Low-voltage SEM contrasts of steel surface studied by observations and electron trajectory simulations for GEMINI lens system.

Scanning electron microscopy (SEM) is a powerful tool for observing the surface of materials. Modern SEM systems have multiple detectors with different geometries. Consequently, the SEM image contrast depends on the instrument and experimental conditions even for the same sample. Understanding the SEM imaging mechanism is necessary to clarify SEM contrast. In this paper, low-voltage (LV)-SEM image contrast is investigated by comparing LV-SEM images and electron trajectory simulation results. Surface observations of oxides on a steel surface, positive charging contrast and topographic contrast in the image systematically changed with the working distance (WD). The electron trajectory simulation revealed the sharing of emitted electrons by the in-lens and Everhart-Thornley detectors, and systematic changes in the electron sharing caused by changes in WD. The image contrast was reasonably explained by the kinetic energy and take-off angle (acceptance plots) of the detected electrons derived by the electron trajectory simulations. This approach is essential to understanding the SEM image contrast obtained by SEM systems with multiple detectors. Thorough image simulations based on acceptance plots are required in future work.

Contrast of positively charged oxide precipitate in out-lens, in-lens and in-column SE image.

Modern scanning electron microscopes are usually equipped with multiple detectors and enable simultaneous collection of two or even three secondary electron images. The secondary electrons become divided between the detectors in dependence on their initial kinetic energy and emission angle. In this study, sharing of the secondary electrons by out-lens, in-lens and in-column detectors has been systematically investigated. Energy filtering of the signal electrons is demonstrated by separation of the voltage and the topographical contrast in the micrographs obtained by out-lens and in-lens/in-column detectors. The presence of two detectors inside the electron column enables further filtering of the low kinetic energy secondary electrons, which results to unusual contrasts and phenomena. In this paper, inversion of the contrast sign between a positively charged oxide particle and conductive steel matrix (i.e. voltage contrast) in SE images collected under specific imaging conditions is demonstrated.

Quantitative FE-EPMA measurement of formation and inhibition of carbon contamination on Fe for trace carbon analysis.

Hydrocarbon contamination introduced during point, line and map analyses in a field emission electron probe microanalysis (FE-EPMA) was investigated to enable reliable quantitative analysis of trace amounts of carbon in steels. The increment of contamination on pure iron in point analysis is proportional to the number of iterations of beam irradiation, but not to the accumulated irradiation time. A combination of a longer dwell time and single measurement with a liquid nitrogen (LN2) trap as an anti-contamination device (ACD) is sufficient for a quantitative point analysis. However, in line and map analyses, contamination increases with irradiation time in addition to the number of iterations, even though the LN2 trap and a plasma cleaner are used as ACDs. Thus, a shorter dwell time and single measurement are preferred for line and map analyses, although it is difficult to eliminate the influence of contamination. While ring-like contamination around the irradiation point grows during electron-beam irradiation, contamination at the irradiation point increases during blanking time after irradiation. This can explain the increment of contamination in iterative point analysis as well as in line and map analyses. Among the ACDs, which are tested in this study, specimen heating at 373 K has a significant contamination inhibition effect. This technique makes it possible to obtain line and map analysis data with minimum influence of contamination. The above-mentioned FE-EPMA data are presented and discussed in terms of the contamination-formation mechanisms and the preferable experimental conditions for the quantification of trace carbon in steels.

Quantitative analysis of angle-selective backscattering electron image of iron oxide and steel.

The contrasts in backscattered electron (BSE) images, such as topographic, channeling and mean atomic number (Z) contrasts, were investigated quantitatively from the cross section of a heat-treated steel sheet using a scanning electron microscope (SEM). High primary electron energy (EP) enhances Z contrast, whereas low EP improves channeling contrast. A high take-off angle (θ; measured from the specimen surface) also enhances Z contrast, whereas low θ improves channeling contrast. When θ becomes very low, topographic information is enhanced and superimposed on channeling contrast due to the tilt effect of BSE. The relationship of the behaviors of the Z contrast and the channeling contrast can be understood by the detection ratio of low-loss electrons (LLEs) to the inelastic BSE components emitted from the sample surface; LLEs contribute to channeling contrast, and their ratio increases with decreasing EP and θ. The systematic results obtained in this study are useful for controlling SEM conditions in order to enhance the target information in BSE images for practical materials of interest.

Extraction of topographic and material contrasts on surfaces from SEM images obtained by energy filtering detection with low-energy primary electrons.

Secondary electron microscope (SEM) images have been obtained for practical materials using low primary electron energies and an in-lens type annular detector with changing negative bias voltage supplied to a grid placed in front of the detector. The kinetic-energy distribution of the detected electrons was evaluated by the gradient of the bias-energy dependence of the brightness of the images. This is divided into mainly two parts at about 500 V, high and low brightness in the low- and high-energy regions, respectively and shows difference among the surface regions having different composition and topography. The combination of the negative grid bias and the pixel-by-pixel image subtraction provides the band-pass filtered images and extracts the material and topographic information of the specimen surfaces.