Characterisation, Measurement & Analysis
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  • What is an FFT Spectrum Analyser?

    FFT Spectrum Analysers, such as the SRS SR760, SR770, SR780 and SR785, take a time varying input signal, like you would see on an oscilloscope trace, and compute its frequency spectrum. Fourier's theorem states that any waveform in the time domain can be represented by the weighted sum of sines and cosines.The FFT spectrum analyser samples the input signal, computes the magnitude of its sine and cosine components, and displays the spectrum of these measured frequency components.

    Click here to download the full Application Note.


    If you would like more information, to arrange a demonstration or receive a quotation please contact us via email or call us on 01582 764334.

  • Nano Mechanical Imaging

    The nano mechanical imaging (NMI) mode is an extension of the contact mode. The static force acting on the cantilever is used to produce a topography image of the sample. Simultaneously, at each pixel force curves are produced and used to extract quantitative material properties data such as adhesion, deformation, dissipation...

    Click here to read the complete article.


    To speak with a sales/applications engineer please call 01582 764334 or click here to email

    Lambda Photometrics is a leading UK Distributor of Characterisation, Measurement and Analysis solutions with particular expertise in Electronic/Scientific and Analytical Instrumentation, Laser and Light based products, Optics, Electro-optic Testing, Spectroscopy, Machine Vision, Optical Metrology, Fibre Optics and Microscopy.

  • New Baumer LX Cameras now with integrated JPEG compression

    We are pleased to announce that we are now delivering an on-board image compression video camera, which is able to transmit high-quality images and reduce the data output in real-time.

    Baumer is supplementing the hugely popular LX series with 2, 4 and 25 megapixel cameras with integrated JPEG image compression and frame rates of up to 140 fps. With the GigE cameras, your savings are continual: from bandwidth through CPU load to storage space – this simplifies the system structure design and reduces integration costs.

    Why not give us a call now on 01582 764334 if you would like a free trial of the camera or click here to email.

    Further information on our Machine Vision camera series click here.

    Lambda Photometrics is a leading UK Distributor of Characterisation, Measurement and Analysis solutions with particular expertise in Electronic/Scientific and Analytical Instrumentation, Laser and Light based products, Optics, Electro-optic Testing, Spectroscopy, Machine Vision, Optical Metrology, Fibre Optics and Microscopy.

  • LED Lighting Techniques

    The future belongs to LED technology. The long lifetime and high energy efficiency of these devices form the main reason for changing over to this technology for illumination requirements in Machine Vision.

    Depending on what the requirements are in terms of price, performance and flexibility, the user can find the best solution in the market using this state-of-the-art technology.

    The user has a number of options available to them for target illumination.

    Click here for a useful guide to help you choose the right lighting for your Machine Vision application.

    Alternatively why not contact our Machine Vision specialists on 01582 764334 or click here to email.

    Lambda Photometrics is a leading UK Distributor of Characterisation, Measurement and Analysis solutions with particular expertise in Electronic/Scientific and Analytical Instrumentation, Laser and Light based products, Optics, Electro-optic Testing, Spectroscopy, Machine Vision, Optical Metrology, Fibre Optics and Microscopy.

  • SEM automation guidelines for small script development: evaluation

    Scripts are small automated software tools that can help a scanning electron microscope (SEM) user work more efficiently In my previous two blogs, I wrote about image acquisition and analysis with the Phenom Programming Interface (PPI). In this blog I will explain how we can use the physical properties we obtained in the last blog in the evaluation step.

    SEM automation workflows

    Typically, SEM workflows always consist of the same steps, see Figure 1. The four steps that can be automated using PPI are:

    1. Image acquisition
    2. Analysis
    3. Evaluation
    4. Reporting

    In the image acquisition step (1), images are automatically made using PPI and the Phenom SEM (read this blog for more information on this step). In the analysis step (2), the physical properties are extracted from the image (see this blog) .The images are evaluated based on these physical properties in the evaluation step (3). The final automated step (4) is reporting the results back to the user.

    Figure 1: Scanning Electron Microscopy workflow

    Image evaluation

    In the evaluation step, the physical quantities are evaluated and categorized. This can be done by:

    • Counting particles based on their morphology
    • Determining the coverage on a sample
    • Base actions on physical properties of the sample

    In this blog we will base action on the physical properties in an image to determine where the center of the copper aluminum stub is.

