Patent Description:
Scanning electron microscopes (SEM) are widely used imaging tools for the semiconductor industry. SEMs are, for example, used to measure critical dimensions (CD) and roughness features of resist patterns.

The electrical characteristics of electronic circuit formed by lithographic patterning techniques are significantly influenced by features such as line-edge (LER) and line-width (LWR) roughness. It is, therefore, important to analyze these features. However, CD-SEM images inherently contain a significant level of noise and, thus, accurate measurements of LER/LWR and CD of SEM images are a substantial challenge. In particular, conventional image processing techniques and noise-filtering methods often smooth away such edge information. In addition, the quantification of noise in CD-SEM images is difficult, because the noise of an image often depends on metrology settings and sample properties and does not fit truly Gaussian or Poisson distributions.

Recent discriminative deep learning-based algorithms have outperformed conventional noise filtering methods. However, these machine learning methods need require clean ground-truth images for training purposes which, in many cases, either do not exist, or are difficult to acquire.

Alternatively, some known deep learning approaches are based on generating synthetic noiseless images from software-tools and use them for supervised learning or on degrading clean target images with approximated noise levels and use them for semi-supervised learning. Often these methods require additional conditional files to generate those corresponding synthetic images which may lead to additional artefacts and, thus, affect the accuracy of measured metrics such as LER/LWR and CD.

<CIT> discloses a semiconductor inspection apparatus that detects micro device defects with high sensitivity by irradiating a sample with an electron beam, measuring outputs from a contact probe during and after irradiation, and analyzing the difference in measurements to identify abnormalities.

<CIT> discloses a system and a method for controlling a semiconductor manufacturing process.

<NPL>, discloses a training scheme Noise2Void (N2V) for image denoising that eliminates the need for clean targets or noisy image pairs, allowing direct training on the data to be denoised.

<NPL>, discloses a self-supervised denoising approach for light microscopy images that incorporates convolution with a point spread function to reduce high-frequency artifacts, achieving results comparable to traditional supervised methods.

Thus, it is an objective to provide an improved method and an improved device for denoising an electron microscope image. Furthermore, it is an objective to provide an improved method for analyzing a sample. In particular, the above-mentioned disadvantages should be avoided.

The objective is achieved by the embodiments provided in the enclosed independent claims. Advantageous implementations of the embodiments of the invention are further defined in the dependent claims.

According to a first aspect, the present disclosure relates to a method for analyzing a sample, in particular a die comprising memory structures, wherein the method comprises the steps of:.

This achieves the advantage that the sample can efficiently be analyzed based on a denoised SEM image. In particular, a representation of the sample can be provided in the form of the property map that allows quickly and easily determining the electrical properties of the sample.

For example, the spatially resolved inspection measurement can be carried out by an electrical probe station fitted with an impedance analyzer.

Determining a calibration curve achieves the advantage that further property maps for further EM images can efficiently be generated based on the calibration curve.

Preferably, the calibration curve is generated in the form of a lookup table, which comprises a list of electrical property values, e.g. resistivity values, for respective pixel values of an EM image, in particular gray level values. In particular, one electrical value each is assigned to a gray value in the lookup table. The lookup table can be stored in a memory and can be updated each time a measurement according to the analysis method above is carried out.

In an embodiment, the step of denoising the recorded EM image is carried out by a neural network, wherein the neural network is trained by: selecting a patch of the EM image, wherein the patch comprises a plurality of pixels, wherein the following steps are performed on the patch:.

wherein the training further comprises the steps of:.

This achieves the advantage that the entire EM image can efficiently be denoised.

This achieves the advantage that the SEM image can efficiently be denoised, in particular without requiring additional ground truth or synthetic images.

Furthermore, by replacing the value of the selected pixel with another, preferably randomly selected, pixel value from anywhere in the image, a blind spot in the location of the selected pixel is created. This blind spot prevents a neural network that carries out the method from simply learning the identity of the selected pixel.

Preferably, the pixel within the patch that is denoised is a center or an edge pixel of the patch. The patch can be of any size, depending on the model performance and/or the original image. For example, the patch has a size of 32x32, 64x64 or 128x128 pixels.

