TOF‐SIMS Data: Introduction, Information Content, and Retrospective Analysis - mikee9265/SIMS-Wiki GitHub Wiki

In many cases the analyst knows exactly what to look for in the data. Peaks of interest have already been identified, added to a working peak list, and images were obtained even as the data was being acquired. Relative peak intensities from spectrum to spectrum or images within a single data set can be added to a report, and the analysis is complete.

Often, however, it is not possible to thoroughly plan the analysis at the outset. The effort is exploratory or unknowns are involved. In other cases, the signal to noise for individual peaks is not sufficient for the task, and it proves better to group many peaks together after the analysis is complete. In these cases it is necessary to more thoroughly explore the data the instrument has produced, both directly and via statistical methods. All this is made possible because the instrument will store all of the raw data, raster position, time of arrival at the detector relative to the primary pulse, and the point in the analysis at which the secondary ion was detected.

Information Content in TOF-SIMS Data

Every secondary ion detected comes with the following:

  • The time it takes from the primary gun’s pulse to the arrival of an ion at the detector is saved. This is translated via a mass calibration into a mass to charge ratio (m/e), which in most cases is simply the mass of the detected secondary ion.

  • The plot of m/e versus counts places every secondary ion in the context of a spectrum. Every secondary ion has a distance in m/e from other peaks in the spectrum. The patterns of peaks in the spectrum can have significance beyond the peak positions in the spectra.

  • The position of the primary ion beam on the sample (x, y) within the raster is saved. The combination of raster positions for all of the ions in a given peak gives the ion image for that peak.

  • The time of the pulse that produced each secondary ion is saved. When producing static data, this information is not particularly useful. When passing the static limit, or when the analysis is coupled with the use of a sputter gun, the time of the pulse becomes significant. Every mass range can be monitored for intensity changes through the course of the analysis. When a sputter beam is used, these plots amount to depth profiles.

Every peak in the spectrum or selected mass range can be used to produce an image. Every pixel in an image has an associated spectrum, and therefore spectra can be reconstructed from any selected area within an image. Patterns can be found in the spectra, and similarly, patterns may be evident in the images. Profiles of intensity versus analysis time can be made for any mass range, and similarly a profile for every mass is available for every pixel. When the sample is eroded during the analysis, this is the basis for 3D imaging.

This wealth of data also allows the analyst to correct some anomalies in the data. Charging effects not completely eliminated via charge compensation may broaden peaks due to differential charging either from location to location or in depth. Mass calibration can be performed differently for different areas/depths to compensate for the effects differential charging can have on ion flight times, thus sharpening spectra otherwise broadened by these effects. Similarly, height differences and other topographical effects can be somewhat compensated in this way.

Retrospective Analysis

Retrospective analysis best begins with a check of the mass calibration, because during the analysis, the calibration is often performed with only a subset of the data, but at this stage, the entire data set is available for calibration. This is obviously more important when the data has been acquired in a mode that allows for higher mass resolution. In cases where the sample is not completely flat or level, or when the analysis area is large, recalibration may also be appropriate when taking region of interest (ROI) spectra. IonTof software also has an “advanced TOF correction” routine, which relies on the fact that in principle each pixel has an associated mass spectrum, and thus each could have its own mass calibration. In practice there is often not enough data at each pixel to do independent mass calibrations, but the routine allows for the binning of as many pixels as it takes to get localized mass calibration corrections.

The acquisition of spectra and images from the raw data is a process that closely resembles the initial data acquisition. Obviously, it is not possible to change instrument settings such as primary ion species, mass range, and so on, at this stage. It is possible, however, to specify new mass intervals from within the full range to obtain new images and profiles. It is also possible to choose a subset of the area analyzed, a portion of a depth profile, or a volume from the 3D rendering of the data from which to obtain a spectrum.

Advances in software for some systems now allows changes to be made while an acquisition is in progress, thus allowing the addition of a new peak/mass interval, or the adjustment of an existing one, the selection of an area as an ROI, the grouping of mass intervals, etc. After the acquisition is completed, such changes can be made, and the results viewed without "replaying" the data stream.