    To do this we will assume that the copper insert is perfectly round. The script will start at a location pstart within the copper part of the stub. From here it will move in both positive and negative x and y directions to find a set of four edges points of the copper insert. These points will be Schermafbeelding 2018-07-05 om 12.17.40. Because of the circular symmetry of the stub, the arithmetic average of the x positions of Schermafbeelding 2018-07-05 om 12.20.01 and the y-position of Schermafbeelding 2018-07-05 om 12.20.45 will yield the center Schermafbeelding 2018-07-05 om 12.21.08 of the stub. In Figure 2 all the points are shown.


    Figure 2: Definitions of the locations on the stub

    To find the edges, the stage is moved. In every step the image is segmented using the techniques explained in the previous blog. When less than 50% of the image consists of the copper part, the edge is located. The exact position of the edge point is then defined as the center of mass of the area that is neither copper nor aluminum.

    Figure 3: Definitions of the locations on the stub

    Code snippet 1 shows an example of how this can be done. First the stage is brought to its original starting point with the Phenom.MoveTo method. This position is retrieved back from the Phenom using the phenom. GetStageModeAndPosition command. After that, the step size is defined. A step of 250 µm is chosen, which is equal to half the image field width. Four vectors are defined in all directions to find the four edges. These vectors are combined into an iterable list, to be able to iterate over them in the for loop.

    In the for loop, the stage is first moved to an initial guess of the location of the center. Then, a while loop is started where the stage moves to one direction with the step size. At every step the image is segmented and checked if the area of copper is smaller than 50%. If the copper area is less than 50%, the edge has been found and the center location of the edge is determined using ndimage.measurements.center_of_mass method.

    The resulting center of mass is expressed in pixels and is converted to metric units using the metadata that is available in the Phenom acquisition objects. The centers of masses are stored in a list and from this list the Schermafbeelding 2018-07-05 om 13.09.38 and Schermafbeelding 2018-07-05 om 13.10.07 locations are determined. From the set of locations, the arithmetic averages are easily determined, and the stage is moved to its new improved center location.

    Code snippet 1: Code to find and to move to the center of the stub

    In Figure 4, the initial guess of the location of the center is shown on the left-hand side and the improved center location is shown on the right-hand side. Iterating this process a few times could improve the center location even further; this because the symmetry will improve towards the center of the stub.

    Figure 4: Definitions of the locations on the stub


    In code snippet 2, the complete code is shown, including the code from my two previous blogs.

    Code snippet 2: Complete code

    Click here to learn more about SEM automation and the Phenom Programming Interface

    Topics: Scanning Electron Microscope Automation, Industrial Manufacturing, Automation, PPI, Automated SEM Workflows

    About the author:

    Wouter Arts is Application Software Engineer at Thermo Fisher Scientific, the world leader in serving science. He is interested in finding new smart methods to convert images to physical properties using the Phenom desktop SEM. In addition, he develops scripts to help companies in using the Phenom desktop SEM for automated processes.

  • Buying a scanning electron microscope: how to select the right SEM

    You want to buy a new scanning electron microscope (SEM) because you know you need more SEM capability. Maybe you have a traditional floor model SEM, but it is slow and complicated to operate. Maybe you are using an outside service and the turn-around time is unacceptably long.

    You have made your case that your company could significantly improve their business performance and you could do your job better if SEM imaging and analysis were easier, faster and more accessible. Can a desktop SEM do what you need? This article provides the answers and helps you to select the right SEM.

    Floor model SEM vs. Desktop SEM

    The choice between a desktop SEM and a larger, floor model system is almost always primarily an economic one: desktops are much less expensive. But there are other factors that also argue in favor of a desktop solution, even when cost is not the primary consideration.

    Scanning electron microscopes: pricing & affordability

    Let’s deal first with SEM pricing. Desktop SEMs are typically priced at a fraction of their floor model relatives. And there are certainly situations in which the additional cost of the larger systems are justifiable, for example, when the resolution requirements are beyond those achievable in a desktop SEM system.

    However, today’s desktop SEM’s can deliver resolutions smaller than 10 nm, enough for 80%-90% of all SEM applications. So your first question has to be, is it enough for yours?

    Beyond the initial acquisition, there are significant additional costs for a floor model scanning electron microscope system:

    • facilities – typically at least a dedicated room (perhaps including specialized foundations and environmental isolation)
    • additional space and equipment for sample preparation; personnel – a dedicated operator, trained in instrument operation and sample preparation.

    It is worth noting that while the cost of the equipment and facility are primarily fixed costs of acquisition, the operator is an ongoing expense that will persist for the lifetime of the instrument.

