Backscattered electron images: how to improve their quality

By Karl Kersten - September 21, 2018

Backscatter electrons (BSEs) carry information on the material of the sample. Obtaining high-quality images with a backscattered electron detector depends on many factors, such as the conductivity of the sample, its morphology and composition, the type of BSE detector and the electronics. Given a fixed system with the same detector and electronics— and the same sample, we analyzed the factors that play a role in the quality of a BSE image. Beginning with the number of integrating frames and beam intensity, in this blog we will also discuss the roles of the working distance and the chamber pressure.

BSE imaging in an SEM 

Backscattered electrons are generated from elastic scattering events of the primary beam electrons that travel very close to the nuclei of the specimen atoms. Read more about the formation of backscattered electrons and the detection of BSE in an SEM here.

Of course, the quality of BSE images depends on the type of detector and its electronics, and on the type of sample and whether it is conductive or not. But if you are using a specific microscope with a specific detector and electronics, which factors play a role in determining the quality of a BSE image?

How to measure the quality of a backscattered electron image

To compare the quality of images, one could calculate the so-called peak signal to noise ratio (PSNR). The PSNR is defined as follows:

backscattered electrons in SEM

Where 10log10 is the conversion in dB, nx and ny is the size in pixels of the image, r(x,y) is the reference image and t(x,y) is the test image. Essentially, in this formula, the maximum intensity of the pixels in the reference image (hence the signal) is divided by the difference in intensity between the reference image and the test image, hence the noise.

When acquiring an image, we know that the higher the number of the integrated frames the lower the noise, as shown in Figure 1. In this example, we acquired four images of the same area and we varied the number of integrated frames, from 1 to 4, 16 and 32. The higher the number of integrated frames, the higher the PSNR, as shown in the table:

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Here, the PSNR is calculated by taking as reference the image acquired with 32 frames.

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Fig 1: BSE images of the same area acquired with the same beam settings with different number of integrated frames, from 1 to 32

The influence of the probe current

Naturally, the quality of an image depends on the amount of current in the primary beam. Images acquired with lower current will be “noisier” than images acquired with large currents. In the example shown in Figure 2, four images of the same area acquired at the same beam acceleration voltage of 10 kV, but with different beam intensities are shown. These beam intensities correspond to beam currents of approximately 180pA, 330pA, 0.9nA and 5.7nA.

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Fig 2: BSE images of the same area acquired with the same beam voltage and number of integrated frames, at different beam intensities: low, image, point and map.

In this case, the PSNR is calculated by taking as reference the image acquired with 5.7nA, or the largest beam intensity. The resulting PSNR values are shown in the following table:

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As expected, the higher the beam intensity (or beam current), the higher the PSNR.

The influence of the working distance

While the working distance has no effect on the number of backscattered electrons generated, the quality of BSE images is affected by the distance between the detector, which is placed at the pole piece, and the sample. In the schematics shown in Figure 3, the sample is placed both at a short working distance WD1 and a large working distance WD2. The collection angle that the detector “sees” is larger in the first case (α) than for large working distance (β).

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Fig 3: Schematics of different geometry, where the working distance is varied, from short (WD1) to large (WD2). With the short working distance, the collection angle is larger than that of the large working distance.

The increase in working distance affects the collection angle of the BSE detector, as shown in Figure 4. As α is the collection angle at the shortest working distance, the ratio between the angle α and β, the angle at WD2, increases linearly with the working distance. This means that the further away the sample is positioned, the smaller the collection angle of the backscattered electron detector, the “noisier” the image, as shown in Figure 5.

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Fig 4: Ratio between the angle at the shortest working distance α and the angle at large working distance β for different sample position.

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Fig 5: The effect of the working distance on the noise in the BSE image. The scale bar is 5 µm.

So, if we look at images acquired at different working distances, with the same beam settings on the same area, we can calculate the increase in noise taking the image acquired at a short working distance as a reference image. In Figure 6, four images of the same area acquired at working distances from 7.69mm to 10.7mm are shown.

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Fig 6: On the left, the reference image is taken at a working distance of 7.69 mm. The other three images of the same area are taken with the same beam settings at different working distance (8.47mm, 9.48mm and 10.7mm). The horizontal field of view in the four micrographs is 179 µm. 

The PSNR, calculated by taking the image acquired at the shortest working distance as reference, as a function of working distance is shown in Figure 7. As expected, the noise increases (so the signal-to-noise ratio decreases) with the working distance. The larger the working distance, the “noisier” the images.

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Fig 7: PSNR calculated from the images in Figure 6.

The influence of the chamber pressure

The chamber pressure also influences the quality of the backscattered electrons images. In high vacuum, the BSE emitted from the sample have more chances to reach the detector without being scattered from air molecules, as shown in Figure 8 on the left. In low vacuum, there are many more air molecules with which the BSEs interact and are scattered, without being collected by the BSE, as shown in Figure 8 on the right. Given the same sample, working distance and beam settings, in low vacuum the images are “noisier” than in high vacuum.

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Fig 8: The effect on the collection of BSEs by the detector due to high chamber pressure (left) and low chamber pressure (right).

To demonstrate this, we acquired images at different acceleration voltages (5kV and 10kV) for different chamber pressures (1Pa, 10Pa and 60Pa), as shown in Figure 9. The reference image is acquired at 1Pa chamber pressure, by integrating 32 frames. The other images are acquired by integrating four frames.

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Fig 9: BSE images acquired in the same area, at the same working distance, by varying the chamber pressure (1Pa, 10Pa and 60Pa) for 5Kv and 10kV acceleration voltage. The reference image is acquired at the same settings as that at 1Pa, but with a larger number of integration frames. The horizontal field of view in these micrographs is 26.9 µm.

The PSNR calculated from these images shows that the noise increases when the chamber pressure increases, as shown in Figure 10. For the 10kV beam, the PSNR decreases by a factor of 6% when the pressure increases from 1Pa to 60Pa. In the same conditions, for the 5kV primary beam energy, the PSNR drops by a factor of 30%.

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Fig 10: PSNR calculated for images acquired at different chamber pressure for 5kV and 10kV incident beam, shown in Figure 9.

 More on SEM

In this detailed description, we described how to improve the quality of BSE images when using an SEM. Has this made you curious about the technology behind scanning electron microscopy? And do you want to find out how you can select the right microscope for your research? Then it’s good to know that we have a comprehensive e-guide on How to Choose an SEM available for you.

The e-guide details the working principle of an SEM and enables you to gain a deeper understanding of the microscopy technique that produces high-quality images faster and easier. It helps you to choose the SEM that is most suitable for your research. I highly recommend taking a look at the guide; it’s available here for free.

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About the author

Karl Kersten is head of the Thermo Scientific Phenom Desktop SEM Application Team at Thermo Fisher Scientific. He is passionate about the Phenom Desktop SEM product and likes converting customer requirements into product or feature specifications so customers can achieve their goals.

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