Patent Description:
The description herein relates generally to processing images acquired by an inspection or measurement tool, and more particularly, related to image denoising by using machine learning.

A lithographic projection apparatus can be used, for example, in the manufacture of integrated circuits (ICs). In such a case, a patterning device (e.g., a mask) may contain or provide a pattern corresponding to an individual layer of the IC ("design layout"), and this pattern can be transferred onto a target portion (e.g. comprising one or more dies) on a substrate (e.g., silicon wafer) that has been coated with a layer of radiation-sensitive material ("resist"), by methods such as irradiating the target portion through the pattern on the patterning device. In general, a single substrate contains a plurality of adjacent target portions to which the pattern is transferred successively by the lithographic projection apparatus, one target portion at a time. In one type of lithographic projection apparatuses, the pattern on the entire patterning device is transferred onto one target portion in one go; such an apparatus is commonly referred to as a stepper. In an alternative apparatus, commonly referred to as a step-and-scan apparatus, a projection beam scans over the patterning device in a given reference direction (the "scanning" direction) while synchronously moving the substrate parallel or anti-parallel to this reference direction. Different portions of the pattern on the patterning device are transferred to one target portion progressively. Since, in general, the lithographic projection apparatus will have a reduction ratio M (e.g., <NUM>), the speed F at which the substrate is moved will be <NUM>/M times that at which the projection beam scans the patterning device. More information with regard to lithographic devices as described herein can be gleaned, for example, from <CIT>.

Prior to transferring the pattern from the patterning device to the substrate, the substrate may undergo various procedures, such as priming, resist coating and a soft bake. After exposure, the substrate may be subjected to other procedures ("post-exposure procedures"), such as a post-exposure bake (PEB), development, a hard bake and measurement/inspection of the transferred pattern. This array of procedures is used as a basis to make an individual layer of a device, e.g., an IC. The substrate may then undergo various processes such as etching, ion-implantation (doping), metallization, oxidation, chemo-mechanical polishing, etc., all intended to finish off the individual layer of the device. If several layers are required in the device, then the whole procedure, or a variant thereof, is repeated for each layer. Eventually, a device will be present in each target portion on the substrate. These devices are then separated from one another by a technique such as dicing or sawing, whence the individual devices can be mounted on a carrier, connected to pins, etc..

Thus, 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 devices. Such layers and features are typically manufactured and processed using, e.g., deposition, lithography, etch, chemical-mechanical polishing, and ion implantation. Multiple devices may be fabricated on a plurality of dies on a substrate and then separated into individual devices. This device manufacturing process may be considered a patterning process. A patterning process involves a patterning step, such as optical and/or nanoimprint lithography using a patterning device in a lithographic apparatus, to transfer a pattern on the patterning device to a substrate and typically, but optionally, involves one or more related pattern processing steps, such as resist development by a development apparatus, baking of the substrate using a bake tool, etching using the pattern using an etch apparatus, etc..

<NPL>, discloses the use of deep supervised learning for the Poisson denoising of low-dose scanning electron microscope (SEM) images as a step in the estimation of line edge roughness (LER) and line width roughness (LWR).

<CIT> discloses an image noise reduction method using a forward propagation type neural network.

<CIT> discloses generating simulated images from design information.

The invention is defined in the claims. According to an embodiment, there is provided a method according to claim <NUM>.

In an embodiment, there is provided, one or more non-transitory computer-readable media for storing a denoising model. In an embodiment, one or more non-transitory computer-readable media is configured to generate a denoised image via the stored denoising model. In particular, one or more non-transitory computer-readable media stores instructions that, when executed by one or more processors, provides the denoising model. In an embodiment, the denoising model being produced by execute instruction for: obtaining a first set of simulated images based on design patterns (e.g., by using a trained GAN to convert GDS patterns into simulated images); providing the first set of simulated images as input to a base denoising model to obtain a second set of simulated images, the second set of simulated images being denoised images associated with the design patterns; and using reference denoised images as feedback to update one or more configurations of the base denoising model, wherein the one or more configurations are updated based on a comparison between the reference denoised images and the second set of simulated images.

In some embodiments, a GAN is trained to convert GDS pattern images into simulated clean SEM images. Noise features are extracted from scanned SEM images first and then the noise is added to these clean images to generated simulated noisy images. The simulated clean mages and noisy images, in combination with scanned SEM images, are used to train a denoising model. The denoising model may be further fine-tuned with captured SEM images. Once trained, the denoising model is operable to remove noises from input SEM images to generate denoised images.

According to embodiments of the present disclosure, a denoising model is trained by using simulated images that are converted from design patterns through a generator model as described above. Training data comprising such simulated images can collectively cover remarkably and sufficiently more patterns than SEM-captured images. As a result of the improved pattern coverage, the training can advantageously result in significantly improved effectiveness and accuracy of the denoising model. The requirement for retraining can be far reduced or even eliminated.

The above aspects and other aspects and features will become apparent to those ordinarily skilled in the art upon review of the following description of specific embodiments in conjunction with the accompanying figures, wherein:.

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

In the present document, the terms "radiation" and "beam" may be used to encompass all types of electromagnetic radiation, including ultraviolet radiation (e.g. with a wavelength of <NUM>, <NUM>, <NUM>, <NUM> or <NUM>) and EUV (extreme ultra-violet radiation, e.g. having a wavelength in the range of about <NUM>-<NUM>).

The patterning device can comprise, or can form, one or more design layouts. The design layout can be generated utilizing CAD (computer-aided design) programs, this process often being referred to as EDA (electronic design automation). Most CAD programs follow a set of predetermined design rules in order to create functional design layouts/patterning devices. These rules are set by processing and design limitations. For example, design rules define the space tolerance between devices (such as gates, capacitors, etc.) or interconnect lines, so as to ensure that the devices or lines do not interact with one another in an undesirable way. One or more of the design rule limitations may be referred to as "critical dimension" (CD). A critical dimension of a device can be defined as the smallest width of a line or hole or the smallest space between two lines or two holes. Thus, the CD determines the overall size and density of the designed device. Of course, one of the goals in device fabrication is to faithfully reproduce the original design intent on the substrate (via the patterning device).