    Clearly, a desktop SEM solution — less costly to acquire and with no requirement for a dedicated facility or operator — is the less expensive choice, as long as its capabilities satisfy the requirements of the application.

    Other decision factors when selecting and buying a scanning electron microscope

    • Microscope speed
      Desktop SEM systems require minimal sample preparation and their relaxed vacuum requirements and small evacuated volume allow the system to present an image much more quickly than a typical floor model system.Moreover, desktop SEMs are usually operated by the consumer of the information, eliminating the time required a dedicated operator to perform the analysis, prepare a report and communicate the result.In addition to faster answers, there is considerable intangible value in the immediacy of the analysis and the user’s ability to direct the investigation in real-time response to observations.Finally, in some applications, such as inspection, longer delays carry a tangible cost by putting more work-in-progress at risk.
    • Microscope applications
      Is the application routine well defined? If it is, and a desktop SEM can provide the required information, why spend more? Concerns about future requirements exceeding the desktop capability should be evaluated in terms of the certainty and timing of the potential requirements and the availability of outside resources for more demanding applications.Even in cases where future requirements will exceed desktop capability, the initial investment in a desktop SEM can continue to deliver a return as that system is used to supplement a future floor model system.Perhaps in a screening capacity or to continue to perform routine analyses while the floor model system is applied to more demanding applications.A desktop system may also serve as a step-wise approach to the justification of a larger system, establishing the value of SEM while allowing an experience-based evaluation of the need and cost of more advanced capability from an outside provider.
    • Microscope users
      How many individuals will be using the system? Are the users trained? If not, how much time are they willing to invest in training? Desktop SEMs are simple to operate and require little or no sample preparation. Obtaining an image can be as easy as pushing a couple of buttons.More advanced procedures can be accessed by users with specific needs who are willing to invest a little time in training. In general, the requirements for operator training are much lower with a desktop system and the system itself is much more robust. It is harder to break, and the potential repair cost is much lower.

    Buying a scanning electron microscope: take-aways

    Now a short recap. The primary decision factors when selecting a SEM are:

    • Pricing
    • Speed
    • Applications
    • Users

    The question to ask yourself while going over these factors is: does a desktop SEM meet my application requirements?

    From experience we can say that it will, in most scenarios. If a desktop SEM is indeed suitable for your application, you’re looking at an investment that’s significantly lower compared to a floor model SEM.

    Remember, desktop systems are typically priced at a fraction of their floor model relatives.

    As I stated earlier there are situations in which the additional cost of larger systems is justifiable. This is the case when the resolution requirements are beyond those achievable in a desktop system.

    However, today’s desktop SEMs can deliver resolutions less than 10 nm — enough for 80%-90% of all SEM applications. So the question will often be: is it enough for yours?

    If that’s a difficult question to answer — or if you’re still just in doubt which SEM to choose — we have an e-guide available that should be of help: how to choose a SEM.

    This guide takes an even deeper dive into the selection process of a SEM, and will help you select the right model for your process and applications.

    Topics: Research Productivity, Scanning Electron Microscope, Pricing

    About the author:

    Karl Kersten is head of the Application team at Thermo Fisher Scientific, the world leader in serving science. He is passionate about the Thermo Fisher Scientific product and likes converting customer requirements into product or feature specifications so customers can achieve their goals.

  • Sputter coating for SEM: how this sample preparation technique assists your imaging

    Scanning electron microscopes (SEMs) are very versatile tools that can provide information at the nanoscale of many different samples - with little or no sample preparation. In some cases though, sputter coating the samples prior to working with SEMs is recommended, or even necessary, in order to get a good SEM image. In this blog, we will explain how the sputter coating process works, and to which type of samples it should be applied.

    How does sputter coating work?

    As mentioned above, SEMs can image almost all kind of samples; ceramics, metals and alloys, semiconductors, polymers, biological samples and many more. However, certain types of samples are more challenging and require an extra step in sample preparation to enable the user to gather high-quality information from a SEM. This extra step involves coating your sample with an additional thin layer (~10 nm) of a conductive material, such as gold, silver, platinum or chromium etc.

    When a metallic target material is bombarded with heavy particles, the erosion of this material begins. Sputtering occurs when the erosion process takes place in conditions of glow discharge between an anode and a cathode. In this way, and by careful selection of the ionization gas and the target material, an additional thin layer (¬ 10nm) of a conductive material, such as gold, silver, platinum or palladium will coat your sample.