Data sets can be quite large. The time of flight of each secondary ion is recorded by placing each ion that arrives at the detector into a channel, typically 156 ps long. Each ion is also associated with a pixel in an image that can range vastly in pixel count (especially for large area scans). For 3D data, the pixels are really voxels, separate from each other in x, y, and z. Uncompressed, such data sets can span many gigabytes. The data is, however, sparse. That is, many channels will have zero counts. Many more pixels will have zero counts from a given channel. Further, the fact that counts accumulate in a given channel, and not in the neighboring channel, is often purely a matter of chance. The same secondary ion will have counts spread across a number of channels. Similarly, the presence of counts in one pixel and not in a neighboring pixel may also be a matter of chance because the pixel size will often be significantly smaller than the primary ion beam spot size.

It is a common procedure when analyzing these data sets to bin data. In the image, this is easy. Neighboring pixels (usually in a square) can be combined to reduce the number of pixels. In the spectra, the process is a little more complicated as the relationship between the channel number and the mass is not linear. Nonetheless, after mass calibration it is a common procedure to rebin the data in set mass units (0.1 amu, or ditching mass accuracy, in 1 amu bins). A more meaningful approach is to attempt to bin the spectral data into peaks. In this exercise, one is trying to group counts together that represent a single secondary ion type. This is the approach taken by many other mass spectrometry methods. The challenge for time of flight secondary ion mass spectrometry (TOF-SIMS) is not only that the channel scale is nonlinear relative to the mass scale, but also that peak intensities can vary over more than six orders of magnitude. It has been a struggle to make accurate and reliable algorithms that will define peaks even as well as the human eye. The good news is that the instrument software does a reasonable job of this. The bad news is that after the software has defined a peak list/mass interval list for you, it is still a good idea to look though the spectrum and make sure the software has left nothing that you would find significant out. The effort is worthwhile in many cases, especially when samples have unknowns or where subtle differences in surfaces are under study.

Given a peak list, one can quickly explore the data for clues. When the data consists of a series of spectra of relatively homogeneous samples or sample areas, an exploration of the differences between the spectra in the series may immediately reveal the answers sought. In case of inhomogeneous sample areas, the analyst turns to an examination of the images generated from each of the peaks in the list for clues. When clearly different regions are revealed in the images, the identities of the peaks that map the different areas may be sufficient to answer the question posed. In cases where the relative intensities of the peaks mapping for distinct regions in the ion images help identify the materials in each region, one can obtain spectra for each of these areas. ROIs can be defined in the instrument software using a variety of techniques. Regions can be selected by manually drawing shapes on an ion image. Peak intensities can be used to specify regions in which the pixels have a defined range of intensities for a given peak or set of peaks. Sometimes when the signals are intense, the raw intensities can be used. In others, image-smoothing filters should be applied before defining a region. In any case, once a region is defined, the spectrum for just that region can be readily obtained. As a practical matter, in most instances, the qualitative analysis of the data can stop here.

Quantitation can follow if you have standards of similar and known surface composition. The relationship between signal intensities and concentration may or may not be linear, but even nonlinear calibration curves can be used. As noted earlier, this can be done with samples that have been analyzed by other methods (e.g., Auger, or X-ray photoelectron spectroscopy {XPS}). In order to do this, there are two prerequisites. First, the peaks you will use must not be saturated and the appropriate dead time corrections must be performed where needed. Simple dead time corrections are available in the instrument software. These may not be sufficient, however, especially in cases where multiple peaks are present at the same nominal mass; where there is a substantial metastable signal preceding a peak, dead time corrections that come directly from the instrument are not currently adequate (Tyler 2014; Tyler and Peterson 2013). In cases where peaks are too intense to be dead time corrected, they should simply not be used in the analysis. One can reduce the need for dead time corrections by simply using a lower primary ion flux and reducing the secondary ion count rate. Second, the absolute intensities must somehow be made comparable from spectrum to spectrum.