there may be one or more pupils before and / or after the mask 18A. As described in further detail herein, the pupil 20A may provide patterning of 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 that impinge on the substrate plane 22A, where the maximum possible angle defines the numerical aperture NA=n sin(θmax) of the projection optics, where n is the refractive index of the medium between the substrate and the last element of the projection optics, and θmax is the maximum angle of the beam that leaves the projection optics such that the beam can still impinge on the substrate plane 22A.

[0093] In a lithographic projection device, a source provides irradiation (i.e., radiation) to a patterning device, and projection optics guides and shapes the irradiation onto a substrate via a patterning device. In some instances, a source may provide patterning, guiding, or shaping of radiation. In some instances, patterning, guiding, or shaping of radiation may occur between a source and projection optics. Projection optics may include at least some of components 14A, 16Aa, 16Ab, and 16Ac. Aerial image (AI) is a radiation intensity distribution at substrate level. A resist model may be used to calculate a resist image by an aerial image, an example of which may be found in U.S. Patent Application Publication No. US 2009-0157360, the disclosure of which is incorporated herein by reference in its entirety. The resist model is related to the characteristics of the resist layer (e.g., the effects of chemical processes occurring during exposure, post-exposure baking (PEB), and development). The optical characteristics of a lithographic projection device (e.g., characteristics of irradiation, patterning devices, and projection optics) specify an aerial image and may be defined in an optical model. Since the patterning device used in the lithographic projection apparatus can be changed, it is desirable to separate the optical characteristics of the patterning device from the optical characteristics of the rest of the lithographic projection apparatus, including at least the source and projection optics. Details of techniques and models for transforming a design layout into various lithographic images (e.g., aerial images, resist images, etc.), applying OPC using these techniques and models, and evaluating performance (e.g., in terms of process windows) are described in U.S. Patent Application Publication Nos. US2008-0301620, 2007-0050749, 2007-0031745, 2008-0309897, 2010-0162197, 2010-0180251, the disclosures of each of which are incorporated herein by reference in their entirety.

[0094] One aspect of understanding the photolithography process is understanding the interaction of radiation with the patterning device. The electromagnetic field of the radiation after the radiation passes through the patterning device can be determined by the electromagnetic field of the radiation before the radiation reaches the patterning device and a function that characterizes the interaction. This function can be called the mask transmission function (it can be used to describe the interaction performed by the transmissive patterning device and / or the reflective patterning device).

[0095] The mask transmission function can have a variety of different forms. One form is binary. The binary mask transmission function has any one of two values ​​(e.g., zero and a positive constant) at any given location on the patterning device. The binary form of the mask transmission function can be referred to as a binary mask. Another form is continuous. That is, the modulus of the transmittance (or reflectance) of the patterning device is a continuous function of the location on the patterning device. The phase of the transmittance (or reflectance) can also be a continuous function of the location on the patterning device. The continuous form of the mask transmission function can be referred to as a continuous tone mask or a continuous transmission mask (CTM). For example, the 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.), rather than a binary value of 0 or 1. In an embodiment, the CTM can be a pixelated grayscale image, where each pixel has a value (e.g., in the range [-255, 255], with a normalized value in the range [0, 1] or [-1, 1] or other appropriate ranges).

[0096] The thin mask approximation (also known as Kirchhoff boundary conditions) is widely used to simplify the determination of the interaction of radiation with the patterned device. The thin mask approximation assumes that the thickness of the structures on the patterned 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 patterned device is the multiplication of the incident electromagnetic field and the mask transmission function. However, as lithography processes use radiation with shorter and shorter wavelengths, and the structures on the patterned device become smaller and smaller, the assumptions of the thin mask approximation may be broken. For example, due to its finite thickness ("mask 3D effect" or "M3D"), the interaction of radiation with structures (such as the edge between the top surface and the sidewall) may become significant. Including this scattering in the mask transmission function can enable the mask transmission function to better capture the interaction of radiation with the patterned device. The mask transmission function under the thin mask approximation can be referred to as a thin mask transmission function. The mask transmission function that includes M3D can be referred to as an M3D mask transmission function.

[0097] Figure 2An exemplary flow chart for simulating lithography in a lithography projection apparatus according to an embodiment is illustrated. A source model 31 represents the optical properties of a source (including radiation intensity distribution and / or phase distribution). A spectrum model 30 represents the components of a spectrum (including the number of components, the shape of the components, the intensity as a function of wavelength, the peak width, etc.). The spectrum model 30 may be part of or different from the source model 31. A projection optics model 32 represents the optical properties of the projection optics (including changes in the radiation intensity distribution and / or phase distribution caused by the projection optics). A design layout model 35 represents the optical properties of a design layout (including changes in the radiation intensity distribution and / or phase distribution caused by the design layout 33), which is a representation of a feature arrangement formed on or by a patterning device. An aerial image 36 may be simulated by a design layout model 35, a projection optics model 32, and a design layout model 35. A resist image 38 may be simulated from an aerial image 36 using a resist model 37. The simulation of lithography may, for example, predict the profile and CD in a resist image.

[0098] More specifically, the source model 31 can represent the optical properties of the source, including but not limited to the numerical aperture setting, the illumination sigma (σ) setting, and any specific illumination shape (e.g., off-axis radiation sources, such as toroidal, quadrupole, and dipole, etc.). The spectral model 30 can represent the spectral properties of the source, including but not limited to the number of spectral components, the shape of the spectral components (e.g., Gaussian, Lorentzian, rectangular wave train, etc.), the shape of the spectrum (e.g., intensity as a function of wavelength), the quality of the spectrum (e.g., smooth, continuous, discontinuous, differentiable, non-differentiable, etc.), and other spectral quantities and qualities. The projection optics model 32 can represent the optical properties of the projection optics, including aberrations, distortions, one or more refractive indices, one or more physical sizes, one or more physical dimensions, etc. The design layout model 35 can represent one or more physical properties of the physical patterning device, such as described in U.S. Patent No. 7,587,704, which is incorporated herein by reference in its entirety. The purpose of the simulation is to accurately predict, for example, edge placement, spatial image intensity slope, and / or CD, which can then be compared with the intended design. The intended design is typically defined as a pre-OPC design layout, which may be provided in a standardized digital file format (such as GDSII or OASIS) or another file format.

[0099] From the design layout, one or more portions may be identified, which are referred to as "clips." In an embodiment, a collection of clips are extracted that represent complex patterns in the design layout (typically about 50 to 1000 clips, although any number of clips may be used). These patterns or clips represent small portions of the design (i.e., circuits, cells, or patterns), and more specifically, clips typically represent small portions that require special attention and / or verification. In other words, a clip may be a portion of the design layout, or may be similar or have similar behavior to a portion of the design layout, where one or more key features are identified through experience (including clips provided by customers), through trial and error, or by running full-chip simulations. A clip may contain one or more test patterns or gauge patterns.

[0100] An initial larger set of clips may be provided a priori by the customer based on known key feature areas in the design layout that require specific image optimization. Alternatively, in another embodiment, an initial larger set of clips may be extracted from the entire design layout using some automatic (such as machine vision) or manual algorithm that identifies one or more key feature areas.

[0101] In a lithographic projection apparatus, as an example, the cost function can be expressed as

[0102]

[0103] Among them (z 1 ,z 2 ,…z N ) are N design variables or their values. p (z 1 ,z 2 ,…z N ) can be a design variable (z 1 ,z 2 ,…z N ), such as (z 1 ,z 2 ,…z N ) is the difference between the actual and expected values ​​of a characteristic for a set of values ​​of a design variable. p is with f p (z 1 ,z 2 ,…z N ) is a weight constant associated with the edge of the pattern. For example, the characteristic may be the position of the edge of the pattern measured at a given point on the edge. p (z 1 ,z 2 ,…z N ) may have different weights w pFor example, if a particular edge has a narrow range of allowed positions, then f, which represents the difference between the actual position of the edge and the expected position, p (z 1 ,z 2 ,…z N )’s weight w p Can be given higher values. f p (z 1 ,z 2 ,…z N ) can also be a function of the sandwich properties, which in turn are design variables (z 1 ,z 2 ,…z N ) function. Of course, CF(z 1 ,z 2 ,…z N ) is not limited to the form in equation 1. CF(z 1 ,z 2 ,…z N ) may be in any other suitable form.

[0104] The cost function may represent any one or more suitable characteristics of the lithographic projection apparatus, lithographic process, or substrate, such as focus, CD, image shift, image distortion, image rotation, random variation, throughput, local CD variation, process window, interlayer characteristics, or a combination thereof. In one embodiment, the design variables (z 1 ,z 2 ,…z N ) includes one or more selected from dose, overall bias of the patterning device, and / or irradiation shape. Since the resist image generally specifies the pattern on the substrate, the cost function may include a function representing one or more characteristics of the resist image. For example, f p (z 1 ,z 2 ,…z N ) can simply be the distance between a point in the resist image and the expected position of that point (i.e., the edge placement error EPE p (z 1 ,z 2 ,…z N )). Design variables may include any adjustable parameters, such as adjustable parameters of the source, patterning device, projection optics, dose, focus, etc.

[0105] The lithographic apparatus may include components collectively referred to as "wavefront manipulators", which may be used to adjust the shape of the wavefront and the intensity distribution and / or phase shift of the radiation beam. In an embodiment, the projection device may adjust the wavefront and intensity distribution at any location along the optical path of the lithographic projection apparatus, such as before the patterning device, near the pupil plane, near the image plane and / or near the focal plane. The wavefront manipulator may be used to correct or compensate for certain distortions of the wavefront and intensity distribution and / or phase shift caused by, for example, temperature changes in the source, patterning device, lithographic projection apparatus, thermal expansion of components of the lithographic projection apparatus, etc. Adjusting the wavefront and intensity distribution and / or phase shift may change the value of the characteristic represented by the cost function. Such changes may be simulated from a model or actually measured. The design variables may include parameters of the wavefront manipulator.

[0106] Design variables can have constraints, which can be expressed as (z 1 ,z 2 ,…z N )∈Z, where Z is the set of possible values ​​for the design variables. One possible constraint on the design variables may be imposed by the desired throughput of the lithographic projection equipment. In the absence of such a constraint imposed by the desired throughput, the optimization may produce an unrealistic set of values ​​for the design variables. For example, if dose is the design variable, then in the absence of such a constraint, the optimization may produce dose values ​​that make the production volume economically impossible. However, the validity of the constraint should not be interpreted as necessary. For example, throughput may be affected by the pupil fill ratio. For some illumination designs, a low pupil fill ratio may discard radiation, resulting in lower throughput. Throughput may also be affected by the resist chemistry. Slower resists (e.g., resists that require a higher amount of radiation to be properly exposed) result in lower production volumes.