    Challenging samples that require sputter coating:

    • Beam-sensitive samples
      The first type of samples that are usually sputter-coated prior to loading in the SEM are the beam-sensitive samples. These are mainly biological samples, but they can also be other types, such as materials made from plastics. The electron beam in a SEM is highly energetic and, during its interaction with the sample, it carries part of its energy to the sample mainly in the form of heat. If the sample consists of a material that is sensitive to the electron beam, this interaction can damage part or their entire structure. In this case, sputter-coating with a material that is not beam-sensitive can act as a protective layer against such kind of damage.
    • Non-conductive materials
      Another class of materials that is frequently subjected to sputter coating is non-conductive materials. Due to their non-conductive nature, their surface acts as an electron trap. This accumulation of electrons on the surface is called “charging” and creates the extra-white regions on the sample that can be seen in Fig1a, which can influence the image information.

    In order to remove this artefact, a common approach is to lower the vacuum level inside the chamber. This introduces positively-charged molecules near the surface of the sample. These interact with the charging electrons and neutralise them, thereby removing this charging effect. This has proven to be an effective approach, however the air molecules that are introduced in the vacuum chamber interact with the primary electrons reducing the quality of the image.

    For this reason, if a high-quality electron image is required, the use of sputter coater is recommended; the conductive coating material acts as a channel that allows the charging electrons to be removed from the material. In Figure 1b you can see how the charging effect has been removed with the application of a gold coating.

    Figure 1: a) Charging effect on a non-conductive sample and b) BSD imaging of this sample after 10 nm gold coating.

    In some cases, the sputter coating sample preparation technique can be used to improve image quality and resolution. Due to their high conductivity, coating materials can increase the signal-to-noise ratio during SEM imaging and therefore produce better quality images.

    The drawbacks of sputter coating for SEM

    As can be easily understood, there are a few concerns when it comes to using sputter coating for SEM imaging. Initially, it requires additional time and effort by the user to define the optimal coating parameters.

    However, there is an even more important downside of sputter coating; the surface of the sample does not contain the original material but the sputter-coated one, and therefore the atomic number-contrast is lost.

    In some extreme cases, it may lead to altered surface topography or false elemental information about the sample. Nevertheless, in most cases, the parameters of the sputter coating procedure are carefully selected and these issues do not appear and therefore the user is able to acquire high-quality images that carry the type of information that is required.

    Which materials should you use to sputter-coat your sample?

    Historically, the most used sputter coating material has been gold, due to its high conductivity and its relatively small grain size that enables high-resolution imaging. Also, if EDX analysis is required, SEM users typically coat their samples with carbon because carbon’s X-ray peak does not conflict with the peak of any other element.

    Nowadays, people are also using other coating materials with even finer grain sizes such as tungsten, iridium or chromium when ultra-high resolution imaging is required. Other coating materials include, platinum, palladium and silver, with the latter having the advantage of reversibility.

    It goes without saying that certain type of samples need some extra steps of sample preparation to achieve the best possible result in the SEM.

    Topics: Sample Preparation, Sample Degradation

    About the author:

    Antonis Nanakoudis is Application Engineer at Thermo Fisher Scientific, the world leader in serving science. Antonis is extremely motivated by the capabilities of the Phenom desktop SEM on various applications and is constantly looking to explore the opportunities that it offers for innovative characterization methods.

  • Automated scanning electron microscopy (SEM) imaging: how it's used

    In a previous blog, we described how automating scanning electron microscopy (SEM) imaging saves researchers and operators valuable time. A lot of scanning electron microscope users use this for a wide range of purposes. This blog shows an example of how automated SEM imaging is used in the field: it details performing an automated Laser-Induced Damage Threshold test (LIDT).

    Automated SEM imaging: accelerating a Laser-Induced Damage Threshold test

    Intense laser light can damage optical components like mirrors, optical coatings, or fibers. For the selection of the right optical components, it is important to find out what dose of energy causes damage to a component, or permanently changes its optical characteristics.

    Figure 1: BSD SEM image of laser-induced damage on an optical coating

    To determine the exact effect of specific doses of energy, a Laser Induced Damage Threshold test is performed. The optical component is exposed to different intensities and wavelengths of laser light in a grid pattern.

    After being exposed to laser light, the component is inspected for damage using different types of optical microscopes and scanning electron microscopes. The grid can contain hundreds of different points — and each point has to be inspected.