In an embodiment, the further patch partially overlaps with a previously selected patch or is adjacent to the previously selected patch. This achieves the advantage that the true denoised value of the pixel can efficiently be predicted and respectively validated against all possible values.

For example, if there are <NUM> possibilities for a true denoised pixel value, at each iteration first a random value out of <NUM> is predicted, and secondly it is validated how realistic the previous guess is based on its surroundings and best case respective to the entire image body.

In particular, the denoising model generally receives noisy patches at an input side and predicts denoised pixels for the corresponding noisy patch at an output side. For each patch, this procedure is iterated to cover the entire noisy image. Thereby, the following steps can be carried out: a) receiving an image of a large size, e.g. 2048x2048 pixels; b) extracting small size patches of size 64x64, out of the 2048x2048 image, e.g. dividing the 2048x2048 pixels into <NUM> noisy patches with a size of 64x64 each; c) starting to denoise each and every patch with a sliding kernel size of 3x3, i.e. a 3x3 kernel is sliding each time <NUM> pixel [left,right,up,down] to cover each pixel of the 64x64 patch; d) after training and validation, generating denoised image patches; e) stitching all denoised patches to a finally denoised image at the output; and f) iterate randomly the above steps b)-e) a few times.

Preferably, the number of patches for an EM image is fixed. The patches can be extracted in an initial step. For any random patch a kernel of size n*n, with n=<NUM>,<NUM>,<NUM>. etc., can be chosen. For instance, the kernel has a size of <NUM>*<NUM>. This kernel can then be shifted step by step, e.g. by one pixel each time if a stride parameter is <NUM>. In other words, on each selected patch, the kernel is moving over the entire patch step-by-step to denoise the pixels in the patch. By subsequently moving the kernel over the different random patches, the entire image can be denoised in this way.

In an embodiment, the steps of selecting a further patch and repeating the steps i) to iii) for the respective selected further patch are repeated until all pixels of the EM image are replaced by a respective denoised value. This achieves the advantage that the entire EM image can efficiently be denoised.

In particular, the denoising of the EM image can be repeated in several iterations until all noisy pixel are replaced by a denoised value.

In an embodiment, the method comprises the further step of determining a noise level of the EM image based on the differences between the original pixel values of the EM image and the determined denoised values. This achieves the advantage that an information on an inherent noise level of the individual EM tool that was used to record the EM image can be acquired. In particular, this information can be used to analyze and compare different EM tools, e.g. to identify and map differences of their E-beam columns.

In an embodiment, the denoised value of the one pixel is at least partially determined based on assumed statistical dependencies between the noiseless components of the pixel values of the other pixels in the patch and on an assumed statistical independence between the noise components of the pixel values of neighboring pixels. This achieves the advantage that the image can efficiently be denoised.

In particular, due to the statistical dependencies a neural network that carries out the method can estimate the signal of the pixel based on the neighboring pixels within the patch, while at the same time these neighboring pixels carry no information on the noise value of the one pixel.

In an embodiment, at least the step of determining the denoised value for the one pixel of the patch is carried out by a trainable neural network, in particular a U-Net based neural network.

In an embodiment, the trainable neural network is trained by an unsupervised learning algorithm each time it determines a denoised value for a pixel of a patch of the EM image. This achieves the advantage that the neural network can efficiently be trained without requiring additional ground truth or synthetic images.

In an embodiment, the trainable neural network is configured to apply a kernel to the pixels of the patch to determine the denoised value of the one pixel.

In an embodiment, the method further comprises the steps of recording a further EM image of a section of the same sample or of a section of a different sample with the SEM in the voltage contrast mode; and generating a further property map based on the further EM image and the calibration curve, wherein the further property map indicates the electrical property of structures visible in the further EM image. This achieves the advantage that further property maps can efficiently be generated based on the calibration curve.

In an embodiment, the step of determining the calibration curve is carried out by a trainable neural network, in particular a multilayer perceptron (MLP) neural network.

In particular, the MLP neural network is used to predict mapping outputs of the calibration curve. This has the advantage that fewer electrical inspection results, e.g. resistivity values, from previous experiments are required to generate the calibration curve.

In an embodiment, the method further comprises the steps of generating a synthetic EM image of the section of the sample based on information on the sample, such as lithographic mask data, comparing the synthetic EM image to the denoised recorded EM image; and identifying defects in the section of the sample based on said comparison. This achieves the advantage that defects on the sample can efficiently be identified.

