TOF‐SIMS: Ion Images - mikee9265/SIMS-Wiki GitHub Wiki

For every ion found in a TOF-SIMS spectrum, in addition to the time of arrival at the detector, the instrument will keep track of the X and Y coordinates within the raster of the ion beam. The instrument will also keep track of the sequence of the ions detected though this information is not important when keeping within the static SIMS limit. For ion beams with large spots, the beam will be moved across the surface during analysis to ensure even exposure of the surface being analyzed to the beam, but for smaller spots, the instrument will generate meaningful images. In principle, one could generate an ion image for every channel in the spectrum, but generally it is more meaningful to bin channels that correspond to a single ion species, or where necessary, multiple ion species whose signals cannot be resolved.

In most cases, an ion image is easy to understand as pixels with higher ion counts correspond to locations on the surface with higher concentrations of the species from which the ion originates. An ion image is often depicted in a thermal scale, with black pixels having no ions, white representing the maximum count of ions found in a single pixel within the image, and a gradient from black to dark red to light red to dark yellow to light yellow to white displaying visually the continuum of ion counts in the pixels. The following figure shows examples of ion images displayed using the thermal scale described above.

The ion images are less commonly represented as grey scale images (possibly because they are then likely to be confused with secondary electron microscope (SEM) images that are usually displayed in grey scale). So, then, an area of the image with a cluster of white pixels represents an area with a higher concentration of the species represented by the imaged ion rather than a surrounding darker region. It is natural to interpret the visual scale as representative of concentration.

There are numerous exceptions to the rule that ion count varies proportionately or even in the same direction as concentration. This is because of the matrix effects previously mentioned. If the surface is sufficiently inhomogeneous with different regions having drastically different chemistries, ion intensities can change across the surface with no change in the originating species’ concentration. In some cases, ion intensities can be higher in locations where the actual concentration of the material on the surface is lower.

A common situation in which the interpretation of ion images is counterintuitive is the case in which there are trace contaminants on a surface with varying base substrate materials. A wafer with gold pads is a good example. Ion yields on the Au surface will be strongly enhanced relative to the yields obtained from the surrounding wafer when that wafer is coated with a much lower atomic number species such as Si, silica, or alumina. Traces of the surrounding wafer material (Si or Al in this example) present on the Au pad may be more intense than the same atomic signals obtained from the surrounding area where the element is a major component. Trace organic contaminants present everywhere on the wafer will produce much more intense ion signals on the Au pad. The higher atomic number of Au keeps the collision cascade close to the surface, enhancing ion yields. The Au surface is relatively noble, so bonding to the surface is typically weak, making desorption of nascent ions easier. Finally, Au does adsorb a thin oxygen layer from the air, which acts as a decent barrier to electron tunneling, a mechanism that destroys more secondary ions than any other. As a consequence, Au is an ideal substrate for ion for producing high ion yields; often orders of magnitude higher ion yields than will be produced from surrounding materials. For these types of samples, ion intensity definitely cannot be equated with concentration.

One of the principal responsibilities of the SIMS analyst is to have a healthy skepticism of simple interpretations of ion images. In most laboratories there will be types of samples that are generally introduced into the instrument. Sometimes divergence from the maxim “ion intensity is proportional to concentration” will be expected from first principles (as in the case with Au pads). In other cases, the analyst knows that the regions of the sample have completely different chemistries, but the effect this will have on ion yields is not obvious. In many of these situations, it may be worth checking the SIMS result against those from another technique, even if unusual samples with larger areas must be produced so that methods having poorer lateral resolution can be employed.

TOF-SIMS data consists fundamentally of the following information: x, y, t (time of arrival at the detector after the primary ion pulse), and the point during the analysis at which the secondary ion was detected (the raster or analysis cycle). All of this is stored in what is known as the raw data file. Sort all of the data into time channels, mass calibrate the data, and you have the total spectrum for the analysis. Sort all the ions into their respective pixels, and you have the total ion image. Choose a subset of pixels, and sort only the ions that were detected when the ion beam was pointed at those locations in their respective time channels, and after calibration you have a spectrum that corresponds only to that region of interest (ROI). Choose a subset of time channels that correspond to a peak in the spectrum, and you may produce a single ion image.

The use of the raw data to explore regions of the image or to produce ion images from any ion detected during the analysis is a powerful data analysis technique. The following sequence, utilizing the raw data, is a common one employed by analysts to answer that age-old question ”what is that?”

1. Acquire data from an area that includes the feature of interest.

2. Examine the total area spectrum, looking for peaks of interest. Define these as peaks in the instrument software. Alternatively, you can have the instrument software automatically identify a peak list. Another approach is to use a master peak list that you typically see for this type of sample (if this sample is a common variant on samples typically analyzed in you laboratory).

3. Sort the defined peaks into separate images using the raw data.

4. Examine the ion images to determine, where in the rastered area, is an area of particular interest. It may be a physical feature discernable in the total ion image. Often, it is an area with specific chemistry of interest defined by one or more ion images.

5. Define an ROI. You can do this by literally drawing a shape on an ion image. Or, you can take an “intensity slice” of an image. You may be interested only in pixels that have a minimum intensity of a peak of interest. Or you may be more interested in an area from which a species is excluded. The instrument software will allow you to define your ROI as needed.

6. Replay the raw data again if needed, this time looking only to acquire the spectrum from your ROI (or in some cases, multiple ROIs).

7. Examine the spectrum (or spectra) obtained. Now you can have a better idea of peaks that are only associated with your ROI(s). The relative intensities of peaks will be clearer.

8. Optionally, you can replay the raw data, getting ion images for those peaks that you now find significant, but had not before, based on the ROI spectra.

It is a drawback for the TOF-SIMS that there is no separation before analysis, so unlike in the GC-MS tool, mixtures of materials are the norm. The image data makes up for some of that lack of chromatography. Lateral inhomogeneity in mixtures of materials at surfaces is also the norm. The use of raw data allows the analyst to make full use of the variability that will be found across the sample surface.