    Performing this test manually would demand a lot of your time. In this situation, automated microscopy can be a solution that can help save valuable time.

    How to acquire SEM images automatically

    With a programmable interface, a script is created to acquire images automatically for each point with SEM. The script works by uploading a list of coordinates that is created by the laser. You then calibrate the stage on two points, after which the script proceeds to image each point at a selected magnification.

    Figure 2: User interface of the LIDT scan script: the small red and green dots represent points where the optical coating was exposed to laser light.

    All the acquired images are stored in the selected folder for you to inspect. If a specific point requires closer inspection, that point can easily be found by clicking on it in the user interface. This way, you can spend more time on the actual analysis than on the acquisition of your SEM images.

    Automating this process saves you time Now, you can just click on the images and check if there is any damage.

    If you would like to know how scanning electron microscopes with automation capabilities — click  Phenom XL and Programming Interface.

    Topics: Scanning Electron Microscope Automation, Automation, PPI, Automated SEM Workflows

    About the author:

    Karl Kersten is head of the Application team at Thermo Fisher Scientific, the world leader in serving science. He is passionate about the Thermo Fisher Scientific product and likes converting customer requirements into product or feature specifications so customers can achieve their goals.

  • Introducing the Latest Innovation in Fibre-optic MPO/MTP Polarity Testing Solutions

    We are pleased to announce the OP415 Polarity Analyser from OptoTest - the latest innovation in polarity testing solutions.

    This polarity tester was designed to test 24-fibre MTP/MPO cable assemblies efficiently, but is easily configured to test for 8-fibre and 12-fibre cables. It is pre-loaded with 12-fibre and 24-fibre polarity types A, B, and C plus the ability to create and store custom fibre mappings and channel configurations. Additionally the OP415 can learn polarity types from existing cables and store those for future use.

    Most customers will be interested in automatic testing, but the OP415 Polarity Analyzer also has a manual mode to step through a cable channel by channel - a useful feature for troubleshooting or routing fibres during ribbonizing. Bright red laser sources on each channel provide visual fault detection for ribbon cables.

    The full colour touchscreen display graphically shows if fibres are routed incorrectly or are not connected, and can even display a power level for each channel to detect poor connections. On-board data storage allows users to save results for later analysis.

    Based in Camarillo, California, OptoTest strives to be at the forefront of the fibre optics industry with solid fundamental measurement technologies for optical power, insertion loss, return loss, and launch condition. The company maintains a tradition of breakthrough products and innovative solutions for the testing and analysis of fibre optics components and systems. Lambda Photometrics are proud to represent OptoTest in the UK and welcome the opportunity to share our fibre testing experience with potential customers.

    If you would like more information, to arrange a demonstration or receive a quotation for the OP415 Polarisation Analyser, please contact us via email, our website or call us on 01582 764334.

  • The Phenom Process Automation: mixing backscattered and secondary electron images using a Python script

    When the primary beam interacts with the sample, backscattered electrons (BSEs) and secondary electrons (SEs) are generated. Images of the samples obtained by detecting the emitted signals, carry information on the composition (for BSE signals) and on the topography (for SE signals). How are BSEs and SEs formed and why do they carry specific information? Moreover, is it possible to get both compositional and topographical information in one image? And how flexible is this solution? In this blog, I will answer these questions and introduce a script that allows users to mix their own images.

    When the primary beam hits the sample surface, secondary electrons and backscattered electrons are emitted and can be detected to form images. Secondary electrons are generated from inelastic scattering events of the primary electrons with electrons in the atoms of the sample, as shown on the left of Figure 1. SEs are electrons with low energy (typically less than 50eV) that can be easily absorbed. This is the reason why only the secondary electrons coming from a very thin top layer of the sample can be collected by the detector.

    On the other hand, backscattered electrons are formed from elastic scattering events, where the trajectories of primary electrons are deviated by the interaction with the nuclei of the atoms in the sample, as shown on the right in Figure 1. BSEs typically have high energy and can emerge from deep inside the sample.

    Figure 1: Formation of secondary electrons (on the left) and backscattered electrons (on the right). SEs are formed from inelastic scattering events, while BSEs are formed from elastic scattering events.

    Secondary electrons images contain information on the topography of the sample. As shown on the left of Figure 2, the beam is scanned on top of a surface that has a protrusion. When the beam is located on the slope of this protrusion, the interaction volume touches the sidewall causing more secondary electrons to escape the surface. When the beam is located on the flat area, fewer secondary electrons can escape. This means that more secondary electrons will be emitted on edges and slopes, causing brighter contrast than on flat areas, providing information on the morphology of the sample.