In particular, by comparing the synthetic EM image to the denoised EM image, the location of defects on the probe can be determined.

<FIG> shows steps of a method for denoising an electron microscope (EM) image according to an embodiment.

The method <NUM> comprises the steps of: selecting <NUM> a patch of the EM image, wherein the patch comprises a plurality of pixels. Subsequently, the following steps i)-iii) are performed on the patch:.

In particular, the steps i-iii) form a denoising <NUM> step for the one pixel in the patch.

In particular, the steps i) to iii) can be repeated for the pixels in the selected patch until each noisy pixel within the patch is replaced by a denoised value.

Preferably, the method comprises the further steps of: selecting <NUM> a further patch of the EM image, wherein the further patch comprises a plurality of pixels; and wherein the further patch is different to a previously selected patch of the EM image, and repeating <NUM> the steps i) to iii) for the selected further patch, i.e. denoising <NUM> one pixel in the further patch by replacing its value with the value of a different, preferably randomly selected, pixel from the same EM image; determining a denoised value for the one pixel in the further patch based on the values of the other pixels in the further patch; and replacing the value of the one pixel in the further patch with the determined denoised value.

Preferably, the denoising method <NUM> is a machine learning, in particular a deep learning method, that is carried out by a trainable neural network. By replacing the value of the selected pixel with another, preferably randomly selected, pixel value from anywhere in the image, a blind spot in the location of the selected pixel is created. This blind spot prevents a neural network that carries out the method <NUM> from simply learning the identity of the selected pixel.

Preferably, the further patch partially overlaps with a previously selected patch or is adjacent to the previously selected patch. In this way, the patch can be moved over the noisy image such that the image is denoised in a step by step manner.

Overlapping a new patch with a previous selected patch can have the advantage that the true denoised value of the selected pixel can efficiently be predicted and respectively validated against all possible values.

In particular, the noise of a pixel can be a random fluctuation pixel value. Denoising a pixel may refer to at least partially removing said random fluctuation from the pixel value. Preferably, the denoised pixel has value that is closer to its noiseless value, i.e. the real pixel value without the noise component.

The steps of selecting <NUM> a further patch and denoising <NUM> one pixel in each respective further patch can be repeated until the entire EM image is denoised, i.e. until every pixel in the EM image is denoised at least once. In particular, the denoising of an EM image according to the method <NUM> can be repeated in several iterations to correctly replace all corrupted noisy pixel with a denoised signal. With this method <NUM>, the entire EM image can be denoised without requiring a clean reference images, e.g. a ground-truth or a synthetic target image. In other words, the same noisy EM image can be used as input and reference image for the denoising.

Preferably, the method <NUM>, in particular the denoising <NUM>, is carried out by a trainable neural network, in particular a U-Net based neural network. Thereby, the neural network can be configured to apply a kernel, e.g. a convolution matrix, to the pixels of the patch to facilitate determining the denoised value of the one pixel.

Preferably, the trainable neural network is trained by an unsupervised learning algorithm each time it determines a denoised value for a pixel of a patch of the EM image.

The denoised value of a target pixel in a patch can at least partially be determined based on assumed statistical dependencies between the noiseless components of the pixel values of the other pixels in the patch and on an assumed statistical independence between the noise components of the pixel values of neighboring pixels.

Optionally, the method <NUM> can further comprise the steps of: determining a noise level of the EM image based on the differences between the original pixel values of the EM image and the determined denoised values.

This information can be used to analyze and compare different EM tools, e.g. to identify and map difference of their E-beam columns. In particular, the noise level from EM images, e.g. CD-SEM images, extracted from different tools for similar and/or different settings can be extracted to perform a qualitative and comparative analysis in order to label different CD-SEM tools according to their noise levels.

<FIG> shows steps of a method <NUM> for denoising an electron microscope image according to an embodiment. In particular, the method <NUM> shown in <FIG> is based on the more general denoising method <NUM> shown in <FIG>.

In a first step <NUM> of the method <NUM> a noisy patch comprising a plurality of pixels is extracted from an EM image. The EM image was for instance generated by a critical dimension scanning electron microscope (CD-SEM).