The pattern layout design may include, as an example, application of resolution enhancement techniques, such as optical proximity corrections (OPC). OPC addresses the fact that the final size and placement of an image of the design layout projected on the substrate will not be identical to, or simply depend only on the size and placement of the design layout on the patterning device. It is noted that the terms "mask", "reticle", "patterning device" are utilized interchangeably herein. Also, person skilled in the art will recognize that, the term "mask," "patterning device" and "design layout" can be used interchangeably, as in the context of RET, a physical patterning device is not necessarily used but a design layout can be used to represent a physical patterning device. For the small feature sizes and high feature densities present on some design layout, the position of a particular edge of a given feature will be influenced to a certain extent by the presence or absence of other adjacent features. These proximity effects arise from minute amounts of radiation coupled from one feature to another or non-geometrical optical effects such as diffraction and interference. Similarly, proximity effects may arise from diffusion and other chemical effects during post-exposure bake (PEB), resist development, and etching that generally follow lithography.

In order to increase the chance that the projected image of the design layout is in accordance with requirements of a given target circuit design, proximity effects may be predicted and compensated for, using sophisticated numerical models, corrections or pre-distortions of the design layout. The article "<NPL>) provides an overview of current "model-based" optical proximity correction processes. In a typical high-end design almost every feature of the design layout has some modification in order to achieve high fidelity of the projected image to the target design. These modifications may include shifting or biasing of edge positions or line widths as well as application of "assist" features that are intended to assist projection of other features.

An assist feature may be viewed as a difference between features on a patterning device and features in the design layout. The terms "main feature" and "assist feature" do not imply that a particular feature on a patterning device must be labeled as one or the other.

The term "mask" or "patterning device" as employed in this text may be broadly interpreted as referring to a generic patterning device that can be used to endow an incoming radiation beam with a patterned cross-section, corresponding to a pattern that is to be created in a target portion of the substrate; the term "light valve" can also be used in this context. Besides the classic mask (transmissive or reflective; binary, phase-shifting, hybrid, etc.), examples of other such patterning devices include:.

As a brief introduction, <FIG> illustrates an exemplary lithographic projection apparatus 10A. Major components are a radiation source 12A, which may be a deep-ultraviolet excimer laser source or other type of source including an extreme ultra violet (EUV) source (as discussed above, the lithographic projection apparatus itself need not have the radiation source), illumination optics which, e.g., define the partial coherence (denoted as sigma) and which may include optics 14A, 16Aa and 16Ab that shape radiation from the source 12A; a patterning device 18A; and transmission optics 16Ac that project an image of the patterning device pattern onto a substrate plane 22A. An adjustable filter or aperture 20A at the pupil plane of the projection optics may restrict the range of beam angles that impinge on the substrate plane 22A, where the largest possible angle defines the numerical aperture of the projection optics NA= n sin(Θmax), wherein n is the refractive index of the media between the substrate and the last element of the projection optics, and Θmax is the largest angle of the beam exiting from the projection optics that can still impinge on the substrate plane 22A.

In a lithographic projection apparatus, a source provides illumination (i.e. radiation) to a patterning device and projection optics direct and shape the illumination, via the patterning device, onto a substrate. The projection optics may include at least some of the components 14A, 16Aa, 16Ab and 16Ac. An aerial image (AI) is the radiation intensity distribution at substrate level. A resist layer on the substrate is exposed and the aerial image is transferred to the resist layer as a latent "resist image" (RI) therein. The resist image (RI) can be defined as a spatial distribution of solubility of the resist in the resist layer. A resist model can be used to calculate the resist image from the aerial image, an example of which can be found in U. Patent Application Publication No. <CIT>. The resist model is related only to properties of the resist layer (e.g., effects of chemical processes which occur during exposure, PEB and development). Optical properties of the lithographic projection apparatus (e.g., properties of the source, the patterning device and the projection optics) dictate the aerial image. Since the patterning device used in the lithographic projection apparatus can be changed, it may be desirable to separate the optical properties of the patterning device from the optical properties of the rest of the lithographic projection apparatus including at least the source and the projection optics.

Although specific reference may be made in this text to the use of lithography apparatus in the manufacture of ICs, it should be understood that the lithography apparatus described herein may have other applications, such as the manufacture of integrated optical systems, guidance and detection patterns for magnetic domain memories, liquid-crystal displays (LCDs), thin film magnetic heads, etc. The skilled artisan will appreciate that, in the context of such alternative applications, any use of the terms "wafer" or "die" herein may be considered as synonymous with the more general terms "substrate" or "target portion", respectively. The substrate referred to herein may be processed, before or after exposure, in for example a track (a tool that typically applies a layer of resist to a substrate and develops the exposed resist) or a metrology or inspection tool. Where applicable, the disclosure herein may be applied to such and other substrate processing tools. Further, the substrate may be processed more than once, for example in order to create a multi-layer IC, so that the term substrate used herein may also refer to a substrate that already contains multiple processed layers.

The terms "radiation" and "beam" used herein encompass all types of electromagnetic radiation, including ultraviolet (UV) radiation (e.g. having a wavelength of <NUM>, <NUM>, <NUM>, <NUM> or <NUM>) and extreme ultra-violet (EUV) radiation (e.g. having a wavelength in the range of <NUM>-<NUM>), as well as particle beams, such as ion beams or electron beams.

Existing training methods for a denoising model requires a large number of images (e.g., SEM images) of a patterned substrate as training data. In such training methods, pattern coverage of design layout is limited to the patterns of the SEM images. In an embodiment, pattern coverage refers to number of unique patterns within a design layout. Typically, a design layout may have hundreds of millions to billions of patterns, and millions of unique patterns. Measuring millions of patterns on a patterned substrate for training purpose is impractical, as it will require substantial amount of metrology time and computing resources for training. As such, for example, training data comprising the SEM images is usually far less than adequate for training a machine learning model. Hence, retraining the trained model with new patterns in real-time may be required.

The methods of the present disclosure have several advantages. For example, pattern coverage of design layout can be increased substantially during offline training. Only limited SEM images (e.g., <NUM>-<NUM> real SEM images) can be used e.g., for training and verification purposes. After training, the trained model can be used at runtime to denoise captured metrology images (e.g., SEM images). As relatively large numbers of patterns are covered during the training, the amount of retraining of the present model will be substantially less than existing models. The fine-tuning of the model can be achieved quickly e.g., by acquiring <NUM> - <NUM> real SEM images. As such, a substantial machine scanning time and online model training time can be saved compared to existing models. For example, with the present method, scanning time can be limited to <NUM> SEM images as opposed to thousands of SEM images and online model training time can be limited to approximately <NUM> hour compared to <NUM>- <NUM> hours.