    On the other hand, the backscattered electrons yield depends on the material, as shown on the right of Figure 2. If the beam hits silicon atoms, which have atomic number Z=14, fewer backscattered electrons will be formed than in the case of gold, which has atomic number Z = 79. The reason for this is that gold atoms have bigger nuclei, providing a stronger effect on the primary electrons’ trajectories, which translates into a bigger deviation. Backscattered electrons images therefore provide information on the material difference of the sample.

    Figure 2: On the left, more secondary electrons can escape the sample surface on edges and slopes than in flat areas. On the right, the yield of backscattered electrons depends on the atomic number of the material, more BSEs are generated in gold (Z = 79) than in silicon (Z = 14).

    To collect the secondary electrons, the Everhart-Thornley detector (ETD) is typically used. Because SEs have low energy, a grid at high potential is placed in front of the detector to attract the secondary electrons. On the other hand, BSEs are often collected by a solid-state detector placed above the sample. The images obtained by the ETD detector and the BSD detector contain information on the morphology and the composition of the sample respectively.

    For some applications, however, it is convenient to have information on both the topography and the composition, in one image. This can be done by simply adding the signal coming from the two detectors.

    Mixing BSE and SE images

    When an image is acquired, the beam scans the sample surface pixel by pixel. In each pixel, the signal is collected by the detector and translated into a value. If images are acquired in 8 bits, the range of pixel values varies from 0 to 255. If images are acquired in 16 bits, the values of each pixel can vary from 0 to 65,535.

    The value of the pixel depends on how many secondary electrons or backscattered electrons are emitted and the higher the value of the pixel, the brighter the pixel appears in the image. This means that in the case of the sample shown in Figure 2 on the left, the edges will appear brighter in the image because more SEs are emitted and therefore the pixels in that position will have a higher value.

    Mixing backscattered electron and secondary electron images means that the two images are summed together. In practice, each pixel in the SE image is summed to the corresponding pixel in the BSE image, using the formula:

    Where ratio is the percentage of how much SE and BSE information the mixed image will carry. For equal topographic and compositional information, the ratio will be equal to 0.5.

    On the left of Figure 3 you can see SE (top) and the BSE (bottom) images of a solar cell, where the white area is silver and the dark area is silicon. In the SE image, the topography of the sample is clear: the granular structure of the silver strip can be easily noticed, as well as the bumpy silicon surface. Of course the ETD detector picks up some BSE signal as well, which is the reason why there is a difference in contrast between the two materials.

    In the BSE image, the topography of the sample is less visible. However, the material contrast is enhanced, and also shows some dirty particles on the silver strip. On the right of Figure 3, the mixed image is shown. In this case, we used a ratio of 0.5, meaning that each pixel value contains 50% of topographic information and 50% of compositional information. Not only are all the particles with different material contrast visible, but also the surface roughness of the strip and the silicon area.

    Figure 3: An example of mixing images. On the top left, the SE image and on the bottom, the BSE image of a solar cell, where the silver stripe (bright area) can be distinguished from the silicon (dark area). While the SE image carries information on the topography, in the BSE image the material contrast is dominant. On the right is the resulting mixed image using a ratio of 0.5.

    The mixed imaging script

    Being able to generate and save mixed backscattered and secondary electron images is a key value in many applications. Not only that, being able to set the SE:BSE ratio is also important for obtaining flawless images, that provide valuable information to the user.

    Using the Phenom Programming Interface (PPI), we developed a script that can acquire BSE and SE images directly from the Phenom SEM and mix them together, as shown in Figure 4. It is also possible to load BSE and SE images that were previously saved with the Phenom and generate and save the mixed image offline.

    Figure 4: User interface of the mixed images script, developed with PPI.

    Are you an experienced programmer interested in knowing more about the Phenom Programming Interface and its functionalities? Click here for further information.

    If you are not familiar with programming, but would still like an automated solution for your workflow, then we can help by developing the solution for you.

    Topics: Scanning Electron Microscope Software, Automation, PPI, Automated SEM Workflows

    About the author:

    Marijke Scotuzzi is an Application Engineer at Phenom-World, the world’s no 1 supplier of desktop scanning electron microscopes. Marijke has a keen interest in microscopy and is driven by the performance and the versatility of the Phenom SEM. She is dedicated to developing new applications and to improving the system capabilities, with the main focus on imaging techniques.

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