[0107] As used herein, the term "process model" refers to a model that includes one or more models that simulate a patterning process. For example, a process model may include any combination of an optical model (e.g., modeling a lens system / projection system used to deliver light in a lithography process, and may include modeling a final optical image of light entering a photoresist), a resist model (e.g., modeling physical effects on the resist, such as chemical effects caused by light), and an optical proximity correction (OPC) model (e.g., may be used to make a mask or reticle and may include sub-resolution assist features (SRAFs), etc.).

[0108] As used herein, the term "concurrently" refers to two or more events occurring approximately at the same time, but not necessarily at exactly the same time. For example, changing the pupil design concurrently with the mask pattern may mean making a small modification to the pupil design, then making a small adjustment to the mask pattern, then making another modification to the pupil design, etc. However, the present invention contemplates that in some parallel processing applications, concurrency may refer to operations occurring simultaneously or operations having some overlap in time.

[0109] The present disclosure provides apparatus, methods and computer program products, and it is particularly related to modifying or optimizing the features of lithographic equipment to improve performance and manufacturing efficiency. The features that can be modified can include the spectrum of light used in the lithographic process, mask, pupil, etc. Any combination of these features (and possible other features) can be implemented to improve, for example, the depth of focus, process window, contrast, etc. of the lithographic equipment. In some embodiments, the modification of a feature affects other features. In this way, in order to achieve the desired improvement, multiple features can be modified / changed concurrently, as described below.

[0110] Figure 3 A schematic overview of stochastic-aware optimization of a lithography process 300 is depicted. The lithography process 300 may be any type of lithography process, including UV, EUV, etc. The lithography process may be characterized by a design layout 302 and a set of requirements 304. The design layout 302 may include information about a plurality of layers. The design layout 302 may include information about the three-dimensional shapes of features contained in the design layout 302. The set of requirements 304 may be instrument requirements (e.g., corresponding to limitations, ranges, etc. of process equipment), material requirements (e.g., limitations, ranges, etc. imposed on the lithography process by the material of the device to be fabricated, which may or may not be exposed or patterned by the current lithography process), customer requirements (e.g., limitations, ranges, etc. requested by a supplier of the design layout 302), etc. The set of requirements 304 may include a set of design rules to which a recipe 330 of the lithography process may preferentially follow.

[0111] The process optimizer 306 may be operable to optimize the lithography process 300. The design layout 302 and the set of requirements 304 may be input into or obtained by the process optimizer 306. The process optimizer 306 may optimize a recipe 330 for the lithography process 300 to comply with the design layout 302 and the set of requirements 304. The process optimizer 306 may also optimize process window conditions (including exposure latitude, common process window, etc.) or other lithography metrics (including EPE, LCDU, etc.), which may be irrelevant to the design layout 302 or the set of requirements 304. The process optimizer 306 may operate based on a cost function 308.

[0112] The cost function 308 may include weighted contributions from lithography metrics such as EPE. The cost function 308 may also include penalties for various parameters that may be used to drive the process optimizer 306 toward a solution 330 that satisfies the design layout 302 and the set 304 of requirements. The cost function 308 may be a multivariate cost function. The cost function 308 may be a differentiable function. The process optimizer 306 may use the cost function 308 to optimize the solution 330, such as by gradient descent or other methods. The cost function 308 may be determined based on an iteration of a lithography solution, wherein the solution 330 is an optimized iteration of the lithography solution. Optimization includes determining a solution, source configuration, mask configuration, etc. that satisfies the design layout 302 and the set 304 of requirements. Optimization is not limited to determining the best solution or configuration among all possible solutions or configurations, but includes determining a solution or configuration that falls within an allowable threshold of an ideal solution or configuration or otherwise satisfies the design layout 302 and the set 304 of requirements. Optimization may include selecting between multiple solutions or configurations that satisfy the design layout 302 and the set of requirements 304 , including selecting based on process window considerations, lithography metrology considerations, and the like.

[0113] The cost function 308 can be determined based on a mask configuration, which can be determined based on a mask optimization 310. The mask optimization 310 can iteratively operate on the mask configuration, including changing the mask configuration based on the cost function 308. The mask optimization 310 can include continuous transmission mask (CTM) optimization, polygon optimization, Manhattan optimization, etc. The mask optimization 310 can include the generation of one or more assist features. The mask optimization 310 can interact with a lithography model, such as a resist model, a photon model, an etch model, etc., which can be used to generate a predicted output of the lithography process 300 for iterations of the mask optimization 310.

[0114] The cost function 308 can be determined based on the source configuration, which can be determined based on the source optimization 312. The source optimization can iteratively operate on the source configuration, including changing the mask configuration based on the cost function 308. The source optimization 312 can include unconstrained free optimization, free optimization, etc. The source optimization 312 can include optimization of one or more parameters of the source configuration. The source optimization 312 can interact with a lithography model, such as a resist model, a photon model, an etch model, etc., which can be used to generate a predicted output of the lithography process 300 for iteration of the source optimization 312.

[0115] The cost function 308 can be determined based on the radiation spectrum, which can be determined based on the spectrum optimization 314. The spectrum optimization 314 can iteratively operate on the radiation spectrum, including by adding or subtracting components from the spectrum, changing the shape of the components of the spectrum, etc. The spectrum optimization 314 can include optimization of one or more parameters of the radiation source. The spectrum optimization 314 can interact with a lithography model, such as a resist model, a photon model, an etch model, etc., which can be used to generate a predicted output of the lithography process 300 for iterations of the spectrum optimization 314.

[0116] Spectral optimization 314 may operate based on spectral library 316. Spectral library 316 may contain spectra for lithography process 300 for which various parameters have been determined. Spectra may include one or more contributions, such as peaks, having a central wavelength, a wavelength dispersion, a distribution shape, etc. Spectra may include laser line spectra, which may be combined to generate additional spectra. Spectral library 316 may contain lithography parameters associated with spectra, such as one or more performance indicators, which may be used to determine one or more optimal spectra from a plurality of spectra. Spectral optimization 314 may additionally or alternatively operate on spectra of spectral library 316, combinations of free spectra, and / or generate new spectra not contained in spectral library 316.

[0117] The process optimizer 306 may also interact with other optimization processes, including dose optimization, focus optimization, spectral optimization, etc. The mask optimization 310, source optimization 312, and spectral optimization 314 may occur simultaneously, alternatively, on different time scales, etc. The process optimizer 306 operates to jointly optimize the mask optimization 310, source optimization 312, and optical frequency optimization 314. The process optimizer 306 may operate to jointly optimize the mask optimization 310 and source optimization 312, and then optimize the spectral optimization 314 based on the joint optimization of the mask optimization 310 and source optimization 312. The spectral optimization 314 may operate based on the selection of a performance metric rather than the optimization of the cost function 308.

[0118] The process optimizer 306 may determine a cost function 308 based on a current iteration of a solution for the lithography process 300. The process optimizer 306 may determine the cost function 308 based on a modeling output of the current iteration of the lithography process 300. The process optimizer 306 may determine a solution 330 that satisfies the design layout 302 and the set of requirements 304 for the lithography process 300. The solution 330 may be an optimized solution, where optimization does not require that the solution is the best solution, but refers to a solution that at least satisfies a set of conditions (such as the design layout 302, the set of requirements 304, etc.).

[0119] The process optimizer 306 may determine process window conditions 320 for the current iteration of the lithography process 300. The process window conditions 320 may or may not include variables used to determine the cost function 308. For example, exposure latitude may be included in the process window conditions 320, but may or may not be a variable on which the cost function 308 depends. In another example, edge placement error (EPE) may be used to determine the cost function 308, but may not be the process window conditions 320. The process window conditions 320 may include one or more latitudes, such as exposure latitude, dose latitude, etc. The process window conditions 320 may include measured values ​​(e.g., ranges) of values ​​of one or more parameters, where the current iteration of the lithography process 300 satisfies the design layout 302 and the set of requirements 304. The process window conditions 320 may be measured as a function of the current iteration of the lithography process 300. Alternatively, one or more of the process window conditions 320 may be optimized by the process optimizer 306 using the cost function 308 or another process.

[0120] The process window condition 320 of the output of the process optimizer 306 for the current iteration of the lithography process 300 may be checked by a lithography manufacturability check 322, which may check the current iteration of the lithography process 300 against lithography manufacturability criteria. For example, the lithography manufacturability check 322 may determine that the current iteration of the lithography process 300 includes a source configuration that is incompatible with the radiation source of the lithography process 300. The lithography manufacturability check 322 may be performed for each configuration or iteration, or may be performed partially for most configurations or iterations, and may also include a final iteration of the lithography manufacturability check 322 before outputting a recipe 330 for the lithography process 300. The lithography manufacturability check 322 may also determine whether the source configuration, mask configuration, and spectrum are compatible in the combination of the lithography process 300.

[0121] The scheme 330 may be output to one or more lithography equipment. For example, the scheme 330 may be output in whole or in part to a mask writer, a source configurator, a laser (which may provide at least a portion of the spectrum), etc. The scheme 330 may also be stored in one or more memory storage devices.

[0122] Figure 4 An exemplary method 400 for spectrally aware optimization of a lithography process is illustrated. The operations of method 400 presented below are intended to be illustrative. In some embodiments, method 400 may be performed with one or more additional operations not described and / or without one or more operations discussed. Additionally, the operations of method 400 may be performed in Figure 4The order illustrated in and described below is not intended to be limiting. In some embodiments, one or more portions of method 400 may be implemented in one or more processing devices (e.g., one or more processors) (e.g., by simulation, modeling, etc.). The one or more processing devices may include one or more devices that perform some or all of the operations of method 400 in response to instructions electronically stored on an electronic storage medium. For example, the one or more processing devices may include one or more devices that are configured by hardware, firmware, and / or software to be specifically designed to perform one or more operations of method 400.

[0123] In operation 402, a layout for a lithography process is obtained. The layout may be a design layout (e.g., a pre-OPC design layout), which may be provided in a standardized digital file format (such as GDSII or OASIS) or another file format. The layout may be a single-layer layout or a multi-layer layout. The layout may correspond to a single lithography process, a multi-step lithography process, multiple lithography processes, etc. The layout may include a desired feature set and may include information about tolerances, such as acceptable edge placement errors, CD, etc.

[0124] In operation 404, requirements for the lithography process are obtained. These requirements may be instrument requirements, material requirements, process requirements, design requirements, etc., which define the allowable ranges or values ​​of various parameters of the lithography process. These requirements may correspond to a single lithography process, a multi-step lithography process, multiple lithography processes, etc. These requirements may constrain or limit one or more variables of the lithography process that may be optimized. Operation 404 and operation 402 may be performed concurrently, sequentially in any order, synchronously, asynchronously, etc.