<FIG> is an exemplary method <NUM> of training a denoising model according to an embodiment of the present disclosure. In an embodiment, for training purposes, another model is used to convert design patterns (e.g., design layout in GDS file data) into simulated clean SEM images. Further, noise is added to these clean images to generate simulated noisy images. The simulated clean images and noisy images, in combination with scanned SEM images, are used to train a denoising model. In an embodiment, the method includes processes P201 and P203 discussed in detail below.

Process P201 includes converting design patterns to a first set of simulated images <NUM>, for example simulated SEM images. In an embodiment, the design patterns DPs are in Graphic Data Signal (GDS) file format. For example, a design layout including millions of design patterns is represented as a GDS data file.

In an embodiment, the obtaining the first set of simulated images <NUM> includes executing a trained model MD1 using the design patterns DPs as an input to generate the simulated images <NUM>. In an embodiment, the trained model MD1 is trained based on the design patterns DPs and captured images of a patterned substrate, each captured image being associated with a design pattern. In an embodiment, the captured images are SEM images are acquired via a scanning electron microscope (SEM) (e.g., <FIG>).

In an embodiment, the trained model MD1 can be any model such as a machine learning model that can be trained using existing training methods using training data as discussed herein. For example, the trained model MD1 can be a convolutional neural network (CNN) or a deep convolutional neural network (DCNN). The present disclosure is not limited to a particular training method or a particular neural network. As an example, the model MD1 may be a first deep leaning model (e.g., DCNN) trained using a training method such as generative adversarial network (GAN), wherein the design patterns DPs and SEM images are used as training data. In this example, the trained model MD1 (e.g., DCNN) is referred as a generative model configured to generate the simulated SEM image from a given design pattern, e.g., a GDS pattern.

In an embodiment, a generative adversarial network (GAN) includes two deep learning model - a generator model (e.g., CNN, or DCNN) and a discriminator model (e.g., another CNN or another DCNN) trained together, particularly in opposition to one another. The generator model can take as input the design patterns DPs and the captured images (e.g., SEM images) and output a simulated image (e.g., simulated SEM image). In an embodiment, the outputted simulated images may be labelled as fake images or real images. In an example, a fake image is an image of a certain class (e.g., denoised image of SEM image) that never actually existed before. On the other hand, a real image used as reference (or ground truth) is a previously existing image (e.g., SEM of a printed substrate) that may be used during the training of the generator model and the discriminator model. The goal of the training is to train the generator model to generate fake images that closely resemble the real image. For example, the features of the fake image are at least <NUM>% match with the features of the real image (e.g., denoised SEM image). Consequently, the trained generator model is capable of generating realistic simulated images with high level of accuracy.

In an embodiment, the generator model (G) may be a convolutional neural network. The generator model (G) takes as input the design patterns (z) and generate an image. In an embodiment, the image may be referred as fake image or a simulated image. The fake image can be expressed as Xfake = G(z). The generator model (G) may be associated with a first cost function. The first cost function enables tuning of parameters of the generator model such that the cost function is improved (e.g., maximized or minimized). In an embodiment, the first cost function comprises a first log-likelihood term that determines a probability that the simulated image is a fake image given the input vector.

An example of the first cost function can be expressed by equation <NUM> below: <MAT>.

In above equation <NUM>, a log likelihood of conditional probability is computed. In the equation, S refers to a source assignment as fake by the discriminator model and X_fake is an output i.e., a fake image of the generator model. Thus, in an embodiment, the training method minimizes the first cost function (L). Consequently, the generator model will generate fake images (i.e., the simulated images) such that the conditional probability that the discriminator model will realize the fake image as fake is low. In other words, the generator model will progressively generate more and more realistic images or patterns.

<FIG> illustrates an exemplary process of training a model (e.g., MD1) as discussed in the process P201. In an embodiment, the trained generator model MD1 is trained using a generative adversarial network. Training based on the generative adversarial network comprises two deep learning model GM1 and DM1 trained together such that a generator model GM1 progressively generates more accurate and robust results.

In an embodiment, the generator model GM1 can take as input, for example, a design patterns DP1 and DP2, and SEM images SEM1 and SEM2 corresponding to the design patterns DP1 and DP2. The generator model GM1 outputs simulated images such as images DN1 and DN2.

The simulated images of GM1 are received by a discriminator model DM1, which is another CNN. The discriminator model DM1 also receives the real images SEM1 and SEM2 (or a set of real patterns) in the form of a pixelated image. Based on the real images, the discriminator model DM1 determines whether the simulated images are fake (e.g., label L1) or real (e.g., label L2) and assigns labels accordingly. When the discriminator model DM1 classifies the simulated images as fake, parameters (e.g., biases and weights) of the GM1 and DM1 are modified based on a cost function (e.g., the first cost function above). The models GM1 and DM1 are iteratively modified until the discriminator DM1 consistently classifies the simulated images generated by GM1 as real. In other words, the generator model GM1 is configured to generate realistic SEM images for any input design patterns.

<FIG> illustrates example of obtaining the first set of simulated SEM images using the trained generator model MD1 of <FIG>, according to an embodiment. In an embodiment, any design pattern DP11, DP12,. DPn can be inputted to the trained generator model MD1 to generate the simulated SEM images S1, S2,. Sn, respectively. Although the present disclosure is not limited thereto, these simulated SEM images S1, S2,. Sn may be clean images that do not include typical noise or includes very low noise.

In some embodiments, an image noise may be added to generate the first set of simulated images <NUM> in <FIG>. <FIG> illustrates an example of adding noise to the simulated SEM images S1, S2,. Sn to generate the first set of simulated images S1*, S2*,. Sn* for training a denoising model which is another machine learning model configured to denoise an input image. The training of the denoising model is discussed with respect to process P203 below.

Process P203 includes training the denoising model uses the first set of simulated images <NUM> and image noise as training data. In an embodiment, additionally, captured images <NUM> of a patterned substrate may be included in the training data. For example, the captured images <NUM> can be SEM images of the patterned substrate.

In an embodiment, the denoising model MD2 is a second machine leaning model. For example, the second machine learning model may be a second CNN or a second DCNN. The present disclosure is not limited to particular deep learning training method or machine learning training methods. In an embodiment, the training is an iterative process performed until the second set of simulated images are within a specified threshold of ground truth such as the first set of simulated images <NUM> (e.g., simulated SEM images S1, S2,. Sn of <FIG>) before adding noise, or reference images. In an embodiment, the training of the denoising model using the first set of simulated image, the image noise, and captured images as training data.

