Matthew Hiscock

Advances in the Analysis of Gunshot Residue and Other Trace Evidence using EDS and EBSD in the SEM

Scanning Electron Microscopy (SEM) based analysis is a standard tool in the analysis of trace evidence providing morphological and, in combination with EDS, also chemical information down to the sub micron scale. Many forensic science laboratories use SEM-EDS to analyse for gunshot residue (GSR) with samples taken from suspected shooters’ hands, hairs and clothing. Other commonly analysed samples include broken glass, soil from suspects’ shoes or paint. The aim of the analysis is to either conclusively link the evidence to a suspected source by matching chemical and morphological characteristics or to rule out such connections. In the case of GSR automated analysis is used to acquire morphological (from the electron image) and chemical (EDS) data from suspected GSR particles. Based on this data particles are then automatically classified according to ASTM 1588-10 ε1 as either characteristic or consistent with GSR. This classification was relatively straight forward when most ammunition types used lead bearing primers which resulted in large populations of easy to detect, characteristic particles. However, with the advent of non-toxic ammunition, the particle populations typically detected have changed and fewer or no classically characteristic particles may be found in samples taken from shooters. Here we show that by adding data from EBSD analysis we can positively identify phases associated with the high temperatures and pressures associated with firing a handgun using non-toxic ammunition. Furthermore, we discuss how advances in both the software and hardware enable more accurate automated element identification at high speed which in turn allows us to link particle populations to the presence of a particular type of non-toxic ammunition which has so far only been achieved using principle component analysis of EDS spectra [1].

New Developments in Automated Particle Analysis in the Electron Microscope - from Micro to Nano

As EDS detectors have become faster, computers more powerful and data storage space cheaper, EDS analysis has become more image centric. X-ray mapping is now used routinely and can be combined with automated stage movements and stitching of maps, so that large sample areas can be covered at high resolution. These very large data sets contain a wealth of information which so far has often not been fully utilized. A new Particle Analysis software (AZtecFeature) enables the automated processing of these large data sets in order to identify features of interest in the sample and measure their morphology and composition. As in conventional particle analysis, the electron image is used to identify the location of features. Different to conventional particle analysis, the new particle analysis software has the option to link a set of electron images to a set of EDS maps of the same area, from which it extracts the compositional information. As these maps can cover the whole sample area, the new particle analysis system can store a virtual sample (figure 1a). This virtual sample can be analyzed offline (figure 1b), enabling the user to adjust threshold settings and also to investigate features which span more than one field of view. Storing and working with virtual samples enables their sharing amongst research groups over the internet, avoiding the need for transportation of rare or precious samples.

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.

Local thickness and composition analysis of TEM lamellae in the FIB

Abstract High-resolution TEM image quality is greatly impacted by the thickness of the TEM sample (lamella) and the presence of any surface damage layer created during FIB–SEM sample preparation. Here we present a new technique that enables measurement of the local thickness and composition of TEM lamellae and discuss its application to the failure analysis of semiconductor devices. The local thickness in different device regions is accurately measured based on the X-ray emission excited by the electron beam in the FIB–SEM. Examples using this method to guide FIB–SEM preparation of high quality lamellae and to characterise redeposition are shown for Si and III–V semiconductor devices.

Separation of Hard to Distinguish Phases in Automated Feature Analysis

Automated feature analysis in the SEM is a powerful technique which is widely applied across many applications. The technique relies on having a method for determining where the features to be analyzed are. This could be a case of separating features from the mounting medium or from other features, which may be a different phase but which sit adjacent to or touch the feature of interest. In many scenarios this task is successfully carried out using a backscattered electron (BSE) image – taking advantage of the phase density contrast in the images to separate the phases. Figure 1 shows this scenario.

Automated Analysis of EDS Maps

With increases in EDS detector speed and the use of multiple EDS detectors on one SEM, EDS is rapidly moving from being a purely spectroscopic technique to becoming an imaging technique. As the beam is rastered over the sample and an X-ray spectrum is recorded at every pixel an X-ray map is built up that can qualitatively indicate the presence of different phases in the sample (Figure 1b). If the spectra acquired during X-ray mapping are live-time corrected, they can be processed and quantified like any other spectrum. This enables the reconstruction and quantification of spectra from features of interest in maps. So far this reconstruction has generally been a manual process where the user defines the area from which to reconstruct and then examines the spectrum and draws conclusions about the presence of certain phases. Here we discuss the combination of image processing and classification with EDS mapping tools to create a virtual sample from which morphological and chemical features can be automatically determined.

Enhancing Materials and Device Analysis Capability in the SEM and FIB-SEM by using a Nanomanipulator

Nanomanipulators, whilst commonly used to lift out TEM samples prepared in the FIB, have a number of other potential applications and can aid in the chemical and crystallographic analysis of samples. Here, we discuss some of these alternative uses of nanomanipulators and show how they enable the EDS and EBSD analysis of thin samples in the FIB-SEM as well as serve as electrical probes and enable charge dissipation on uncoated samples.

Characterization of Layer Thickness and Orientation of 2D WSe 2 /MoS 2 Heterostructures using EDS, EBSD and AFM

2D transition metal chalcogenides enable exciting new applications in electronic devices and show great promise to replace traditional silicon technology as functional building blocks [1]. However, in order to realize this potential there is a range of fabrication and integration challenges that have to be overcome and suitable, non-destructive characterization techniques are needed. Due to their high resolution, electron optical characterization in scanning electron microscopes (SEMs) and atomic force microscope is ideally suited. We show how a full structural and compositional characterization can be obtained by combining EDS, EBSD and AFM analysis.

Characterization of Two-Dimensional Transition Metal Dichalcogenides in the Scanning Electron Microscope Using Energy Dispersive X-ray Spectrometry, Electron Backscatter Diffraction, and Atomic Force Microscopy

Two-dimensional (2D) transition metal dichalcogenides enable exciting new applications in electronic devices and show great promise to replace traditional silicon technology as functional building blocks (Butler et al., 2013). However, in order to realize this potential there is a range of fabrication and integration challenges that have to be overcome and suitable, non-destructive characterization techniques are needed. Due to their high resolution, electron optical characterization in scanning electron microscopes (SEMs) and atomic force microscopy (AFM) is ideally suited. Also, SEM is already used extensively in the characterization and failure analysis of conventional semiconductor devices. Therefore, an extension of the same analysis technique to devices based on 2D materials will enable easier integration of 2D materials into standard production and quality control processes. Here we show how a full structural and compositional characterization can be obtained by combining energy dispersive X-ray spectrometry (EDS), electron backscatter diffraction (EBSD), and AFM analysis including the number of layers present in the 2D material which is critical to its performance.

Automated SEM Analysis in Industrial Process Control and Scientific Research

While automated SEM analysis has been used for specific tasks such as gunshot residue analysis and technical cleanliness analysis for many years, recent advances in hardware and software make it a compelling proposition for a much wider group of researchers. Automated analysis in this context refers to solutions that enable users to define analysis tasks that are then carried out repeatedly, sometimes over large sample areas (Figure 1) or even multiple samples. The automation solution comprises control of the microscope stage, electron beam and the analysis equipment such as an EDS detector during a run as well as data reporting tools. Analysis areas are identified automatically based on user defined criteria involving sophisticated image processing. Data from both the EDS detector and imaging detectors containing both morphological and compositional information can then be combined to classify analysis areas according to set criteria.