Peter Statham

The Determination of the Efficiency of Energy Dispersive X-Ray Spectrometers by a New Reference Material

A calibration procedure for the detection efficiency of energy dispersive X-ray spectrometers (EDS) used in combination with scanning electron microscopy (SEM) for standardless electron probe microanalysis (EPMA) is presented. The procedure is based on the comparison of X-ray spectra from a reference material (RM) measured with the EDS to be calibrated and a reference EDS. The RM is certified by the line intensities in the X-ray spectrum recorded with a reference EDS and by its composition. The calibration of the reference EDS is performed using synchrotron radiation at the radiometry laboratory of the Physikalisch-Technische Bundesanstalt. Measurement of RM spectra and comparison of the specified line intensities enables a rapid efficiency calibration on most SEMs. The article reports on studies to prepare such a RM and on EDS calibration and proposes a methodology that could be implemented in current spectrometer software to enable the calibration with a minimum of operator assistance.

Improved X-ray Spectrum Simulation for Electron Microprobe Analysis.

The accurate calculation of characteristic peak intensity is essential for interpreting X-ray spectra in electron microprobe analysis. Conventionally, the measured intensity from a standard of known composition is used as a reference to simplify the calculation. However, if no such standard is available, then all factors influencing X-ray generation and X-ray detection efficiency must be included. If the intensity and energy distribution of the background radiation can also be calculated, the investigator can simulate an entire spectrum from an assumed composition, gaining powerful benefits in setting up an experiment and in confirming the results. The study presented here demonstrates a fast method of spectrum simulation, suitable for energy-dispersive spectroscopy (EDS), and assesses the accuracy using 309 spectra from samples of known composition. These include K, L, and M lines from elements of atomic number 6-92, excited by beam energies in the range of 5-30 keV. The RMS error between 360 measured and calculated peak intensities was found to be 7.1%. Central to the method is the use of the ratio of peak intensity/total background intensity, which allows spectra to be compared from instruments of differing collection efficiency, thereby easing the collection of data over a wide range of conditions.

Can we detect Li K X‐ray in lithium compounds using energy dispersive spectroscopy?

Lithium is the key element for the development of battery and new technology and the development of an analytical technique to spatially and quantitatively resolve this element is of key importance. Detection of Li K in pure metallic lithium is now possible in the Scanning Electron Microscope (SEM) with newly designed Energy Dispersive Spectroscopy (EDS). However, this work is clearly showing, for the first time using EDS, the detection of Li K in several binary lithium compounds (LiH, Li3 N, Li2 S, LiF and LiCl). Experimental Li K X-rays intensity is compared with a specially modified Monte Carlo simulation program showing discrepancies between theoretical and experimental Li K measurements. The effect of chemical bounding on the X-rays emission using density functional theory with the all-electron linearized augmented plane wave is showing that the emission of Li K from the binary compounds studied should be, at least, 12 times lower than in metallic Li. SCANNING 38:571-578, 2016.

Windowless EDS Detection of N Lines and their Practical use in sub 2 kV X-ray mapping to Optimize Spatial Resolution

Reducing microscope accelerating voltage for elemental characterization by EDS improves the spatial resolution and surface sensitivity of results, with the potential to reduce emission volumes to less than 10 nm at 1 kV. In addition to instrumental challenges for SEM and EDS systems, the use of very low accelerating voltage reduces lines available for elemental analysis. Below 2 kV many common elements can no longer be analyzed without the use of low energy L lines (e.g. P-Cl), and M lines (e.g. Sr – Sn). This study considers whether N lines are sufficiently detectable and intense for useful nanocharacterization, for otherwise undetectable elements at 2 kV Hf – Bi, and 1 kV Ba – Yb.

Recent Developments in Instrumentation for X-Ray Microanalysis

This paper reviews the instrumentation advances for x-ray microanalysis 1991–1997, with particular emphasis on energy dispersive x-ray spectrometers. Most developments have been aimed at improved convenience and reliability while offering sensitivity well below 1 keV, particularly for semiconductor applications. Although EDX technology matured during the 1980’s, previous methods of characterisation are now inadequate to reveal the variability in performance as a function of x-ray energy in this low energy region. Furthermore, at low beam voltages where K lines are not excited, computer processing of peak overlaps is the only way to obtain element intensities. In this situation, detector and electronic stability and reproducibility have to be substantially improved in order to achieve results anywhere near the limit of statistical precision.

In-Situ Quantification of TEM Lamella Thickness and Ga Implantation in the FIB

Focused Ion beam (FIB) based tools have become the preferred method to prepare TEM lamellas, largely due to their high resolution imaging capabilities used to identify the site of interest. The quality and thickness of samples has become paramount in order to take full advantage of the ever increasing resolution in aberration corrected TEMs and accurately controlling the lamella thickness at the same time as minimising any amorphisation caused by ion implantation is challenging. For instruments combining a focused ion beam with an electron beam methods based on either back scattered electron contrast [1] or transmissivity of electrons [2] have been demonstrated. However, these methods only work on homogenous samples without compositional variations and require for the contrast to be calibrated using the same material. They also don’t provide any information on ion implantation or surface amorphisation and can greatly affect the quality of the TEM image obtainable from the lamella.