In an embodiment, the image noise is extracted from captured images of the patterned substrate, e.g., captured SEM images. For example, a noise filter may be applied to extract the noise from a SEM image captured by the SEM tool. The extract noise can be represented as the image noise. In an embodiment, the image noise is a Gaussian noise, white noise, salt and pepper noise characterized by user specified parameters. In an embodiment, the image noise includes pixels whose intensity values are statistically independent from each other. In an embodiment, the Gaussian noise can be generated by varying parameters of a Gaussian distribution function.

In an embodiment, referring to <FIG>, the image noise such as the Gaussian noise can be added to, for example, simulated images S1, S2,. Sn to generate the noisy images such as S1*, S2*,. The noisy images are the first set of simulated images <NUM> used to train the denoising model.

Conventionally, a denoising model is trained by using captured SEM images as training images. Limited by imaging throughput of SEM systems and so by the quantity of the SEM-captured images, the training images collectively can only cover a relatively small number of patterns, which renders the trained denoising model ineffective in denoising input images that may have a wide range patterns. Undesirably, a trained denoising model needs to be retrained to be able to process images with new patterns. According to embodiments of the present disclosure, a denoising model is trained by using simulated images that are converted from design patterns through a generator model as described above. Training data comprising simulated images can collectively cover remarkably and sufficiently more patterns than SEM-captured images. As a result of the improved pattern coverage, the training can advantageously result in significantly improved effectiveness and accuracy of the denoising model. The requirement for retraining can be far reduced or even eliminated.

<FIG> illustrates a process of training a denoising model (e.g., MD2), according to the invention of the present disclosure.

The first simulated images <NUM>, image noise, and/or reference images REF as training data to train the denoising model MD2. According to the invention, additionally, captured images <NUM> (e.g., SEM images) of a patterned substrate are included in the training data. According to the invention, the number of captured images <NUM> is less than the number of simulated images <NUM> According to the invention, the captured images <NUM> can be used to update a trained denoising model MD2.

In an embodiment, the model MD2 can take as input, for example, noisy images S1*, S2*,. , Sn* (e.g., generated using the model MD1) and image noise discussed with respect to <FIG> and <FIG>. The model MD2 outputs denoised images such as images DN11, D12,. During the training process, one or more model parameters (e.g., weights and biases of different layers of DCNN) of the model MD2 may be modified until convergence is achieved or the denoised images are within a specified threshold of reference images REF. In an embodiment, convergence is achieved when changing model parameter values do not cause significant improvement in the model output compared to a prior model output.

In an embodiment, the method <NUM> further includes obtaining, via a metrology tool, a SEM image of a patterned substrate; and executing the trained denoising model MD2 using the SEM image as the input image to generate the denoised SEM image.

In an embodiment, the second machine leaning model MD2 may also be trained using GAN training method, as discussed above, using the inputs discussed in process P203. For example, using the first simulated images <NUM>, image noise, and reference images REF as training data. In an embodiment, the reference images REF can be the first simulated images <NUM>.

<FIG> illustrates an exemplary process of using the trained denoising model MD2 to generate denoised SEM images. Example SEM images <NUM> and <NUM> of a patterned substrate are captured via a SEM tool. Note the SEM image <NUM> and <NUM> have very different and complex patterns than used patterns in images in the training data. As the trained model MD2 is trained based on simulated images related to design patterns, a large number of patterns can be advantageously covered. As such, the trained model MD2 is able to generate highly accurate denoised images <NUM> and <NUM> of the SEM images <NUM> and <NUM>, respectively. The results in <FIG> show that the trained model MD2 can handle new patterns without additional training. In an embodiment, further fine-tuning of the denoising model MD2 can be performed using the newly captured SEM images to further improve a quality of the denoised images.

<FIG> is a flow chart of another exemplary method <NUM> for generating a denoising model according to an embodiment of the present disclosure. The method <NUM> includes process P301, P303 and P305 discussed below.

Process P301 includes obtaining a first set of simulated images <NUM> based on design patterns. In an embodiment, each image of the first set of simulated images <NUM> is a combination of a simulated SEM image and an image noise (e.g., see S1*, S2*,. Sn* in <FIG>).

In an embodiment, the obtaining of the simulated SEM images includes executing a trained model using the design patterns as input to generate the simulated SEM images. For example, executing the trained model MD1 as discussed with respect to <FIG>. In an embodiment, the trained model (e.g., MD1) is trained based on the design patterns and captured images of a patterned substrate, wherein each captured image is associated with a design pattern. In an embodiment, the captured images are SEM images acquired via a scanning electron microscope (SEM). In an embodiment, the image noise is noise extracted from the captured images of the patterned substrate. In an embodiment, the image noise is a Gaussian noise, white noise, salt and pepper noise characterized by user specified parameters.

In an embodiment, the trained model (e.g., MD1) is a first machine learning model. In an embodiment, the first machine learning model is a CNN or a DCNN trained using a generative adversarial network. In an embodiment, the trained model MD1 is a generative model configured to generate the simulated SEM image for a given design pattern. For example, the trained model MD1, as discussed with respect to <FIG> and <FIG>. In an embodiment, the reference denoised images are the simulated SEM images associated with the design patterns. For example, the reference images can be S1, S2,. Sn generated by MD1 of <FIG>.

Process P303 includes providing the first set of simulated images <NUM> as input to a base denoising model BM1 to obtain an initial second set of simulated images, the initial second set of simulated images being denoised images associated with the design patterns. In an embodiment, the base model is can be an untrained model or a trained model that needs to be fine-tuned. In an embodiment, captured images of a patterned substrate may also be used for training or fine-tuning the denoising model. Process P305 includes using reference denoised images as feedback to update one or more configurations of the base denoising model BM1. The one or more configurations are updated based on a comparison between the reference denoised images and the second set of simulated images. For example, updating the one or more configurations includes modifying model parameters of the base model. At the end of the training process, the base model with the updated configuration becomes the denoising model. Such denoising model can generate the second set of simulated images using SEM images as input, for example.

In an embodiment, the denoising model is a second machine learning model. In an embodiment, the second deep learning model is trained using a deep leaning method or a machine learning method. In an embodiment, the denoising model may also be trained using a generative adversarial network training method. In an embodiment, the denoising model is a convolutional neural network, or other machine learning models. In an embodiment, the denoising model is MD2, as discussed with respect to <FIG>.