[0125] In operation 406, at least one of source optimization, mask optimization, or a combination thereof is performed on the lithography process. Operation 406 may be performed based on a cost function, which may be determined based on one or more models of the lithography process, including an optical model, an etching model, a photon shot model, a resist chemistry model, and the like. Operation 406 may include an iterative process based on an iteration of a lithography process protocol. Operation 406 may include modeling the results of the current iteration of the protocol for the lithography process, and determining the value of the cost function based on the modeled results. Operation 406 may include optimization based on the cost function, such as by gradient descent or other appropriate methods. The cost function may be a multivariate cost function, which may depend on design variables, including source parameters, mask parameters, and the like. The cost function may also include a penalty term, which may be a function of the design variables, a function of the modeling parameters, and the like (e.g., a sidelobe penalty, a process window penalty), which may reduce undesirable source and mask configurations, accelerate convergence toward a stable configuration, and improve process robustness. The cost function can depend on one or more lithography metrics, including modeled lithography metrics, which can include edge placement error (EPE), critical dimension (CD), critical dimension uniformity (CDU), local critical dimension uniformity (LCDU), line edge roughness (LER), line width roughness (LWR), resist profile characteristics, maximum defect size, exposure latitude, image shift, mask error enhancement factor, focus, depth of focus (DOF), DOF tolerance, critical dimension depth of focus, process window tolerance, common process window tolerance, exposure latitude, image logarithmic slope (ILS), normalized image logarithmic slope (NILS), image contrast, image contrast latitude, available process window (nDOF) above a specific NILS threshold.

[0126] Operation 406 may continue until a termination criterion is met. The termination criterion may be the number of iterations, the value of the cost function, a consistency threshold between the modeling parameters of the current iteration based on the optimization and the design layout, etc. Operation 406 may output an iterative solution that meets the termination criterion based on the optimization of the lithography process.

[0127] Operation 406 may include operation 408, operation 410, or a combination thereof. Operation 408 and operation 410 may be performed concurrently, sequentially in any order, serially, in parallel, synchronously, asynchronously, etc. Operation 408 and operation 410 may be performed based on the same cost function. Operation 408 and operation 410 may have the same termination criteria.

[0128] In operation 408, source optimization of the lithography process is performed. Source optimization may optimize parameters of the source. Source optimization may include optimization of optical properties of the source, including aperture settings, illumination settings, illumination shapes, etc. Source optimization may be performed based on a cost function, where the cost function may be a function corresponding to one or more design variables of the source.

[0129] In operation 410, mask optimization of the lithography process is performed. Mask optimization may optimize parameters of the mask. Mask optimization may include optimization of clipping, optimization of mask layout, optimization of SRAF, optimization of physical properties of mask design, etc. Mask optimization may be performed based on a cost function, where the cost function may be a function corresponding to one or more design variables of the mask.

[0130] In operation 412, a process window is determined for a scheme for a lithography process. The scheme may include source optimization, mask optimization, or a combination thereof that satisfies termination criteria. The process window may be a measure of the range of values ​​of one or more design parameters that the scheme satisfies the layout and requirements of the lithography process. The process window conditions may include tolerances for depth of focus, exposure, image contrast, image logarithmic slope (ILS), normalized image logarithmic slope (NILS), etc. The process window may include a process window for one or more processes of the lithography process. The process window may be or include a common process window. The process window may be determined based on one or more models of the lithography process.

[0131] In operation 414, it is determined whether the solution for the lithography process corresponds to acceptable parameters. The parameters may be process window parameters, lithography metrics, source parameters, mask parameters, etc. The parameters may include termination criteria. If the solution corresponds to acceptable parameters, the process continues to operation 416. If it is not determined that the solution corresponds to acceptable parameters, the process continues to operation.

[0132] In operation 416, a source spectrum is determined. The source spectrum may be determined based on a solution. The source spectrum may be determined based on source optimization, mask optimization, or a combination thereof. The source spectrum may be selected from a spectrum library. The source spectrum may be selected from a component library and consist of one or more components of a spectrum. The source spectrum may be determined based on a machine learning model. The lithography process may be classified, for example, by a machine learning model, and the source spectrum may be determined based on the classification. The source spectrum may be selected based on one or more performance indicators. The source spectrum may be

[0133] In operation 418, the solution is checked for lithography compatibility. The solution can be checked by a lithography manufacturing checker (LMC), which can determine whether the solution produces an incompatible process. The LMC can determine whether one or more parts of the solution are incompatible with each other or may generate unacceptable defect levels. The LMC can determine defect density, defect variation, hot spot variability, etc. The LMC can be used to determine whether the solution meets a reliability threshold. If the solution is incompatible, operation 406 can be performed again for the adjusted layout and / or requirements. Additionally or alternatively, the weights of the cost function can be adjusted, which can include using additional penalty terms to reduce defect-intensive areas of the solution.

[0134] In operation 420, a solution is output. The solution may be output to one or more lithography tools, such as a lithography tool, a mask aligner, a scanner, etc. The solution may be output to a radiation source, which may include one or more radiation sources that may produce a spectrum. The solution may be output to a storage device in addition or in lieu thereof. The solution may be output to more than one device (e.g., a tool), stored in more than one piece, or stored and output to a combination of devices. The solution may be checked for compatibility with a tool for which a portion of the solution is intended. If the solution is incompatible with a tool, a different tool may be selected for the solution and / or the solution may be reoptimized with a set of updated requirements including additional tool restrictions.

[0135] As described above, method 400 (and / or other methods and systems described herein) is configured to perform spectrally-aware optimization of a lithography process.

[0136] Figure 5 Example spectra for spectrally aware optimization of a lithography process are illustrated. Figure 5 Several example spectra (spectra 502, 504, 506, 508, 510, 512, 514, and 516) are included, which can be selected for use in a lithography process based on spectral-aware optimization. Spectra 502, 504, 506, 508, 510, 512, 514, and 516 are plotted as a function of wavelength along the x-axis in arbitrary units of intensity along the y-axis. Spectra 502, 504, 506, 508, 510, 512, 514, and 516 include multiple peaks, each representing an individual contribution to the spectrum. The depicted spectra 502, 504, 506, 508, 510, 512, 514, and 516 include a combination of various components, which can be Gaussian distributions, Lorentz distributions, Fock distributions, etc. of wavelengths. Spectra 502, 504, 506, 508, 510, 512, 514, and 516 can be linear combinations of various components, with separation and relative intensity determined by spectral perception optimization. Spectra 502, 504, 506, 508, 510, 512, 514, and 516 can also include convolutions of various components. The components of spectra 502, 504, 506, 508, 510, 512, 514, and 516 can also have different shapes, dispersion relations, side peaks, and / or other effects of the components themselves that are added to the combination of components. Spectra 502, 504, 506, 508, 510, 512, 514, and 516 are provided as non-limiting illustrative examples, and not all available spectra or components are illustrated.

[0137] Figure 6is a schematic overview of generating training data for spectrally aware optimization of a lithography process. Generating training data or a spectral library that may include performance metrics may include determining a pattern classification based on a design layout and / or a set of design requirements. The lithography process can be classified by pattern type. The pattern type may correspond to the size and / or shape of the features of the design. For example, a pattern may be classified as a linear pattern (e.g., a pattern including a line or other straight shape) or a circular pattern (e.g., a pattern including a contact hole or other curved shape). Pattern types may be classified as pitches (e.g., regularly repeating patterns) or non-repeating patterns. Pattern types may be classified by which type of feature the pattern corresponds to, such as a memory feature, a via feature, etc. Pattern types may be classified in a variety of ways.

[0138] The training data 610 can be generated based on pattern types, which can be grouped by one or more pattern classifications. The training data 610 can also be generated based on selected patterns. In a non-limiting illustrative example, linear pattern 602a and circular pattern 602b are used to generate training data. For each pattern of the training set, one or more spectra are selected. In a non-limiting illustrative example, spectra 604a, 604b, and 604c are selected for linear pattern 602a, while spectra 604e and 604f are selected for circular pattern 602b. The same or different numbers of the same spectra or different spectra can be selected for each pattern. The spectra can include components, such as individual intensity peaks corresponding to optical excitation. The spectra can include multiple constituent components, such as in Figure 5 Spectra 502 to 516 are depicted. Spectra may include components having different shapes or distributions, such as Gaussian, Lorentzian, Focke, etc.

[0139] For each spectrum selected for the pattern of the training set, source mask optimization 606 can be performed. Source mask optimization 606 can be determined based on the design layout of the pattern and the set of requirements. Source mask optimization 606 can generate a solution for the lithography process corresponding to the design layout of the pattern of the training set and the set of requirements. A performance metric set can be determined for the solution generated by source mask optimization 606. Each spectrum can be associated with a performance metric set 608a to 608f determined to be optimized for the lithography process. In a non-limiting illustrative example, spectrum 604a can correspond to performance metric 608a, spectrum 604b can correspond to performance metric 608b, spectrum 604c can correspond to performance metric 608c, spectrum 604e can correspond to performance metric 608e, and spectrum 604f can correspond to performance metric 608f. Performance metrics can include lithography metrics, including EPE, CD, LCDU, etc. described previously. Performance metrics can also or instead include process window conditions, including exposure latitude, DOF latitude, etc. described previously. The performance metric may include a plurality of parameters describing one or more portions of a lithography process or an output of a lithography process. The performance metric may include an indication of an optimal spectrum for a pattern. The optimal spectrum may be a spectrum having the best lithography metric (e.g., minimum EPE, minimum CD, lowest predicted defect rate, etc.) or the best process window condition (e.g., maximum latitude) for the spectrum selected for inclusion in the training data. The selection of the optimal spectrum is not limited to the selection of the absolute optimal spectrum of an infinite number of available spectrums, but rather the selection of the best spectrum based on a set of available spectrums or a specific metric in resource-limited spectrum generation.

[0140] Training data 610 can then be generated based on performance metrics associated with the spectrum corresponding to each pattern included in the training set. Training data 610 may include pattern and spectrum pairs labeled with values ​​of one or more performance metrics. Training data 610 may also include a scheme for a lithography process generated by source mask optimization. Training data 610 may include classification of patterns into one or more pattern types. Training data 610 may also be divided into training data sets corresponding to one or more pattern types, such as can be used to train multiple machine learning models or a collection of machine learning models. Training data 610 can be used to train a model different from a machine learning model. Training data 610 may further or instead include a spectral library, wherein the spectrum is associated with a performance metric. The spectral library can be used to select a spectrum based on a desired performance metric.

[0141] Figure 7An exemplary method 700 for training a model for spectral perception optimization is illustrated. The operations of method 700 presented below are intended to be illustrative. In some embodiments, method 700 may be performed with one or more additional operations not described and / or without one or more operations discussed. Additionally, the operations of method 700 may be performed in Figure 7 The order illustrated in and described below is not intended to be limiting. In some embodiments, one or more portions of method 700 may be implemented in one or more processing devices (e.g., one or more processors) (e.g., by simulation, modeling, etc.). The one or more processing devices may include one or more devices that perform some or all of the operations of method 700 in response to instructions electronically stored on an electronic storage medium. For example, the one or more processing devices may include one or more devices that are configured by hardware, firmware, and / or software to be specifically designed to perform one or more operations of method 700.