Systematic Discrepancies in Monte Carlo Predictions for Thin Surface Layers and Visualisation to Expose Root Causes

Thickness and composition of thin layers on a substrate can be determined by measuring the “k-ratio”, the ratio of intensity for a characteristic line to the intensity from a bulk pure specimen, for all emitted lines. Conversion from k-ratios to structure requires an accurate model for x-ray generation and this is commonly based on the assumption of a continuous phi-rho-z curve for each line. Useful weighting rules have been developed and successfully deployed to determine an “effective” phi-rho-z which works well for the analysis of many types of multilayer sample, although it is well recognised that this analytical model approach is questionable when there is a large atomic number difference between the various layers [1]. The obvious alternative is Monte Carlo (MC) simulation which can deal explicitly with sudden changes in material properties as individual electrons travel inside the specimen. The application of MC to stratified specimens is known to be useful in understanding how x-ray corrections behave for complex samples [2]. However, whereas analytical model approaches have converged on some programs that have been tested for practical analysis tasks, MC programs are often provided with many adjustable parameters and are usually intended as research tools rather than for routine analysis. With the widespread availability of some free MC programs and published examples showing excellent performance, there is an increasing expectation that these programs provide results that can replace real experiments. While there is no doubt these programs are useful, we would like to know if MC could ever match the accuracy of semi-empirical phi-rho-z programs for thin film analysis and deliver results well within 10% relative error. Therefore, we have investigated performance using the comprehensive data base of k-ratios measured on layered samples by Bastin & Heijligers [3].

Prospects for Single Standard Quantitative Analysis with SDD

For concentrations > 5 wt% and x-ray line energies > 0.93keV, Newbury [1] found 95% of relative errors within +/25% for standardless quantitative analysis from two unnamed commercial systems using Si(Li) detectors. No elements below Z=11 were included and oxygen concentrations were calculated for minerals by stoichiometry. Silicon drift detectors (SDD) are now an attractive alternative to Si(Li) and Burgess et al [2] used Newbury’s protocol for testing a commercial SDDbased system and showed 95% of relative errors were within +/10%. Both studies used normalisation to force the total to 100% which requires the operator to know all the elements present in the sample and also x-ray lines must be excited for all elements. Normalised results only provide corroboration for expected concentrations and give no indication of gross errors caused by missing or incorrect element identifications. If standards are used, normalisation is not necessary and independent estimates of concentration can be made for each element; if the analytical total is close to 100%, this provides an extremely useful indication that results are valid. The spectrometer efficiency should remain stable with time so that, after a one-off calibration using standards for every element under the same conditions, only a single standard measurement is needed to determine the intensities for all other standards at this condition. This one-off calibration using standards is still costly for the operator and a “factory default” calibration can be used on the assumption that all manufactured detectors will behave the same. When new detectors are developed, the factory default calibration needs to be adjusted by making assumptions about the change in detector efficiency. Fig.1 shows results from a set of analyses performed using an INCAx-act SDD and factory defaults in INCA Energy software suite 17b. The left hand histogram in Fig.1 shows the relative errors for a set of known reference materials using the same “standardless” protocol used in ref.[1], This confirms that standardless analysis with the SDD is capable of delivering results with errors < 10% and corroborates the conclusions of ref.[2]. However, many investigations involve concentrations below 5%, light elements such as oxygen and fluorine are not always combined stoichiometrically with heavier elements and an un-normalised total provides valuable information so a more exacting test is needed. When a single reference cobalt standard was used to obtain un-normalised results on the same system for elements >1% and with no stoichiometry assumptions, the histogram on the right of fig.1 shows a much wider spread and bias on the relative errors. This demonstrates that the assumptions about detector efficiency were not accurate enough so a new approach was taken to improve future “factory defaults”. A direct measurement of efficiency vs energy was made using the Bessy II synchrotron [3] for a particular Oxford Instruments Si(Li) detector that was subsequently treated as a “gold standard” reference detector (6400GS). A set of detector parameters was deduced, consistent with the manufacturing process, which predicted a theoretical absorption behaviour matching the measured efficiency. 6400GS was mounted on an SEM with a special tilt stage and take-off-angle was determined to within 0.5 degrees. A large series of measurements on reference standards were taken under conditions of stable beam current and spectrum synthesis [4] was used to expose potential outliers. This provided a new analysis calibration consistent with the measured 6400GS efficiency curve. Using intimate knowledge of the manufacturing processes employed for each type of detector, the relative efficiency of an INCAx-act SDD was calculated relative to the 6400GS Si(Li) and used to generate a new efficiency curve for the INCAx-act SDD. The new Microsc Microanal 15(Suppl 2), 2009 Copyright 2009 Microscopy Society of America doi: 10.1017/S1431927609096007 528

On the Detection Limits of Li K X-rays Using Windowless Energy Dispersive Spectrometer (EDS)

With the advent of commercially available EDS detector compatible with the detection of Li K X-ray (54 eV) it is very important to know the detection limits in order to determine if Li is present or if it can be detected (higher than the detection limits) For the K X-ray lines of higher atomic number this value is often easy to determine, or calculate and is around 0.1 wt%. However, several problems can be found for Li K X-ray; very low and unknown electronic efficiency (very low signal, absorption at the top surface layer of the detector), very high absorption of the top surface layer (contamination, surface oxide and roughness), emission function of chemical bonding, very few knowledge of the fundamental emission parameters (elastic cross-section, mass absorption coefficient, fluorescence yields, etc.). This work will present some of the parameters that could affect the Li K detection limits.

Progress in a New Method of Thickness Measurement by X-ray Analysis in TEM

A new method for measuring mass thickness ( t )sp, of a thin film specimen in a transmission electron microscope (TEM) uses a pre-calibrated thin film reference standard to avoid the need to measure beam current [1]. The beam current just needs to be stable for the duration of the analysis session and a single X-ray spectrum acquired from the reference film allows mass thickness and elemental composition to be determined from all subsequently acquired X-ray spectra. The mass thickness is automatically incorporated into the X-ray absorption correction. Mass thickness for an element is also proportional to the areal density (atoms/m 2 ) and can be used in estimation of beam broadening, modelling of image contrast, EELS quantification and determination of defect density.