As discussed herein, an example of a denoising model is a machine learning model. Both unsupervised machine learning and supervised machine learning models may be used to generate denoised images from an input noisy images such as SEM of a patterned substrate. Without limiting the scope of the invention, applications of supervised machine learning algorithms are described below.

Supervised learning is the machine learning task of inferring a function from labeled training data. The training data includes a set of training examples. In supervised learning, each example is a pair having an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way.

Given a set of N training examples of the form {(x<NUM>,y<NUM>),(x<NUM>,y<NUM>),. ,(xN,yN)} such that xi is the feature vector of the i-th example and yi is its label (i.e., class), a learning algorithm seeks a function g:X→Y, where X is the input space and Y is the output space. A feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis. When representing images, the feature values might correspond to the pixels of an image, when representing texts perhaps term occurrence frequencies. The vector space associated with these vectors is often called the feature space. The function g is an element of some space of possible functions G, usually called the hypothesis space. It is sometimes convenient to represent g using a scoring function <MAT> such that g is defined as returning the Y value that gives the highest score: <MAT>. Let F denote the space of scoring functions.

Although G and F can be any space of functions, many learning algorithms are probabilistic models where g takes the form of a conditional probability model <NUM>(x) = P(y|x), or f takes the form of a joint probability model f(x,y) = P(x,y). For example, naive Bayes and linear discriminant analysis are joint probability models, whereas logistic regression is a conditional probability model.

There are two basic approaches to choosing f or g: empirical risk minimization and structural risk minimization. Empirical risk minimization seeks the function that best fits the training data. Structural risk minimization includes a penalty function that controls the bias/variance tradeoff.

In both cases, it is assumed that the training set has a sample of independent and identically distributed pairs (xi,yi). In order to measure how well a function fits the training data, a loss function <MAT> is defined. For training example (xi,yi), the loss of predicting the value ŷ is L(yi,ŷ).

The risk R(g) of function g is defined as the expected loss of g. This can be estimated from the training data as <MAT>.

Exemplary models of supervised learning include decision trees, ensembles (bagging, boosting, random forest), k-NN, linear regression, naive Bayes, neural networks, logistic regression, perceptron, support vector machine (SVM), relevance vector machine (RVM), and deep learning.

SVM is an example of supervised learning model, which analyzes data and recognizes patterns and can be used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of two categories, a SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. A SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.

In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel methods, implicitly mapping their inputs into high-dimensional feature spaces.

Kernel methods involve a user-specified kernel, i.e., a similarity function over pairs of data points in raw representation. Kernel methods owe their name to the use of kernel functions, which enable them to operate in a high-dimensional, implicit feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the images of all pairs of data in the feature space. This operation is often computationally cheaper than the explicit computation of the coordinates. This approach is called the "kernel trick.

The effectiveness of SVM depends on the selection of kernel, the kernel's parameters, and soft margin parameter C. A common choice is a Gaussian kernel, which has a single parameter γ. The best combination of C and γ is often selected by a grid search (also known as "parameter sweep") with exponentially growing sequences of C and γ, for example, C ∈ {<NUM>-<NUM>,<NUM>-<NUM>,. ,<NUM><NUM>,<NUM><NUM>}; γ ∈ {<NUM>-<NUM>,<NUM>-<NUM>,. ,<NUM><NUM>,<NUM><NUM>}.

A grid search is an exhaustive searching through a manually specified subset of the hyperparameter space of a learning algorithm. A grid search algorithm is guided by some performance metric, typically measured by cross-validation on the training set or evaluation on a held-out validation set.

Each combination of parameter choices may be checked using cross validation, and the parameters with best cross-validation accuracy are picked.

Cross-validation, sometimes called rotation estimation, is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. In a prediction problem, a model is usually given a dataset of known data on which training is run (training dataset), and a dataset of unknown data (or first seen data) against which the model is tested (testing dataset). The goal of cross validation is to define a dataset to "test" the model in the training phase (i.e., the validation dataset), in order to limit problems like overfitting, give an insight on how the model will generalize to an independent data set (i.e., an unknown dataset, for instance from a real problem), etc. One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set). To reduce variability, multiple rounds of cross-validation are performed using different partitions, and the validation results are averaged over the rounds.

The final model, which can be used for testing and for classifying new data, is then trained on the entire training set using the selected parameters.

Another example of supervised learning is regression. Regression infers the relationships between a dependent variable and one or more independent variables, from a set of values of the dependent variables and corresponding values of the independent variables. Regression may estimate the conditional expectation of the dependent variable given the independent variables. The inferred relationships may be called the regression function. The inferred relationships may be probabilistic.

In an embodiment, there is provided a system that can use the model MD2 to generate denoised images after the system captures images of a patterned substrate. In an embodiment, the system can be, for example, a SEM tool of <FIG> or an inspection tool of <FIG> that are configured to include the model MD1 and/or MD2 discussed herein. For example, the metrology tool includes an e-beam generator to capture an image of a patterned substrate; and one or more processors including the MD1 and MD2 model. The one or more processors are configured to execute a trained model configured to generate a simulated image based on a design pattern used to pattern the substrate; and execute a denoising model using the captured image and the simulated image as input to generate a denoised image of the patterned substrate. As mentioned earlier, the denoising model (e.g., MD2) is a convolutional neural network.

Furthermore, in an embodiment, the one or more processors is further configured to update the denoising model based on a captured image of a patterned substrate. In an embodiment, the updating of the denoising model includes execute the denoising model using the captured to generate the denoised image; and update one or more parameters of the denoising model based on a comparison of the denoised image with a reference denoised image.

The present disclosure is not limited to any applications that use denoised images. In the semiconductor industry, the denoised images can be used for inspection and metrology for example. In an embodiment, the denoised images can be used to determine hot spots of patterned substrate. Hot spots may be determined based on absolute CD values measured from the denoised image. Alternatively, hot spots may be determined based on a set of predetermined rules such as those used in a design rule checking system, including, but not limited to, line-end pullback, corner rounding, proximity to neighboring features, pattern necking or pinching, and other metrics of pattern deformation relative to the desired pattern.

In an embodiment, the denoised images can be used to improve patterning process. For example, the denoised images can be used in simulation of the patterning process, for example, to predict contours, CDs, edge placement (e.g., edge placement error), etc. in the resist and/or etched image. The objective of the simulation is to accurately predict, for example, edge placement, and/or aerial image intensity slope, and/or CD, etc. of the printed pattern. These values can be compared against an intended design to, e.g., correct the patterning process, identify where a defect is predicted to occur, etc. The intended design is generally defined as a pre-OPC design layout which can be provided in a standardized digital file format such as GDSII or OASIS or other file format.