In a second step <NUM> of the method <NUM>, the value of a center pixel of the patch is replaced by a randomly selected value from anywhere else in the image. This generates a blind spot in the location of the center pixel. Due to this blind spot, the receptive field of the center pixel, e.g. pixels within a radius around the pixel, excludes the pixel itself.

In particular, the value of each pixel in the EM image can be expressed by x = s + n, wherein s is the noiseless component of the pixel value and n is the noise component. The EM image can be expressed by a joint probability distribution according to: <MAT> while satisfying the condition p(si, sj) ≠ p(si) for <NUM> pixels i, j within a receptive field (RF).

In a third step <NUM> of the method <NUM>, a noiseless value for the center pixel is estimated based on the surrounding pixels in the patch. In particular, the noiseless signal component si of the pixels in the patch comprise statistical dependencies from each other. At the same time, the neighboring pixels in the patch carry no information about the noise component ni of the center pixel. This can be expressed by the flowing equation: <MAT>.

Therefore, the mean value of the pixel value xi based on multiple images is: <MAT> which corresponds to a clean ground truth image (GT), i.e. a noiseless image. In particular, the mean value of the noise component is zero: E[ni] = <NUM>.

In a fourth step <NUM> of the method <NUM>, the noisy value is replaced by a noiseless value to obtain a clean image value for the pixel.

In the last step <NUM>, the denoising of the EM image according to the previous steps <NUM> to <NUM> is iterated for further patches of the EM image until the entire image is denoised.

<FIG> shows a schematic diagram of a section <NUM> of a patch of the EM image according to an embodiment.

The section <NUM> shown in <FIG> is a 3x3 kernel within the patch.

The patch can have any size, depending on the model performance and/or the original image. For example, the patch could have a size of 32x32, 64x64 or 128x128 pixels.

Preferably, the left section <NUM> in <FIG> comprises noisy pixels, including a noisy center pixel si. When performing the denoising method <NUM>, the center pixel is replaced by a different, preferably randomly selected, pixel from the same EM image. In this way, a patch section with a blind spot in the location of the center pixel can be generated (right patch section <NUM> in <FIG>). Subsequently, a denoised value for the blindspot can be determined based on the surrounding pixels.

The 3x3 kernel shown in <FIG> can be shifted in a step by step manner, e.g. by one pixel each time, over the patch. For each kernel position within the patch, a selected pixel of the patch is denoised, e.g. the pixel in the center of the kernel.

<FIG> shows a comparison of a noisy EM image <NUM> and a denoised EM image <NUM> according to an embodiment. In particular, the denoised EM image <NUM> was denoised by any one of the denoising methods <NUM>, <NUM> shown in <FIG> or <FIG>.

For example, the image <NUM> is a SEM image that shows an array of memory structure, in particular memory pillars, which are arranged on top of bottom electrodes on a substrate. Thereby, brightness differences between these structures in the SEM image can indicate different electrical properties of the pillars.

Preferably, the image was recorded in a voltage contrast (VC) mode of the SEM. Thereby, a positive voltage can be applied to the sample that is observed. In particular, the brightness level of each structure in the images <NUM>, <NUM> depends on the structure being shorted, i.e. set to ground, or open, i.e. electrically insulated. A structure that is shorted appears bright, wherein the brightness level depends on the conductivity of the structure. This conductivity analysis can be used to analyze various different memory structures. For instance, in phase change memories (PCM), the conductivity depends on the degree of crystallization of the memory structure, and in MRAM (magnetoresistive random-access memory), STT-MRAM (spin-transfer torque MRAM) or RRAM (resistive random-access memory) memories this conductivity, for instance, shows if the respective layers of the memories have aligned spins.

Generally, such VC SEM images are recorded in the context of defect inspection. During a typical inspection process, an optical or E-beam inspection is used to identify visible defects on a sample. These results can be compared with electrical inspection results. If the location of defects that were identified with both methods match, it indicates a pattern or process defect at this location. If a defect location does not match, a VC image can be recorded to identify non-visible defects.

For instance, the denoised VC SEM images can be used to identify non-visible metal traces between the memory pillars. In addition, by analyzing the gray levels in the SEM images different resistance due to, e.g. thicker metal layers can be detected. While recording the EM image, the sample can be grounded physically, e.g. with wires, or placed on a wafer chuck and the wafer itself can be grounded.