[0142] In operation 702, a layout and requirements for a photolithography process are obtained. The layout may be obtained in any suitable manner, such as with respect to Figure 4 These requirements may be obtained in any suitable manner, such as with respect to Figure 4 The obtaining of the layout and the obtaining of the requirements may be performed concurrently, sequentially in any order, synchronously, asynchronously, etc.

[0143] In operation 704, a spectrum of a radiation source for a lithography process is selected. The spectrum can be selected from a set of spectra, generated a priori from a set of spectral criteria, generated from one or more other spectra (e.g., by convolution, linear combination, etc.), etc. The spectrum can be selected based on a set of allowed criteria, which can correspond to a laser or other radiation source. The spectrum can be selected based on a previously identified spectrum of the lithography source, for example, the spectrum can be generated by adjusting one or more parameters of a previously identified spectrum of the lithography process. The spectrum can be generated without considering the allowed spectrum of the radiation source. For example, the spectrum can be a theoretical spectrum that cannot be generated by a specific radiation source. The radiation source can be adjusted based on the identified spectrum, or multiple radiation sources can be combined, so that a previously unallowed spectrum can be selected for model training.

[0144] The selected spectrum can be parameterized. The selected spectrum can be described as a spectrum (e.g., in the form of intensity as a function of wavelength). The selected spectrum can be described as a set of parameters, such as the number of components, the wavelength separation distance of such components, the intensity of such components, etc. The selected spectrum can be symmetrical about the central wavelength or can be asymmetrical. The selected spectrum can be described by one or more full width at half maximum (FWHM) of one or more components. The selected spectrum can be described by one or more distribution types of one or more components.

[0145] In operation 706, source mask optimization (SMO) of the lithography process is performed. SMO can be performed based on the layout and requirements obtained for the lithography process. SMO can adjust various parameters of the source, mask and combination thereof to provide an optimized solution for the lithography process. SMO can operate based on a selected spectrum, wherein the selected spectrum may not be adjusted during SMO. Alternatively, the spectrum can be adjusted based on requirements for the lithography process, source optimization, etc. SMO may have been previously performed, such as for a pre-modeled and / or fabricated lithography process that may be included in training data. SMO may be performed in batches for one or more layouts and requirements. SMO may be performed individually for a spectrum, or may be performed partially or entirely for a collection of spectra.

[0146] Operation 706 may be performed based on a cost function, such as that previously described with reference to Figure 4 Operation 706 may be performed based on a cost function, which may be determined based on one or more models of the lithography process, including an optical model, an etching model, a photon shot model, a resist chemistry model, etc. Operation 706 may include an iterative process based on iterations of a scheme for the lithography process. Operation 706 may include modeling the results of the current iteration of the scheme for the lithography process, and determining a value of the cost function based on the results of the modeling. Operation 706 may continue until a termination criterion is met, as previously described with reference to Figure 4 Operation 406 is described.

[0147] Operation 706 may include a source optimization operation, a mask optimization operation, or a combination thereof. The source optimization and mask optimization may be performed concurrently, sequentially in any order, serially, in parallel, synchronously, asynchronously, etc. The source optimization and mask optimization may be performed based on the same cost function. The source optimization and mask optimization may have the same termination criteria. The source optimization may be performed by any suitable method, such as previously described with reference to Figure 4 The mask optimization may be performed by any suitable method, such as described previously with reference to Figure 4 Operation 410 is described.

[0148] In operation 708, a performance metric for the lithography process is determined. The performance metric may be a key performance metric (KPI) or other performance metric determined for the lithography process. The performance metric may be determined based on modeling of the lithography process performed during or as a result of the SMO. The performance metric may be determined based on measured metrics of the lithography process, such as for a test wafer, for a fabricated device, and the like. The performance metric may be determined based on both modeling and measured metrics, based on modeling informed by measured metrics, and the like. The performance metric may include a performance metric determined for a previously fabricated device, such as for a pattern and / or spectrum previously used for a lithography process and to be included in the training data. The performance metric may include defect metrics, lithography metrics, process window conditions, and the like. The performance metric may include another measure of the impact of a cost-benefit analysis or a spectrum on device quality.

[0149] In operation 710, it is determined whether the spectrum is still to be selected for the lithography process. If a spectrum is still to be selected, the process continues to operation 704, where one or more additional spectra may be selected, generated, determined by adjusting previous spectra, etc. If there are no spectrums to be selected, the process continues to operation 712. It may be determined that there are no spectrums to be selected based on an iteration count, remaining spectra in the set of spectra, a time limit, etc. If a previous spectrum was determined to be an optimal spectrum or met criteria for optimization, it may be determined that there are no spectrums to be selected.

[0150] In operation 712, it is determined whether to obtain a layout and requirements for the lithography process. If a layout and / or requirements are still to be selected, the process continues to operation 702, where additional layouts and / or requirements can be selected. Multiple spectra of layouts and / or requirements can be analyzed and performance metrics determined. The order in which the spectra are selected in operation 704 does not need to be sorted based on the layout and / or requirement identification, but can be selected in any appropriate order.

[0151] In operation 714, training data is generated based on the performance metrics of the spectrum and SMO for the layout and requirements of the lithography process. The training data may include labeled data, wherein the spectrum, layout and / or requirements are labeled by one or more performance metrics. For a given spectrum, layout and / or requirement, the training data may include or may not include source optimization, mask optimization, or a combination thereof. The training data may be divided into one or more training data sets, such as by pattern type and / or another pattern classification. The training data may include test data, verification data, and the like. The training data may include a spectrum for which the measured performance metrics are available, a spectrum for which the modeled performance metrics are available, and a spectrum for which both the measured and modeled performance metrics are available. The training data may include multiple performance metrics, including performance metrics that may or may not present a dependency on the spectrum. For example, a first performance metric may be affected by the spectrum of the design layout, while a second performance metric may be independent (or quasi-independent) of the spectrum of the design layout.

[0152] In operation 716, a model is trained based on the training data. The model may be a mathematical model, such as a regression model. The model may be a machine learning model. The model may be a collection of machine learning models. The model may be a neural network, including a convolutional neural network (CNN). The model may be any appropriate machine learning model. The model may operate based on supervised learning. In some embodiments, the model may operate based on unsupervised learning. The model may be output to a memory or otherwise stored. Any appropriate method may be used to train the model. The model may be iteratively trained based on an additional training data set. The model may be updated based on additional training data, which may include performance metrics for measurements of the fabricated device. The model may be trained for a specific lithography process, a type of lithography process, and the like. The model may be pre-trained based on a training data set and further refined based on a more detailed or more specific training data set.

[0153] As described above, method 700 (and / or other methods and systems described herein) is configured to train a model for spectrally aware optimization.

[0154] Figure 8 8 is a schematic overview 800 of spectrally aware optimization of a lithography process based on a trained model. The trained model 810 operates on an input 802 to determine an output 820 of the lithography process. The trained model 810 can be trained in any suitable manner based on suitable training data, such as by referring to Figure 7The input 802 of the training model may include a pattern 804. The pattern 804 may include a design layout, a set of design requirements, a pattern type classification, etc. The pattern 804 may include a design layout for an entire wafer, a portion of a wafer, a set of features (or clips), or a wafer. The pattern 804 may include one or more pattern classifications. The trained model 810 may include a pattern type identifier. The trained model 810 may also include multiple models, wherein the model applied to the pattern 804 may be determined based on the pattern type classification.

[0155] Input 802 may include spectrum 806. Spectrum 806 may be a base spectrum, such as a spectrum adjusted by trained model 810 or based on output 820 of a machine learning model. Spectrum 806 may include one or more components having one or more distributions described previously. Spectrum 806 may be a spectrum previously determined for a lithography process or a similar lithography process. Spectrum 806 may be selected from a spectral library. Spectrum 806 may be parameterized or may be input as a spectrum (e.g., in the form of intensity versus wavelength). The form of spectrum 806 may depend on the form of the spectrum used to train trained model 810.

[0156] The output 820 of the training model 810 may include one or more performance metrics 822. The output 820 of the training model 810 may include a spectrum or an adjusted spectrum, such as an adjusted spectrum based on the spectrum 806 of the input 802. The output 820 of the training model 810 may include a set of spectra and performance metrics 822 of the spectra in the set. The output 820 of the training model 810 may include parameters of one or more spectra. The performance metric may include a predicted performance metric 822. The performance metric 822 may include an interpolated performance metric 822, such as a performance metric 822 based on one or more models. The performance metric 822 may include a confidence interval, probability, distribution, etc. of one or more performance metrics 822. The output 820 may include an adjustment to the spectrum or an indication of a spectrum optimized for spectral awareness of a lithography process, where the adjusted spectrum may be generated by the spectrum 806 and the adjustment. The adjusted spectrum with or without its corresponding performance metric may be used to update or retrain the trained model 810. The output 820 may include information about source optimization, mask optimization, or a combination thereof of the spectrum 806 of the input 802.

[0157] A performance metric parameter space 830 may be generated based on the performance metric 822. The performance metric parameter space 830 may relate one or more performance metrics to spectral parameters. The performance metric parameter space 830 may be generated based on the output 820 of the training model 810 corresponding to the plurality of spectra 806. The performance metric parameter space 830 may be used to iteratively adjust the spectra 806 to optimize one or more performance metrics 822 of the lithography process. The performance metric parameter space 830 may be updated based on additional inputs 802 and outputs 820 of the training model 810.

[0158] Fig.9A An exemplary method 900 for using a model for spectral adjustment during spectral-aware optimization of a lithography process is illustrated. The operations of method 900 presented below are intended to be illustrative. In some embodiments, method 900 may be performed with one or more additional operations not described and / or without one or more operations discussed. Additionally, the operations of method 900 may be performed in Fig.9A The order illustrated in and described below is not intended to be limiting. In some embodiments, one or more portions of method 900 may be implemented in one or more processing devices (e.g., one or more processors) (e.g., by simulation, modeling, etc.). The one or more processing devices may include one or more devices that perform some or all of the operations of method 900 in response to instructions electronically stored on an electronic storage medium. For example, the one or more processing devices may include one or more devices that are configured by hardware, firmware, and / or software to be specifically designed to perform one or more operations of method 900.

[0159] In operation 902, a layout and requirements for a photolithography process are obtained. The layout and requirements may be obtained by any suitable method, including by referring to Figure 7 Operation 702 is described.

[0160] In operation 904, source mask optimization (SMO) of the photolithography process is performed. SMO may be performed using any suitable method, including reference to Figure 7 The method is described in operation 706. The SMO may output a plan for a photolithography process.

[0161] In operation 906, a spectrum is optionally selected for the radiation source of the photolithography process. Any suitable method may be used to select the spectrum, including reference to Figure 7 Operation 704 of the method is described.