In some embodiments, the inspection apparatus or the metrology apparatus may be a scanning electron microscope (SEM) that yields an image of a structure (e.g., some or all the structure of a device) exposed or transferred on the substrate. <FIG> depicts an embodiment of a SEM tool. A primary electron beam EBP emitted from an electron source ESO is converged by condenser lens CL and then passes through a beam deflector EBD1, an E x B deflector EBD2, and an objective lens OL to irradiate a substrate PSub on a substrate table ST at a focus.

When the substrate PSub is irradiated with electron beam EBP, secondary electrons are generated from the substrate PSub. The secondary electrons are deflected by the E x B deflector EBD2 and detected by a secondary electron detector SED. A two-dimensional electron beam image can be obtained by detecting the electrons generated from the sample in synchronization with, e.g., two dimensional scanning of the electron beam by beam deflector EBD1 or with repetitive scanning of electron beam EBP by beam deflector EBD1 in an X or Y direction, together with continuous movement of the substrate PSub by the substrate table ST in the other of the X or Y direction.

A signal detected by secondary electron detector SED is converted to a digital signal by an analog/digital (A/D) converter ADC, and the digital signal is sent to an image processing system IPU. In an embodiment, the image processing system IPU may have memory MEM to store all or part of digital images for processing by a processing unit PU. The processing unit PU (e.g., specially designed hardware or a combination of hardware and software) is configured to convert or process the digital images into datasets representative of the digital images. Further, image processing system IPU may have a storage medium STOR configured to store the digital images and corresponding datasets in a reference database. A display device DIS may be connected with the image processing system IPU, so that an operator can conduct necessary operation of the equipment with the help of a graphical user interface.

As noted above, SEM images may be processed to extract contours that describe the edges of objects, representing device structures, in the image. These contours are then quantified via metrics, such as CD. Thus, typically, the images of device structures are compared and quantified via simplistic metrics, such as an edge-to-edge distance (CD) or simple pixel differences between images. Typical contour models that detect the edges of the objects in an image in order to measure CD use image gradients. Indeed, those models rely on strong image gradients. But, in practice, the image typically is noisy and has discontinuous boundaries. Techniques, such as smoothing, adaptive thresholding, edge-detection, erosion, and dilation, may be used to process the results of the image gradient contour models to address noisy and discontinuous images, but will ultimately result in a low-resolution quantification of a high-resolution image. Thus, in most instances, mathematical manipulation of images of device structures to reduce noise and automate edge detection results in loss of resolution of the image, thereby resulting in loss of information. Consequently, the result is a low-resolution quantification that amounts to a simplistic representation of a complicated, high-resolution structure.

So, it is desirable to have a mathematical representation of the structures (e.g., circuit features, alignment mark or metrology target portions (e.g., grating features), etc.) produced or expected to be produced using a patterning process, whether, e.g., the structures are in a latent resist image, in a developed resist image or transferred to a layer on the substrate, e.g., by etching, that can preserve the resolution and yet describe the general shape of the structures. In the context of lithography or other pattering processes, the structure may be a device or a portion thereof that is being manufactured and the images may be SEM images of the structure. In some instances, the structure may be a feature of semiconductor device, e.g., integrated circuit. In this case, the structure may be referred as a pattern or a desired pattern that comprises a plurality of feature of the semiconductor device. In some instances, the structure may be an alignment mark, or a portion thereof (e.g., a grating of the alignment mark), that is used in an alignment measurement process to determine alignment of an object (e.g., a substrate) with another object (e.g., a patterning device) or a metrology target, or a portion thereof (e.g., a grating of the metrology target), that is used to measure a parameter (e.g., overlay, focus, dose, etc.) of the patterning process. In an embodiment, the metrology target is a diffractive grating used to measure, e.g., overlay.

<FIG> schematically illustrates a further embodiment of an inspection apparatus. The system is used to inspect a sample <NUM> (such as a substrate) on a sample stage <NUM> and comprises a charged particle beam generator <NUM>, a condenser lens module <NUM>, a probe forming objective lens module <NUM>, a charged particle beam deflection module <NUM>, a secondary charged particle detector module <NUM>, and an image forming module <NUM>.

The charged particle beam generator <NUM> generates a primary charged particle beam <NUM>. The condenser lens module <NUM> condenses the generated primary charged particle beam <NUM>. The probe forming objective lens module <NUM> focuses the condensed primary charged particle beam into a charged particle beam probe <NUM>. The charged particle beam deflection module <NUM> scans the formed charged particle beam probe <NUM> across the surface of an area of interest on the sample <NUM> secured on the sample stage <NUM>. In an embodiment, the charged particle beam generator <NUM>, the condenser lens module <NUM> and the probe forming objective lens module <NUM>, or their equivalent designs, alternatives or any combination thereof, together form a charged particle beam probe generator which generates the scanning charged particle beam probe <NUM>.

The secondary charged particle detector module <NUM> detects secondary charged particles <NUM> emitted from the sample surface (maybe also along with other reflected or scattered charged particles from the sample surface) upon being bombarded by the charged particle beam probe <NUM> to generate a secondary charged particle detection signal <NUM>. The image forming module <NUM> (e.g., a computing device) is coupled with the secondary charged particle detector module <NUM> to receive the secondary charged particle detection signal <NUM> from the secondary charged particle detector module <NUM> and accordingly forming at least one scanned image. In an embodiment, the secondary charged particle detector module <NUM> and image forming module <NUM>, or their equivalent designs, alternatives or any combination thereof, together form an image forming apparatus which forms a scanned image from detected secondary charged particles emitted from sample <NUM> being bombarded by the charged particle beam probe <NUM>.

In an embodiment, a monitoring module <NUM> is coupled to the image forming module <NUM> of the image forming apparatus to monitor, control, etc. the patterning process and/or derive a parameter for patterning process design, control, monitoring, etc. using the scanned image of the sample <NUM> received from image forming module <NUM>. So, in an embodiment, the monitoring module <NUM> is configured or programmed to cause execution of a method described herein. In an embodiment, the monitoring module <NUM> comprises a computing device. In an embodiment, the monitoring module <NUM> comprises a computer program to provide functionality herein and encoded on a computer readable medium forming, or disposed within, the monitoring module <NUM>.