Alternatively to such VC EM images, the denoising method <NUM> can be applied to EM images that were recorded in a secondary emission (SE) mode or a back scattered electron (BSE) mode of an EM. The location of the section visible in the recoded EM image within the sample can be determined based on the known lithographic mask layout, as specified in a GDS file, an optical characterization measurement during an initial defect inspection or an E-beam measurement during a SEM inspection.

<FIG> shows a schematic diagram of a device <NUM> for denoising an electron microscope image according to an embodiment.

The device comprises a receiving unit <NUM> configured to receive an EM image; and a data processing unit <NUM> configured to perform the denoising on the received EM image, in particular according to the method <NUM> shown in <FIG>.

The data processing unit <NUM> can comprise a processor and/or the trainable neural network for carrying out the denoising method <NUM>.

The receiving unit <NUM> can comprise an interface for receiving the EM image. For example, the receiving unit <NUM> can be directly connected to an EM tool for receiving the image. Alternatively, the receiving unit <NUM> can be connectible to a data storage in which the EM image is stored.

<FIG> shows steps of a method <NUM> for analyzing a sample according to an embodiment.

The method <NUM> comprises the steps of: recording <NUM> an EM image of a section of the sample with a scanning electron microscope (SEM), wherein the EM image is recorded in a voltage contrast (VC) mode of the SEM; denoising <NUM> the recorded EM image, in particular, according to any one of the denoising methods <NUM>, <NUM> shown in <FIG> or <FIG>. The method <NUM> further comprises the steps of: performing <NUM> a spatially resolved inspection measurement of the section of the sample, wherein the inspection measurement comprises determining electrical property values, preferably conductivity values, at different locations of the section of the sample; correlating <NUM> the pixel values of the denoised EM image to the electrical property values determined at the different locations of the section of the sample; and generating <NUM> a property map based said correlation, wherein the property map indicates the electrical properties of structures visible in the denoised EM image.

The sample can be a die comprising memory structures, such as the structure visible in the SEM images in <FIG>.

The step of performing <NUM> the spatially resolved inspection measurement can be carried out by an electrical probe station fitted with an impedance analyzer.

Preferably, the property map is a graphical representation of the section of the sample that was recorded in the VC SEM image and comprises additional graphical elements, e.g. a color coding, that indicate the electrical properties of different regions or structures visible in the map. In particular, the graphical representation can be an simplified illustration of the sample that shows the outlines of structures that are visible in the VC SEM image.

In particular, the property map can be a feature map, a proxy map or a variability map of the section of the sample.

In addition to the property map, a calibration curve can be generated based on the correlation between the pixel values of the EM image and the electrical property values.

The calibration curve comprises, for instance, a correlation of gray scale values of a VC SEM image to respective electrical property values, e.g. resistance. Such a calibration curve can be used to generate property maps of further VC SEM images without performing an electrical inspection measurement each time.

<FIG> shows steps of a further method <NUM> for analyzing a sample according to an embodiment.

The method <NUM> comprises the steps of: testing <NUM> a device, such as a die comprising memory structures, in particular PCM or RRAM structurers, by recording an SEM image in a VC mode; and identifying <NUM> faults in the VC SEM image, e.g. unexpected conductivity values of the memory structures. Therefore, the VC SEM image can be denoised <NUM> according to any one of the denoising methods <NUM>, <NUM> shown in <FIG> or <FIG>.

If such faults are detected, the method <NUM> comprises the step of recording <NUM> multiple further VC images in large areas of the device, wherein each of these further VC images can be denoised as well. In a further step <NUM>, the denoised VC SEM images are compared to known resistivity values of the memory structures. Thereby, the known resistivity values are matched to the gray scale values of the CV SEM image. The method <NUM> further comprises generating <NUM> a proxy device map, i.e. a property map, of the device based on the comparison <NUM>. In a final step <NUM>, this proxy device map is used to analyze the electrical property of the memory structures of the device.

<FIG> shows steps of the method <NUM> for analyzing the sample according to a further embodiment. In particular, the method <NUM> shown in <FIG> comprises the steps of the method <NUM> shown in <FIG>.