[0162] In operation 908, one or more performance metrics are predicted based on the SMO of the lithography process and optionally based on the spectrum. The performance metrics can be determined based on a model of the lithography process, such as an optical model, an etch model, a resist model, etc. The performance metrics can be determined based on the selected spectrum. The performance metrics can be determined based on any suitable method, such as referring to Figure 7 Operation 708 is described.

[0163] In operation 910, it is determined whether the performance metric is acceptable. If the performance metric is acceptable, the flow continues to operation 914, where the solution corresponding to the SMO output can be checked against the LMC. If the performance metric is not acceptable, the flow continues to operation 912, where the spectrum can be adjusted.

[0164] In operation 912, the spectrum is adjusted based on the performance metric parameter space, such as Figure 8 The performance metric parameter space 830 is described. The spectra and / or the parameters of the spectra can be based on the trained model (such as Figure 8 The trained model 810) is associated with one or more performance metrics. Based on the correlation of the spectrum and / or spectral parameters obtained from the trained model, a function that associates one or more performance metrics with the spectrum and / or spectral parameters can be created. If the performance metric of the current iteration of the scheme derived from the SMO and the selected spectrum does not meet the performance metric (e.g., one or more performance metrics are determined to be unacceptable in operation 910), then the spectrum and / or the parameters of the spectrum can be adjusted in a direction based on the derived performance metric parameter space. In an example, the spectrum can be parameterized based on the interval between components. The trained model can be used to generate a set of spectra with different parameterized values ​​(e.g., γ). The model can determine (e.g., predict) the value of one or more performance metrics (such as EPE) based on parameterized γ. If the performance metric of the current iteration of the spectrum (such as EPE) does not meet the acceptable performance metric standard, then the relationship between γ and EPE determined based on the trained model can be used to determine in which direction and / or in which amplitude to adjust γ. This example is provided for illustration only. The adjustment of the spectrum can include adjustments in multiple directions, including multivariate adjustments.

[0165] In operation 914, the spectral-based solution obtained from the SMO is checked for lithographic compatibility. The solution may be checked for lithographic compatibility using any suitable method, including reference to Figure 4 Operation 418 of the method is described.

[0166] In operation 916, the solution is output. The solution may be output and / or stored using any suitable method, such as by referring to Figure 4 Operation 420 is described.

[0167] As described above, method 900 (and / or other methods and systems described herein) is configured to use a model to adjust the spectrum during spectrally-aware optimization of a lithography process.

[0168] Fig. 9B An exemplary method 950 for using a model for spectral determination during spectral-aware optimization of a lithography process according to an embodiment is illustrated. The operations of method 950 presented below are intended to be illustrative. In some embodiments, method 950 may be accomplished with one or more additional operations not described and / or without one or more operations discussed. Additionally, the operations of method 950 may be performed in Fig. 9B The order illustrated in and described below is not intended to be limiting. In some embodiments, one or more portions of method 950 may be implemented in one or more processing devices (e.g., one or more processors) (e.g., by simulation, modeling, etc.). The one or more processing devices may include one or more devices that perform some or all of the operations of method 950 in response to instructions electronically stored on an electronic storage medium. For example, the one or more processing devices may include one or more devices that are configured by hardware, firmware, and / or software to be specifically designed to perform one or more operations of method 950.

[0169] In operation 952, a layout and requirements for a photolithography process are obtained. The layout and requirements may be obtained by any suitable method, including by referring to Fig.9A Operation 902 is described.

[0170] In operation 954, source mask optimization (SMO) for the photolithography process is performed. SMO can be performed using any suitable method, including reference to Fig.9A The method is described in operation 906. The SMO may output a plan for a photolithography process.

[0171] In operation 956, an optimal spectrum is determined based on the trained model. The optimal spectrum is determined based on the scheme determined during the SMO. The optimal spectrum is determined based on inputting the scheme of the lithography process into the trained model, wherein the trained model is trained to identify the spectrum based on the design layout, the set of requirements and / or the scheme of the lithography process. The optimal spectrum need not be the absolutely optimal spectrum, but rather a spectrum that is superior to other spectra in at least some respects.

[0172] In operation 958, one or more performance metrics are predicted based on the SMO of the lithography process and optionally based on the spectrum. The performance metrics may be determined based on any suitable method, such as by reference to Fig.9A Operation 908 is described.

[0173] In operation 960, it is determined whether the performance metric is acceptable. If the performance metric is acceptable, flow continues to operation 964, where the solution corresponding to the SMO output can be checked against the LMC. If the performance metric is not acceptable, flow continues to operation 962, where the spectrum can be adjusted.

[0174] In operation 962, if the spectrum does not correspond to an acceptable performance metric, at least one of adjusting the spectrum, source optimization, mask optimization, or a combination thereof is adjusted. The adjustment may be selected from a set of operations for adjustment. The adjustment may include re-optimization. The adjustment may include selecting a secondary spectrum based on the trained model. The adjustment may include selecting an adjustment direction and / or magnitude based on a performance metric parameter space, such as previously described with respect to Fig.9A The adjustment may include determining that a different model should be used, and selecting the spectrum based on the output of the different model.

[0175] In operation 964, the spectral-based solution obtained from the SMO is checked for lithographic compatibility. The solution may be checked for lithographic compatibility using any suitable method, including reference to Fig.9A Operation 914 of the method is described.

[0176] In operation 966, the solution is output. The solution may be output and / or stored using any suitable method, such as by referring to Fig.9A Operation 916 is described.

[0177] As described above, method 900 (and / or other methods and systems described herein) is configured to use a model to adjust the spectrum during spectrally-aware optimization of a lithography process.

[0178] Fig.101 is a schematic overview 1000 of spectral-aware optimization with pattern classification. A trained model 1010 operates on an input 1002 to classify a pattern 1004 of a lithography process. The trained model 1010 may be trained in any suitable manner to classify the pattern based on any suitable classification. The trained model 1010 may operate based on unsupervised learning, where the trained model 1010 may identify a category. The trained model 1010 may also operate based on supervised learning, where the category may be identified in the training data. The trained model 1010 may classify the pattern into one or more classification outputs 1020. The trained model 1010 may determine a classification confidence for the pattern 1004 of the input 1002. The trained model 1010 may classify the pattern 1004 of the input 1002 using multi-class classification. The pattern 1004 may be identified as corresponding to two or more categories, a union of categories, an intersection of categories, and the like. In a non-limiting illustrative example, pattern 1004 of input 1002 may be classified as corresponding to one or more of categories 1022a - 1022n .

[0179] For each of the categories in the classification output 1020, one or more processes for spectrum perception optimization can be performed. In a non-limiting illustrative example, a pattern 1004 identified as corresponding to a category 1022a can be directly associated with a spectrum 1036a and a performance metric 1034a. The pattern 1004 of the category 1022a can be associated with the spectrum 1036a based on a brute force method, for which a plurality of spectra 1036a are optimized and the performance metric 1034a is determined. The pattern 1004 of the category 1022a can be a pattern for which the optimal spectrum is known based on prior experience or modeling. For example, the pattern 1004 of the category 1022a can be a pattern for which a single peak spectrum with minimum FWHM is the optimal spectrum. The wavelength of the spectrum may or may not be variable. In this example, the performance metric 1034a can be determined for each pattern 1004 of the category 1022a, or can be determined for the pattern 1004 of the category 1022a as a category.

[0180] In a non-limiting illustrative example, the pattern 1004 identified as corresponding to the category 1022b can be associated with one or more spectra of the spectral library 1038. The spectral library 1038 can include spectra for which performance metrics have been pre-calculated. The spectral library 1038 can include spectra associated with one or more patterns 1004 for which performance metrics have been calculated. Spectra 1036b can be selected from the spectral library 1038 based on the patterns of the spectral library, wherein each of the patterns corresponding to the category 1022b is most closely similar to the spectral library. More than one spectrum can be selected from the spectral library 1038, and the spectrum 1036b can be determined based on the composition, interpolation, averaging, etc. of the selected spectrum. Spectrum 1036b can be selected from the spectral library 1038 based on the performance metrics associated with the spectra of the spectral library. Spectrum 1036b can be a performance metric 1034b selected from the spectral library having spectrum 1036b.

[0181] In a non-limiting illustrative example, patterns 1004 identified as corresponding to categories 1022n may be associated with one or more spectra based on a trained model 1040. The trained model may output spectra 1036c and performance metrics 1034c based on any suitable method, such as by reference to Fig.9A and 9B Described.

[0182] For a pattern 1004 that is identified as corresponding to more than one category, an optimal spectrum can be determined based on a method associated with the one or more categories to which the pattern 1004 is identified. For example, the spectrum can be determined by a first method and a second method, wherein the optimal spectrum can be determined based on the first method, the second method, or a combination thereof. The optimal spectrum can be selected based on which method corresponds to a better performance metric. The optimal spectrum can be selected based on a combination of spectra generated by various methods. The optimal spectrum can be selected based on a confidence interval or probability of one or more spectra.

[0183] Fig.11A Example spectra for spectrally-aware optimization of an example lithography process are depicted. Fig.11AAn example graph 1102 is depicted, which depicts a set of spectra for an example lithography process. The example graph 1102 depicts spectra with a single peak (e.g., a single component) such as spectra 1110, 1112, 1114, 1116, and 1118 and spectra with dual peaks (e.g., dual components) such as spectra 1120, 1122, 1124, 1126, and 1128. The example graph depicts spectra with various peak distributions, including higher and lower FWHMs. For example, spectrum 1110 has a smaller FWHM than spectrum 1118 co-located therewith. Example graph 1104 depicts an optimal spectrum in the set of spectra of graph 1102 for an example lithography process. Based on a performance metric, spectrum 1124 is an optimal spectrum for the lithography process. Spectrum 1124 is both a dual-component spectrum and not a dual-component spectrum with a minimum FWHM. Small FWHM is traditionally thought to be associated with an optimal spectrum, but for spectrally-aware optimization of lithography processes, this may not be the case. Spectrum 1124 is also not a dual component with maximum FWHM, suggesting that the relationship between performance metrics and FWHM may be more complex than previously understood.

[0184] Fig. 11B Depicted is a diagram showing that due to Fig.11A FIG. 5 is a graph showing process window improvement resulting from spectral-aware optimization of an example lithography process. Fig. 11B Depicted display Fig.11A The performance metrics, exposure window area 1152, nominal focus 1154, center of focus 1156, depth of focus 1158, exposure latitude 1160, MEEF 1162, and NIFS at DoF 1164 are shown for Fig.11A The first row of the chart (row 1166) corresponds to Fig.11A Spectrum 1110 of . Row 1170 corresponds to Fig.11A As depicted in graph 1150, the performance metrics of spectrum 1124 are compared with those of the baseline Fig.11A Spectrum 1110 shows an nDOF enhancement of over 50%.