In an embodiment, like the electron beam inspection tool of <FIG> that uses a probe to inspect a substrate, the electron current in the system of <FIG> is significantly larger compared to, e.g., a CD SEM such as depicted in <FIG>, such that the probe spot is large enough so that the inspection speed can be fast. However, the resolution may not be as high as compared to a CD SEM because of the large probe spot. In an embodiment, the above discussed inspection apparatus may be single beam or a multi-beam apparatus without limiting the scope of the present disclosure.

The SEM images, from, e.g., the system of <FIG> and/or <FIG>, may be processed to extract contours that describe the edges of objects, representing device structures, in the image. These contours are then typically quantified via metrics, such as CD, at user-defined cut-lines. Thus, typically, the images of device structures are compared and quantified via metrics, such as an edge-to-edge distance (CD) measured on extracted contours or simple pixel differences between images.

In an embodiment, the one or more procedures of the process <NUM>, and/or <NUM> can be implemented as instructions (e.g., program code) in a processor of a computer system (e.g., process <NUM> of computer system <NUM>). In an embodiment, the procedures may be distributed across a plurality of processors (e.g., parallel computation) to improve computing efficiency. In an embodiment, the computer program product comprising a non-transitory computer readable medium has instructions recorded thereon, the instructions when executed by a computer hardware system implementing the method described herein.

According to present disclosure, the combination and sub-combinations of disclosed elements constitute separate embodiments. For example, a first combination includes determining a denoising model based on simulated images related to design patterns and noise image. The subcombination may include determining a denoised image using the denoising model. In another combination, the denoised images can be employed in an inspection process, determining OPC, or SMO based on model-generated variance data. In another example, the combination includes determining, based on inspection data based on the denoised images, process adjustments to a lithography process, resist process, or etch process to improve the yield of the patterning process.

<FIG> is a block diagram that illustrates a computer system <NUM> which can assist in implementing the methods, flows or the apparatus disclosed herein. Computer system <NUM> includes a bus <NUM> or other communication mechanism for communicating information, and a processor <NUM> (or multiple processors <NUM> and <NUM>) coupled with bus <NUM> for processing information.

Computer system <NUM> may be coupled via bus <NUM> to a display <NUM>, such as a cathode ray tube (CRT) or flat panel or touch panel display for displaying information to a computer user. A touch panel (screen) display may also be used as an input device.

According to one embodiment, portions of one or more methods described herein may be performed by computer system <NUM> in response to processor <NUM> executing one or more sequences of one or more instructions contained in main memory <NUM>. Such instructions may be read into main memory <NUM> from another computer-readable medium, such as storage device <NUM>. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in main memory <NUM>. In an alternative embodiment, hard-wired circuitry may be used in place of or in combination with software instructions. Thus, the description herein is not limited to any specific combination of hardware circuitry and software.

The term "computer-readable medium" as used herein refers to any medium that participates in providing instructions to processor <NUM> for execution. Such a medium may take many forms, including but not limited to, non-volatile media, volatile media, and transmission media. Volatile media include dynamic memory, such as main memory <NUM>. Transmission media include coaxial cables, copper wire and fiber optics, including the wires that comprise bus <NUM>. Transmission media can also take the form of acoustic or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.

Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to processor <NUM> for execution. For example, the instructions may initially be borne on a magnetic disk of a remote computer. A modem local to computer system <NUM> can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to bus <NUM> can receive the data carried in the infrared signal and place the data on bus <NUM>.

Computer system <NUM> may also include a communication interface <NUM> coupled to bus <NUM>. For example, communication interface <NUM> may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line.

ISP <NUM> in turn provides data communication services through the worldwide packet data communication network, now commonly referred to as the "Internet" <NUM>. The signals through the various networks and the signals on network link <NUM> and through communication interface <NUM>, which carry the digital data to and from computer system <NUM>, are exemplary forms of carrier waves transporting the information.

Computer system <NUM> can send messages and receive data, including program code, through the network(s), network link <NUM>, and communication interface <NUM>. One such downloaded application may provide all or part of a method described herein, for example. In this manner, computer system <NUM> may obtain application code in the form of a carrier wave.

<FIG> schematically depicts an exemplary lithographic projection apparatus in conjunction with the techniques described herein can be utilized. The apparatus comprises:.

As depicted herein, the apparatus is of a transmissive type (i.e., has a transmissive patterning device). However, in general, it may also be of a reflective type, for example (with a reflective patterning device). The apparatus may employ a different kind of patterning device to classic mask; examples include a programmable mirror array or LCD matrix.

The source SO (e.g., a mercury lamp or excimer laser, LPP (laser produced plasma) EUV source) produces a beam of radiation. This beam is fed into an illumination system (illuminator) IL, either directly or after having traversed conditioning means, such as a beam expander Ex, for example. The illuminator IL may comprise adjusting means AD for setting the outer and/or inner radial extent (commonly referred to as σ-outer and σ-inner, respectively) of the intensity distribution in the beam. In addition, it will generally 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.

It should be noted with regard to <FIG> that the source SO may be within the housing of the lithographic projection apparatus (as is often the case when the source SO is a mercury lamp, for example), but that it may also be remote from the lithographic projection apparatus, the radiation beam that it produces being led into the apparatus (e.g., with the aid of suitable directing mirrors); this latter scenario is often the case when the source SO is an excimer laser (e.g., based on KrF, ArF or F2 lasing).

The beam PB subsequently intercepts the patterning device MA, which is held on a patterning device table MT. Having traversed the patterning device MA, the beam B passes through the lens PL, which focuses the beam B onto a target portion C of the substrate W. With the aid of the second positioning means (and interferometric measuring means IF), the substrate table WT can be moved accurately, e.g. so as to position different target portions C in the path of the beam PB. Similarly, the first positioning means can be used to accurately position the patterning device MA with respect to the path of the beam B, e.g., after mechanical retrieval of the patterning device MA from a patterning device library, or during a scan. In general, movement of the object tables MT, WT will be realized with the aid of a long-stroke module (coarse positioning) and a short-stroke module (fine positioning), which are not explicitly depicted in <FIG>. However, in the case of a stepper (as opposed to a step-and-scan tool) the patterning device table MT may just be connected to a short stroke actuator, or may be fixed.

The depicted tool can be used in two different modes:.

<FIG> schematically depicts another exemplary lithographic projection apparatus LA in conjunction with the techniques described herein can be utilized.

The lithographic projection apparatus LA comprises:.