The method <NUM> comprises recording <NUM> an EM image of a section of a sample, wherein the EM image is recorded by a SEM in a VC mode; performing <NUM> a denoising of the EM image, in particular via the denoising method <NUM> shown in <FIG>, to generate <NUM> a denoised image <NUM>; and mapping <NUM> the generated image according to its gray level values, i.e. correlating the gray level values in the image to the respective locations of the sample.

Furthermore, the method <NUM> comprises performing <NUM> a spatially resolved electrical inspection measurement at the same location where the VC image was acquired, and mapping <NUM> the recorded electrical properties, e.g. resistance, leakage, or capacitance, i.e. correlating the recorded electrical properties to the respective locations of the sample. Subsequently, the property map <NUM> of the section of the sample can be generated, by correlating the gray scale values and the recorded electrical property values for the different locations on the sample,.

In addition, the method <NUM> comprises generating <NUM> a calibration curve <NUM> based on the correlation of the pixel values, e.g. gray level values, of the VC SEM image and the recorded electrical property values. Preferably, the calibration curve <NUM> is generated in the form of a lookup table, which comprises a list of the electrical property values, e.g. resistivity values, for respective pixel values of an EM image, in particular gray level values. This calibration curve <NUM> can be used to generate a property maps for further VC SEM images without correlating these images to respective electrical property value measurement.

For instance, the method <NUM> may comprise the further step of: recording a further EM image 89a of a section of the same or a different sample with the SEM in VC mode, and generating a further property map 91a based on the further EM image and the calibration curve <NUM>, wherein the further property map indicates the electrical property of structures visible in the further EM image 89a.

In particular, by using the calibration curve <NUM> a time consuming manual mapping and calibration of VC SEM images and inspection measurements can be avoided. It would be tedious to correlate all possible grayscale values in a range from, e.g., <NUM> to <NUM> to a respective electrical property value.

Preferably, the calibration curve is generated by a trainable neural network, in particular a multilayer perceptron (MLP) neural network. This neural network can further be configured to generate the property map <NUM> based on the denoised VC SEM image <NUM> and the electrical inspection measurement and/or to generate the property map 91a based on the denoised VC SEM image 89a and the calibration curve <NUM>.

In particular, a machine learning based algorithm can be used to learn the correlation between the degree of brightness/intensity/contrast of structures in the VC SEM image, e.g. memory pillars, and the corresponding electrical property, e.g. conductivity, values in terms of resistance/capacitance. This methodology makes it possible to flag small fluctuations in the voltage contrast level of the VC SEM image and to optimize the defect inspection of a sample.

<FIG> shows steps of a method <NUM> for analyzing a sample using a synthetic electron microscope image <NUM> according to an embodiment.

In this method <NUM> a known lithographic mask data <NUM>, e.g. memory structures such as PCM, RRAM or MRAM, is used to generate a synthetic image <NUM>. Based on this synthetic image <NUM> and on expected electrical property information <NUM>, e.g. information of the memory structures, the synthetic VC or CC EM image <NUM> can be generated.

This simulated VC EM image <NUM> can be compared to a denoised recorded VC SEM image <NUM> that was recorded with a SEM in VC mode. Thereby, the defects can be detected based on differences in gray levels in between the synthetic and the recorded image.

Claim 1:
A computer- implemented method (<NUM>) for analyzing a sample, wherein the method (<NUM>) comprises the steps of:
- recording (<NUM>) an electron microscope, EM, image of a section of the sample with a scanning electron microscope, SEM, wherein the EM image is recorded in a voltage contrast mode of the SEM;
- denoising (<NUM>) the recorded EM image;
- performing (<NUM>) a spatially resolved inspection measurement of the section of the sample, wherein the inspection measurement comprises determining electrical property values, wherein the electrical property values are conductivity values; at different locations of the section of the sample;
- correlating (<NUM>) the pixel values of the denoised EM image (<NUM>) to the electrical property values determined at the different locations of the section of the sample;
- generating (<NUM>) a property map (<NUM>) based on said correlation, wherein the property map indicates the electrical properties of structures visible in the EM image (<NUM>); and
- determining a calibration curve (<NUM>) based on said correlation, wherein the calibration curve (<NUM>) correlates pixel values of the EM image (<NUM>) to electrical property values.