[0185] Fig.12 Depicted are example spectra determined by spectrally-aware optimization of an example lithography process. Fig.12 A graph 1202 including a spectrum 1210 is depicted. Fig.12Also depicted is a graph 1212 including a spectrum 1220. Spectrum 1220 is composed of multiple components. Spectrum 1220 may be a free spectrum, where the free spectrum may include many components. A free spectrum may also correspond to a spectrum whose intensity is determined for substantially all wavelengths and then composed of components available from a radiation source. A free spectrum may be a digital waveform, such as a sawtooth, a sine, or the like, or other wavelength distributions that are not normally produced by radiation excitation. Spectra 1210 and spectrum 1220 may be generated by a single radiation source or multiple radiation sources. The radiation source may generate one or more excitations from a material via an optoelectronic circuit system, such as a laser excitation. The radiation source may include an optical circuit system that may modify the wavelength, distribution, intensity, etc. of the components generated by the radiation source. The radiation source may include an optical circuit system that may split the components into parts, each of which may be modified separately. The radiation source may generate multiple peaks from a single component, or may combine multiple components from separate excitations or sources. The radiation source may include filters, beam splitters, diffraction elements, etc. that modify the optical properties of one or more components of the spectrum.

[0186] The embodiments may also be described using the following terms:

[0187] 1. A method comprising:

[0188] Acquiring, by a computer system, a plurality of design variables representing at least one of a characteristic of a lithography process, a geometric characteristic of a design layout, or a combination thereof; and

[0189] A spectrum of a radiation source for a lithographic apparatus is determined by a computer system based on at least one of a plurality of design variables, characteristics of a lithographic process, geometric characteristics of a design layout, or a combination thereof.

[0190] 2. The method according to clause 1, wherein determining comprises: determining, by a computer system, one or more parameters for the spectrum of the radiation source.

[0191] 3. A method according to claim 2, wherein one or more parameters of the spectrum of the radiation source include at least one of the number of components, the wavelength spacing between two or more components, the frequency spacing between two or more components, the component shape, the component distribution, the component intensity, the relative intensity of two components, the component nominal focus, the component focal center, the component displacement, or a combination thereof.

[0192] 4. The method according to clause 1, wherein determining comprises:

[0193] A spectrum for the radiation source is determined, by the computer system, based on a tolerance of at least one design variable of the plurality of design variables.

[0194] 5. A method according to clause 4, wherein the tolerance includes the tolerance of at least one of depth of focus, exposure, common process window, image contrast, image logarithmic slope (ILS), normalized image logarithmic slope (NILS), or a combination thereof.

[0195] 6. The method according to clause 1, wherein determining comprises:

[0196] A spectrum for a radiation source is determined, by a computer system, based on at least one lithography metric.

[0197] 7. A method according to claim 6, wherein at least one lithography metric includes at least one of edge placement error (EPE), critical dimension (CD), critical dimension uniformity (CDU), local critical dimension uniformity (LCDU), line edge roughness (LER), line width roughness (LWR), resist profile characteristics, maximum defect size, exposure latitude, image shift, mask error enhancement factor, focus, depth of focus (DOF), DOF tolerance, critical dimension depth of focus, process window tolerance, common process window tolerance, image logarithmic slope (ILS), normalized image logarithmic slope (NILS), image contrast, image contrast latitude, available process window above a specific NILS threshold (nDOF), or a combination thereof.

[0198] 8. The method according to clause 1, wherein determining comprises:

[0199] determining, by a computer system, a spectrum for a radiation source from a plurality of spectrums based on at least one of a lithography metric, a design variable tolerance, or a combination thereof,

[0200] The plurality of spectra include spectra for which at least one value of a lithography metric, a design variable tolerance, or a combination thereof has been pre-calculated.

[0201] 9. The method according to clause 8, wherein determining comprises:

[0202] selecting, by a computer system, a plurality of spectra from a plurality of spectra; and

[0203] A spectrum for the radiation source is determined based on a combination of several spectra.

[0204] 10. The method according to clause 1, wherein determining comprises:

[0205] A spectrum for a radiation source is determined by a computer system based on a machine learning model trained to determine an optimal spectrum through at least one of a plurality of design variables, characteristics of a lithography process, geometric characteristics of a design layout, or a combination thereof.

[0206] 11. A method according to clause 10, wherein the machine learning model is trained to determine the optimal spectrum based on at least one lithography metric.

[0207] 12. A method according to clause 10, wherein the machine learning model is further trained to generate an optimal spectrum based on a combination of multiple contributing components.

[0208] 13. The method according to clause 12, wherein the combination comprises a linear combination.

[0209] 14. The method of clause 12, wherein the combining comprises a convolution of at least two of the plurality of contributing components.

[0210] 15. The method according to clause 12, wherein combining comprises interpolation.

[0211] 16. The method of clause 12, wherein at least one contributing component of the plurality of contributing components is of Lorentzian type.

[0212] 17. The method of clause 12, wherein at least one of the plurality of contributing components is Gaussian.

[0213] 18. The method of clause 12, wherein at least one contribution component of the plurality of contribution components comprises a Focke profile.

[0214] 19. The method according to clause 10, wherein the optimal spectrum is a free spectrum.

[0215] 20. The method according to clause 10, wherein the optimal spectrum has at least one of a smooth profile, a discontinuous profile, a non-differentiable profile, or a piecewise combination thereof.

[0216] 21. A method comprising:

[0217] Acquire multiple target patterns;

[0218] acquiring a plurality of design variables representing characteristics of a lithography process for a plurality of target patterns;

[0219] acquiring a plurality of spectra for a radiation source used in a photolithography process;

[0220] determining lithography metrics for a plurality of light spectra for the plurality of target patterns based on a plurality of design variables characterizing the lithography process for the plurality of target patterns; and

[0221] Using lithography metrics for multiple spectra, a model is trained to determine one or more optimal spectra for a lithography process based on an input target pattern, a set of design variables that characterize the lithography process for the input target pattern, or a combination thereof.

[0222] 22. A method comprising:

[0223] Acquire multiple target patterns;

[0224] acquiring a plurality of laser spectra for a radiation source used in a photolithography process;

[0225] generating a training data set including lithography metrics by computing a source mask optimization for a lithography process for a plurality of target patterns and a plurality of laser spectra; and

[0226] A model is trained by a computing system using a training data set to determine one or more optimal laser spectra for a lithography process, wherein the model is a machine learning model.

[0227] 23. A method according to clause 21, wherein the model comprises a machine learning model.

[0228] 24. A method according to any of clauses 22 or 23, wherein the machine learning model comprises a neural network.

[0229] 25. A method according to any of clauses 22 or 23, wherein the machine learning model comprises a convolutional neural network.

[0230] 26. A method according to any of clauses 21 or 22, wherein the model comprises a regression model.

[0231] 27. A method according to any of clauses 21 or 22, wherein the model comprises a collection of machine learning models.

[0232] 28. A method according to any of clauses 21 or 22, wherein the training comprises:

[0233] The model is trained to determine the optimal spectrum.

[0234] 29. The method according to clause 28, wherein the training comprises:

[0235] The model is trained to determine an optimal spectrum based on a combination of one or more optimal spectra.

[0236] 30. A method according to any of clauses 21 or 22, wherein the lithography metrics include edge placement error (EPE), critical dimension (CD), critical dimension uniformity (CDU), local critical dimension uniformity (LCDU), line edge roughness (LER), line width roughness (LWR), resist profile characteristics, maximum defect size, exposure latitude, image shift, mask error enhancement factor, focus, depth of focus (DOF), DOF tolerance, critical dimension depth of focus, process window tolerance, common process window tolerance, image logarithmic slope (ILS), normalized image logarithmic slope (NILS), available process window above a specific NILS threshold (nDOF) or at least one of a combination thereof.

[0237] 31. The method of any of clauses 21 or 22, wherein training the model to determine one or more optimal spectra for a lithography process comprises training the model to determine one or more optimal parameters for a spectrum of the lithography process.

[0238] 32. A method according to claim 31, wherein one or more optimal parameters include at least one of the number of components, the wavelength spacing between two or more components, the frequency spacing between two or more components, the component shape, the component distribution, the component intensity, the relative intensity of two components, the component nominal focus, the component focal center, the component displacement, or a combination thereof.

[0239] 33. A method according to any of clauses 21 or 22, wherein the lithography metrics include performance indicators.

[0240] 34. The method according to clause 21, further comprising:

[0241] obtaining a production target pattern, a set of design variables representing characteristics of a lithography process that produces the target pattern, or a combination thereof; and

[0242] One or more optimal spectra for a lithography process that produces a target pattern are determined based on the trained model.

[0243] 35. The method according to clause 22, further comprising:

[0244] acquiring a production target pattern; and

[0245] One or more optimal spectra for a lithography process that produces a target pattern are determined based on the trained model.

[0246] 36. A method according to any of clauses 21 or 22, wherein training comprises training the model to determine one or more optimal spectra based on an input target pattern type.

[0247] 37. A method according to clause 36, wherein training comprises training the model to determine the input target pattern type.

[0248] 38. A method comprising:

[0249] calculating, by a computer system, a multivariate cost function of a plurality of design variables representing characteristics of a lithographic process, the multivariate cost function being a function of a spectrum of a radiation source of the lithographic apparatus, or being a function of the spectrum or of variables affecting the spectrum; and

[0250] One or more characteristics of the lithography process are reconfigured by the computer system by adjusting one or more design variables of the plurality of design variables until a termination criterion is met.

[0251] 39. A method comprising:

[0252] calculating, by a computer system, a multivariate cost function that is a function of a plurality of design variables that characterize a lithography process; and

[0253] reconfiguring, by a computer system, one or more characteristics of a lithography process by adjusting one or more design variables of a plurality of design variables and recalculating a multivariate cost function based on the adjusted one or more design variables of the plurality of design variables until a specific termination condition is met,

[0254] Wherein the spectrum of a radiation source used in the lithographic apparatus is adjustable during reconfiguration.

[0255] 40. A method according to clause 38 or 39, wherein the reconfiguration is performed under the constraint that the geometrical properties of the patterned device including the design layout of the lithographic process remain substantially unchanged.

[0256] 41. A method according to clause 38 or 39, wherein reconfiguring comprises adjusting the spectrum of the radiation source.

[0257] 42. A method according to clause 38 or 39, wherein reconfiguring comprises selecting the spectrum of the radiation source from a plurality of spectra of the radiation source.

[0258] 43. A method according to clause 38 or 39, wherein the spectrum of the radiation source comprises a plurality of contributing components.

[0259] 44. A method according to clause 43, wherein the spectrum comprises a combination of contributing components.

[0260] 45. The method of clause 43, wherein at least one of the plurality of contributing components is of Lorentzian type.

[0261] 46. ​​The method of clause 43, wherein at least one of the plurality of contributing components is Gaussian.

[0262] 47. The method of clause 43, wherein at least one of the plurality of contributing components comprises a Focke profile.

[0263] 48. A method according to clause 38 or 39, wherein the spectrum for the radiation source has at least one of a smooth profile, a discontinuous profile, a non-differentiable profile, or a segmented combination thereof.