As here depicted, the apparatus LA is of a reflective type (e.g. employing a reflective patterning device). It is to be noted that because most materials are absorptive within the EUV wavelength range, the patterning device may have multilayer reflectors comprising, for example, a multi-stack of Molybdenum and Silicon. In one example, the multi-stack reflector has a <NUM> layer pairs of Molybdenum and Silicon where the thickness of each layer is a quarter wavelength. Even smaller wavelengths may be produced with X-ray lithography. Since most material is absorptive at EUV and x-ray wavelengths, a thin piece of patterned absorbing material on the patterning device topography (e.g., a TaN absorber on top of the multi-layer reflector) defines where features would print (positive resist) or not print (negative resist).

Referring to <FIG>, the illuminator IL receives an extreme ultra violet radiation beam from the source collector module SO. Methods to produce EUV radiation include, but are not necessarily limited to, converting a material into a plasma state that has at least one element, e.g., xenon, lithium or tin, with one or more emission lines in the EUV range. In one such method, often termed laser produced plasma ("LPP") the plasma can be produced by irradiating a fuel, such as a droplet, stream or cluster of material having the line-emitting element, with a laser beam. The source collector module SO may be part of an EUV radiation system including a laser, not shown in <FIG>, for providing the laser beam exciting the fuel. The resulting plasma emits output radiation, e.g., EUV radiation, which is collected using a radiation collector, disposed in the source collector module. The laser and the source collector module may be separate entities, for example when a CO2 laser is used to provide the laser beam for fuel excitation.

In such cases, the laser is not considered to form part of the lithographic apparatus and the radiation beam is passed from the laser to the source collector module with the aid of a beam delivery system comprising, for example, suitable directing mirrors and/or a beam expander. In other cases the source may be an integral part of the source collector module, for example when the source is a discharge produced plasma EUV generator, often termed as a DPP source.

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

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

The depicted apparatus LA could be used in at least one of the following modes:.

<FIG> shows the apparatus LA in more detail, including the source collector module SO, the illumination system IL, and the projection system PS. The source collector module SO is constructed and arranged such that a vacuum environment can be maintained in an enclosing structure <NUM> of the source collector module SO. An EUV radiation emitting plasma <NUM> may be formed by a discharge produced plasma source. EUV radiation may be produced by a gas or vapor, for example Xe gas, Li vapor or Sn vapor in which the very hot plasma <NUM> is created to emit radiation in the EUV range of the electromagnetic spectrum. The very hot plasma <NUM> is created by, for example, an electrical discharge causing at least partially ionized plasma. Partial pressures of, for example, <NUM> Pa of Xe, Li, Sn vapor or any other suitable gas or vapor may be required for efficient generation of the radiation. In an embodiment, a plasma of excited tin (Sn) is provided to produce EUV radiation.

The radiation emitted by the hot plasma <NUM> is passed from a source chamber <NUM> into a collector chamber <NUM> via an optional gas barrier or contaminant trap <NUM> (in some cases also referred to as contaminant barrier or foil trap) which is positioned in or behind an opening in source chamber <NUM>. The contaminant trap <NUM> may include a channel structure. Contamination trap <NUM> may also include a gas barrier or a combination of a gas barrier and a channel structure. The contaminant trap or contaminant barrier <NUM> further indicated herein at least includes a channel structure, as known in the art.

The collector chamber <NUM> may include a radiation collector CO which may be a so-called grazing incidence collector. Radiation collector CO has an upstream radiation collector side <NUM> and a downstream radiation collector side <NUM>. Radiation that traverses collector CO can be reflected off a grating spectral filter <NUM> to be focused in a virtual source point IF along the optical axis indicated by the dot-dashed line 'O'. The virtual source point IF is commonly referred to as the intermediate focus, and the source collector module is arranged such that the intermediate focus IF is located at or near an opening <NUM> in the enclosing structure <NUM>. The virtual source point IF is an image of the radiation emitting plasma <NUM>.

Subsequently the radiation traverses the illumination system IL, which may include a facetted field mirror device <NUM> and a facetted pupil mirror device <NUM> arranged to provide a desired angular distribution of the radiation beam <NUM>, at the patterning device MA, as well as a desired uniformity of radiation intensity at the patterning device MA. Upon reflection of the beam of radiation <NUM> at the patterning device MA, held by the support structure MT, a patterned beam <NUM> is formed and the patterned beam <NUM> is imaged by the projection system PS via reflective elements <NUM>, <NUM> onto a substrate W held by the substrate table WT.

More elements than shown may generally be present in illumination optics unit IL and projection system PS. The grating spectral filter <NUM> may optionally be present, depending upon the type of lithographic apparatus. Further, there may be more mirrors present than those shown in the figures, for example there may be <NUM>- <NUM> additional reflective elements present in the projection system PS than shown in <FIG>.

Collector optic CO, as illustrated in <FIG>, is depicted as a nested collector with grazing incidence reflectors <NUM>, <NUM> and <NUM>, just as an example of a collector (or collector mirror). The grazing incidence reflectors <NUM>, <NUM> and <NUM> are disposed axially symmetric around the optical axis O and a collector optic CO of this type may be used in combination with a discharge produced plasma source, often called a DPP source.

Alternatively, the source collector module SO may be part of an LPP radiation system as shown in <FIG>. A laser LA is arranged to deposit laser energy into a fuel, such as xenon (Xe), tin (Sn) or lithium (Li), creating the highly ionized plasma <NUM> with electron temperatures of several <NUM>'s of eV. The energetic radiation generated during de-excitation and recombination of these ions is emitted from the plasma, collected by a near normal incidence collector optic CO and focused onto the opening <NUM> in the enclosing structure <NUM>.

Claim 1:
A computer- implemented method (<NUM>; <NUM>) for training a denoising model (MD2), the method comprising:
converting (P201; P301) design patterns (DP11, DP12, DPn) to a first set of simulated clean SEM images (<NUM>; <NUM>; S1, S2, Sn) of patterned substrates (PSub, <NUM>, W) based on the design patterns, wherein the design patterns are in Graphic Data Signal file format;
training (P203; P305) the denoising model based on the first set of simulated clean SEM images and an image noise added in the simulated clean SEM images, morse; wherein the denoising model is operable to generate a denoised SEM image (<NUM>, <NUM>) of an input SEM image (<NUM>, <NUM>); and
updating the denoising model based on captured patterned substrate SEM images (<NUM>; <NUM>), included in the training data wherein the number of captured patterned substrate SEM images is less than the number of simulated clean SEM images.