[0264] 49. A method according to clause 38 or 39, wherein the reconfiguration improves image quality characteristics of the lithography process.

[0265] 50. The method of clause 38 or 39, wherein the reconfiguration increases the tolerance of at least one design variable of the plurality of design variables.

[0266] 51. The method of clause 50, wherein the latitude comprises a latitude of at least one of depth of focus, exposure, common process window, image contrast, image log slope (ILS), normalized image log slope (NILS), or a combination thereof.

[0267] 52. A method according to clause 38 or 39, wherein the multivariate cost function evaluates at least one of edge placement error (EPE), critical dimension (CD), critical dimension uniformity (CDU), local critical dimension uniformity (LCDU), line edge roughness (LER), line width roughness (LWR), resist profile characteristics, maximum defect size, exposure latitude, image shift, mask error enhancement factor, focus, depth of focus (DOF), DOF tolerance, critical dimension depth of focus, process window tolerance, common process window tolerance, image logarithmic slope (ILS), normalized image logarithmic slope (NILS), available process window above a specific NILS threshold (nDOF), or a combination thereof.

[0268] 53. A method according to clause 38 or 39, wherein the reconfiguration comprises optimization of a multivariate cost function.

[0269] 54. A method according to clause 38 or 39, wherein the reconfiguration comprises gradient-based optimization.

[0270] 55. One or more non-transitory machine-readable media having instructions thereon that, when executed by a processor, are configured to perform the method of any of clauses 1 to 54.

[0271] 56. A system comprising:

[0272] Processor; and

[0273] One or more non-transitory machine-readable media having instructions thereon that, when executed by a processor, are configured to perform the method of any of clauses 1 to 54.

[0274] 57. A system comprising:

[0275] Radiation sources;

[0276] a processor operably connected to control the spectrum for the radiation source; and

[0277] One or more non-transitory machine-readable media having instructions thereon that, when executed by a processor, are configured to:

[0278] obtaining a plurality of design variables representing at least one of a characteristic of a lithography process, a geometric characteristic of a design layout, or a combination thereof;

[0279] determining a spectrum for the radiation source based on at least one of a plurality of design variables, characteristics of the lithography process, geometric characteristics of the design layout, or a combination thereof; and

[0280] The radiation source is controlled to emit a spectrum of light during the photolithography process.

[0281] 58. A system according to clause 57, wherein the radiation source comprises a laser.

[0282] 59. The system of clause 57, wherein the radiation source comprises a plurality of radiation sources.

[0283] 60. The system of clause 59, further comprising a multiplexer, wherein the multiplexer combines outputs of the plurality of radiation sources to generate the spectrum.

[0284] 61. The system of clause 57, wherein the instructions for controlling the radiation source further comprise instructions for controlling the wavelength distribution of the spectral components.

[0285] Fig.13 is a block diagram of an example computer system CS according to an embodiment. The computer system CS can assist in implementing the methods, processes or devices disclosed herein. The computer system CS includes a bus BS or other communication mechanisms for transmitting information and a processor PRO (or multiple processors) coupled to the bus BS for processing information. The computer system CS also includes a main memory MM, such as a random access memory (RAM) or other dynamic storage device, which is coupled to the bus BS for storing information and instructions to be executed by the processor PRO. For example, the main memory MM can also be used to store temporary variables or other intermediate information during the execution of the instructions to be executed by the processor PRO. The computer system CS includes a read-only memory (ROM) ROM or other static storage device, which is coupled to the bus BS for storing static information and instructions of the processor PRO. Storage devices SD such as magnetic disks or optical disks are provided and coupled to the bus BS for storing information and instructions.

[0286] The computer system CS can be coupled to a display DS via a bus BS, such as a cathode ray tube (CRT) or a flat panel or touch panel display, for displaying information to a computer user. An input device ID including alphanumeric keys and other keys is coupled to the bus BS for passing information and command selections to the processor PRO. Another type of user input device is a cursor control CC, such as a mouse, trackball or cursor direction keys, for passing direction information and command selections to the processor PRO and controlling cursor movement on the display DS. The input device typically has two degrees of freedom on two axes (a first axis (e.g., x) and a second axis (e.g., y)), allowing the device to specify a position in a plane. A touch panel (screen) display can also be used as an input device.

[0287] In some embodiments, in response to one or more sequences of one or more instructions included in the processor PRO execution main memory MM, the part of one or more methods described herein can be performed by the computer system CS. Such instructions can be read from another computer readable medium (such as storage device SD) into the main memory MM. The instruction sequence included in the execution main memory MM causes the processor PRO to perform the process steps described herein. One or more processors in the multi-processing arrangement can also be adopted to execute the instruction sequence included in the main memory MM. In some embodiments, the hard-wired circuit system can replace the software instruction use or be used in combination with the software instruction. Therefore, the description herein is not limited to any specific combination of hardware circuit system and software.

[0288] The term "computer-readable medium" used herein refers to any medium that participates in providing instructions to the processor PRO for execution. This medium can take many forms, including but not limited to non-volatile media, volatile media and transmission media. Non-volatile media include, for example, optical disks or disks, such as storage devices SD. Volatile media include dynamic memory, such as main memory MM. Transmission media include coaxial cables, copper wires and optical fibers, including wires that constitute bus BS. 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. Computer-readable media can be non-transient, such as floppy disks, foldable disks, hard disks, 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 memory cartridges. Non-transient computer-readable media can have instructions recorded thereon. When executed by a computer, instructions can implement any feature described herein. Transient computer-readable media can include carrier waves or other propagating electromagnetic signals.

[0289] Various forms of computer-readable media may be involved in carrying one or more sequences of one or more instructions to the processor PRO for execution. For example, the instructions may initially be carried on a disk of a remote computer. The remote computer may load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. The modem local to the computer system CS may receive data on the telephone line and convert the data into an infrared signal using an infrared transmitter. An infrared detector coupled to the bus BS may receive the data carried in the infrared signal and place the data on the bus BS. The bus BS carries the data to the main memory MM, from which the processor PRO retrieves and executes the instructions. Before or after execution by the processor PRO, the instructions received by the main memory MM may optionally be stored on a storage device SD.

[0290] The computer system CS may also include a communication interface CI coupled to the bus BS. The communication interface CI provides a bidirectional data communication coupling with a network link NDL connected to the local network LAN. For example, the communication interface CI may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection with a corresponding type of telephone line. As another example, the communication interface CI may be a local area network (LAN) card to provide a data communication connection with a compatible LAN. A wireless link may also be implemented. In any such implementation, the communication interface CI sends and receives electrical, electromagnetic or optical signals that carry digital data streams representing various types of information.

[0291] The network link NDL typically provides data communication to other data devices through one or more networks. For example, the network link NDL can provide a connection to a host computer HC through a local network LAN. This can include data communication services provided through the global packet data communication network (now generally referred to as the "Internet" INT). The local network LAN (Internet) all use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network data link NDL and through the communication interface CI (carrying digital data to and from the computer system CS) are exemplary forms of carrier waves that transport information.

[0292] The computer system CS can send messages and receive data, including program code, through (one or more) networks, network data links NDL and communication interfaces CI. In the Internet example, the host computer HC can send the request code of the application program through the Internet INT, the network data link NDL, the local network LAN and the communication interface CI. For example, one such download application can provide all or part of the method described herein. The received code can be executed by the processor PRO when it is received, and / or stored in the storage device SD or other non-volatile storage device for later execution. In this way, the computer system CS can obtain the application code in the form of a carrier wave.

[0293] Fig.14 is a schematic diagram of a lithographic projection apparatus according to one or more embodiments. The lithographic projection apparatus may include an illumination system IL, a first stage MT, a second stage WT and a projection system PS.

[0294] The illumination system IL may condition the radiation beam B. In this particular case, the illumination system also includes a radiation source SO.

[0295] A first stage (eg, patterning device stage) MT may be provided with a patterning device holder to hold a patterning device MA (eg, a reticle), and connected to a first positioner PM to accurately position the patterning device relative to the article PS.

[0296] A second workpiece table (substrate table) WT may be provided with a substrate holder to hold a substrate W (eg a silicon wafer coated with resist) and connected to a second positioner PW to accurately position the substrate relative to the item PS.

[0297] A projection system ("lens") PS (eg, a refractive, reflective, or catadioptric optical system) can image the irradiated portion of the patterning device MA onto a target portion C of the substrate W (eg, comprising one or more dies).

[0298] As depicted, the device may be transmissive (i.e., having a transmissive patterning device). However, in general, it may also be reflective, for example (having a reflective patterning device). The device may employ patterning devices of a different kind than a classical mask; examples include a programmable mirror array or an LCD matrix.

[0299] A source SO (e.g. a mercury lamp or an excimer laser, an LPP (laser produced plasma) EUV source) generates a radiation beam. This beam is fed into an illumination system (illuminator) IL, for example directly or after traversing a conditioning device such as a beam expander. The illuminator IL may comprise an adjustment device AD ​​for setting the outer and / or inner radial extent of the intensity distribution in the beam (commonly referred to as σ outer and σ inner, respectively). In addition, it will typically comprise 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.

[0300] In some embodiments, the source SO may be within the housing of the lithographic projection device (e.g. this is usually the case when the source SO is a mercury lamp), but it may also be remote from the lithographic projection device, the radiation beam it generates being fed into the device (e.g. with the aid of suitable directional reflectors); this latter scenario may be the case when the source SO is an excimer laser (e.g. based on KrF, ArF or F2 laser irradiation).

[0301] The beam B can then intercept the patterning device MA held on the patterning device table MT. After traversing the patterning device MA, the beam B can pass through a lens that focuses the beam B onto a target portion C of the substrate W. With the aid of a second positioning device (and an interferometric measurement device IF), the substrate table WT can be accurately moved, for example to position different target portions C in the path of the beam B. Similarly, the first positioning device can be used to accurately position the patterning device MA relative to the path of the beam B, for example after mechanically retrieving the patterning device MA from a patterning device library or during scanning. Typically, movement of the stage MT, WT can be achieved with the aid of a long-stroke module (coarse positioning) and a short-stroke module (fine positioning). However, in the case of a stepper (as opposed to a step-and-scan tool), the patterning device table MT can be just connected to a short-stroke actuator, or can be fixed.

[0302] The depicted tool can be used in two different modes: a step mode and a scan mode. In the step mode, the patterning device table MT remains substantially stationary and the entire patterning device image is projected onto the target portion C at once (i.e., a single "flash"). The substrate table WT can be shifted in the x and / or y direction so that a different target portion C can be irradiated by the beam PB.

[0303] In scan mode, essentially the same scenario applies, except that 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 used to scan over the patterning device image; concurrently, 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 (typically M=1 / 4 or 1 / 5). In this way, a relatively large target portion C can be exposed without having to sacrifice resolution.

[0304] Fig.15 is a schematic diagram of another lithographic projection apparatus (LPA) according to one or more embodiments. The LPA may include a source collector module SO, an illumination system (illuminator IL) configured to condition a radiation beam B (e.g. EUV radiation), a support structure MT, a substrate table WT and a projection system PS.

[0305] The support structure (eg, patterning device table) MT may be configured to support a patterning device (eg, mask or reticle) MA and be connected to a first positioner PM (configured to accurately position the patterning device).

[0306] The substrate table (eg, wafer stage) WT may be configured to hold a substrate (eg, a resist-coated wafer) W, and be connected to a second positioner PW (configured to accurately position the substrate).

[0307] The projection system (eg, a reflective projection system) PS may be configured to project the pattern imparted to the radiation beam B by the patterning device MA onto a target portion C of the substrate W (eg, comprising one or more dies).

[0308] As depicted here, the LPA can be reflective (e.g., using a reflective patterning device). Note that because most materials are absorptive in the EUV wavelength range, the patterning device can have a multilayer reflector that includes 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. Even smaller wavelengths can be produced with x-ray lithography. Since most materials are absorptive at both EUV and x-ray wavelengths, a thin sheet 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).

[0309] The illuminator IL can receive an extreme ultraviolet radiation beam from a source collector module SO. Methods of 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) that has one or more emission lines in the EUV range. In one such method, generally referred to as laser produced plasma ("LPP"), a plasma can be generated by irradiating a fuel (such as a droplet, stream, or cluster of a material having a line emitting element) with a laser beam. The source collector module SO can be part of an EUV radiation system that includes a laser for providing a laser beam to excite the fuel. The resulting plasma emits output radiation, such as EUV radiation, which is collected using a radiation collector disposed in the source collector module. The laser and the source collector module can be separate entities, such as when a CO2 laser is used to provide a laser beam for fuel excitation.

[0310] In this case, the laser may not be considered to form part of the lithographic apparatus, and the radiation beam may be delivered from the laser to the source collector module with the aid of a beam delivery system including, for example, suitable directing mirrors and / or a beam expander. In other cases, the source may be an integrated part of the source collector module, for example when the source is a discharge produced plasma EUV generator, often referred to as a DPP source.

[0311] The illuminator IL may include an adjuster for adjusting the angular intensity distribution of the radiation beam. Typically, at least the outer and / or inner radial extent of the intensity distribution in a pupil plane of the illuminator (commonly referred to as σ outer and σ inner, respectively) may be adjusted. In addition, the illuminator IL may include various other components, such as a facet field and a pupil mirror arrangement. The illuminator may be used to adjust the radiation beam to have a desired uniformity and intensity distribution in its cross-section.

[0312] A radiation beam B may be incident on and patterned by a patterning device (e.g. a mask) MA, which is held on a support structure (e.g. a patterning device table) MT. After reflection from the patterning device (e.g. a mask) MA, the radiation beam B passes through a projection system PS, which focuses the beam onto a target portion C of a substrate W. With the aid of a second positioner PW and a position sensor PS2 (e.g. an interferometric device, a linear encoder or a capacitive sensor), the substrate table WT may be accurately moved, for example to position different target portions C in the path of the radiation beam B. Similarly, a first positioner PM and a further position sensor PS1 may 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 may be aligned using the patterning device alignment marks M1, M2 and the substrate alignment marks P1, P2.

[0313] The depicted device LPA can be used in at least one of the following modes: a stepping mode, a scanning mode and a stationary mode.

[0314] In step mode, the support structure (e.g. patterning device table) MT and the substrate table WT remain substantially stationary while an entire pattern imparted to the radiation beam is projected at one time onto a target portion C (i.e. a single static exposure). The substrate table WT is then shifted in the X and / or Y direction so that a different target portion C can be exposed.

[0315] 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 C (i.e. a single dynamic exposure). The speed and direction of the substrate table WT relative to the support structure (e.g. patterning device table) MT may be determined by the (de-)magnification and image reversal characteristics of the projection system PS.

[0316] In stationary mode, the support structure (e.g., patterning device table) MT remains essentially 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 a target portion C. In this mode, typically a pulsed radiation source is employed, 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 can be readily applied to maskless lithography, which utilizes a programmable patterning device (such as a programmable mirror array).

[0317] Fig.16 Detailed view of a lithographic projection apparatus according to one or more embodiments. As shown, the LPA may include a source collector module SO, an irradiation system IL, and a projection system PS. The source collector module SO is configured so that a vacuum environment can be maintained in an enclosed structure ES of the source collector module SO. The EUV radiation emitting thermal plasma HP may be formed by a plasma source generated by a discharge. EUV radiation may be generated by a gas or vapor, such as xenon, lithium vapor, or tin vapor, wherein the thermal plasma HP is created to emit radiation in the EUV range of the electromagnetic spectrum. The thermal plasma HP is created, for example, by a discharge that results in at least partially ionized plasma. In order to efficiently generate radiation, a partial pressure of, for example, Xe, Li, Sn vapor, or any other suitable gas or vapor of 10 Pa may be required. In an embodiment, a plasma that excites tin (Sn) is provided to generate EUV radiation.

[0318] The radiation emitted by the hot plasma HP is transferred from the source chamber SC to the collector chamber CC via an optional gas barrier or contaminant trap CT (also referred to as a contaminant barrier or fin trap in some cases) positioned in or behind an opening in the source chamber SC. The contaminant trap CT may include a channel structure. The contaminant trap CT may include a channel structure. The contaminant trap CT may also include a gas barrier or a combination of a gas barrier and a channel structure. As known in the art, the contaminant trap or contaminant barrier CT further indicated herein includes at least a channel structure.

[0319] The collector chamber CC 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 US and a downstream radiation collector side DS. Radiation traversing the radiation collector CO may be reflected off by a grating spectral filter SF to be focused in a virtual source point IF along the optical axis indicated by the dash-dot line 'O'. The virtual source point IF may be referred to as an intermediate focus, and the source collector module may be arranged such that the intermediate focus IF is located at or near an opening OP in the enclosed structure ES. The virtual source point IF is an image of the radiation emitting plasma HP.

[0320] The radiation then traverses an illumination system IL which may include a faceted field mirror arrangement FM and a faceted pupil mirror arrangement PM which are arranged to provide a desired angular distribution of the radiation beam B at the patterning device MA and a desired uniformity of the amplitude of the radiation at the patterning device MA. Upon reflection of the radiation beam B at the patterning device MA held by the support structure MT, a patterned beam PB is formed and the patterned beam PB is imaged by the projection system PS via a reflective element RE onto a substrate W held by a substrate table WT.

[0321] 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 SF may optionally be present. Further, there may be more mirrors than shown in the drawings, for example there may be 1 to 6 additional reflective elements in the projection system PS.

[0322] The collector optics CO may be a nested collector with a grazing incidence reflector GR, like the example of a collector (or collector mirror). The grazing incidence reflector GR is arranged axially symmetrically around the optical axis O, and this type of collector optics CO may be used in combination with a discharge produced plasma source, usually called a DPP source.

[0323] Fig.17 Detailed view of a source collector module SO of a lithographic projection apparatus LPA according to one or more embodiments. The source collector module SO may be part of a radiation system of the LPA. The laser LA may be arranged to deposit laser energy into a fuel, such as xenon (Xe), tin (Sn) or lithium (Li), thereby creating a highly ionized plasma HP with an electron temperature of several tens of eV. Energetic radiation generated during deexcitation and recombination of these ions is emitted from the plasma, collected by a collector optics CO at near normal incidence and focused onto an opening OP in an enclosed structure ES.

[0324] The concepts disclosed herein can simulate or mathematically model any general imaging system for imaging sub-wavelength features, and may be particularly useful for emerging imaging technologies that are capable of producing shorter and shorter wavelengths. Emerging technologies already in use include EUV (extreme ultraviolet), DUV lithography, which is capable of producing wavelengths of 193nm using ArF lasers, and even 157nm using fluorine lasers. Moreover, EUV lithography is capable of producing wavelengths in the range of 20 to 50nm by using synchrotrons or by hitting materials (solid or plasma) with high-energy electrons to produce photons in this range.

[0325] Although the concepts disclosed herein can be used for imaging on substrates such as silicon wafers, it should be understood that the disclosed concepts can be used with any type of lithography imaging system, such as those used for imaging on substrates other than silicon wafers. In addition, combinations and subcombinations of the disclosed elements may include separate embodiments. For example, the addition of a single or multiple assist features described herein may include their own separate embodiments, or they may be included with one or more other embodiments described herein.

[0326] The above description is intended to be illustrative, rather than limiting. It will therefore be apparent to those skilled in the art that modifications may be made as described without departing from the scope of the claims set forth below.

Claims

1. A method comprising: Acquire multiple target patterns; acquiring a plurality of laser spectra for a radiation source used in a photolithography process; generating a training data set including lithography metrics by calculating a source mask optimization of the lithography process for the plurality of target patterns and the plurality of laser spectra; as well as The training data set is used by a computing system to train a model to determine one or more optimal laser spectra for the lithography process, wherein the model is a machine learning model.

2. The method of claim 1, wherein the machine learning model comprises a neural network.

3. The method of claim 1, wherein the machine learning model comprises a convolutional neural network. The method of claim 1 , wherein the model comprises a regression model.

5. The method of claim 1, wherein the model comprises an ensemble of machine learning models.

6. The method of claim 1, wherein the training comprises: The model is trained to determine the optimal spectrum.

7. The method according to claim 6, wherein the training comprises: The model is trained to determine an optimal spectrum based on a combination of the one or more optimal spectra.

8. The method of claim 1 , wherein the lithography metrics include at least one of edge placement error (EPE), critical dimension (CD), critical dimension uniformity (CDU), local critical dimension uniformity (LCDU), line edge roughness (LER), line width roughness (LWR), resist profile characteristics, maximum defect size, exposure latitude, image shift, mask error enhancement factor, focus, depth of focus (DOF), DOF latitude, critical dimension depth of focus, process window latitude, common process window latitude, image logarithmic slope (ILS), normalized image logarithmic slope (NILS), available process window above a specific NILS threshold (nDOF), or a combination thereof.

9. The method of claim 1 , wherein training the model to determine one or more optimal spectra for the lithography process comprises: The model is trained to determine one or more optimal parameters of the spectrum for the lithography process.

10. The method of claim 9, wherein the one or more optimal parameters include at least one of: the number of components, the wavelength spacing between two or more components, the frequency spacing between two or more components, the component shape, the component distribution, the component intensity, the relative intensity of two components, the component nominal focus, the component focal center, the component displacement, or a combination thereof. The method of claim 1 , wherein the lithography metric comprises a performance index.

12. The method according to claim 1, further comprising: Acquire production target pattern; as well as One or more optimal spectra for the photolithography process for producing a target pattern are determined based on the trained model.

13. The method of claim 1, wherein the training comprises: The model is trained to determine one or more optimal spectra based on an input target pattern type.

14. The method of claim 13, wherein the training comprises: The model is trained to determine the input target pattern type.

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