By Attolight
Hyperspectral imaging captures full spectra at each pixel, creating data cubes for advanced material analysis. It's used in optoelectronics, failure analysis, and process development to identify layers and defects. Hyperspectral imaging is an acquisition mode in which a full spectrum is recorded for every pixel in an image. The result is an information-rich map, called a data cube, that can be used to visualize differences in a sample's spectral signature across a field of view. The data cube can also be sliced to obtain images within different wavelength bands. Hyperspectral imaging is commonly used in cathodoluminescence (CL) but also in many other spectroscopy techniques. CL can involve both imaging and spectroscopy aspects, depending on the application. The most basic approach to CL imaging is known as panchromatic imaging, which produces a 2D greyscale image, where the brightness of each pixel represents the overall CL intensity emitted by the sample across all wavelengths. The most basic approach to CL spectroscopy involves recording individual point spectra, where the electron beam is directed to a single point on the sample and the resulting cathodoluminescence intensity is recorded as a function of wavelength (λ). Hyperspectral imaging combines both aspects described above to provide an information-rich data set that can be used for advanced analysis beyond what is possible with standard imaging or spectroscopy alone. To create a hyperspectral image, a full CL spectrum is recorded serially at each pixel of an image in order to build up a 3D hyperspectral data cube. The first two dimensions of the cube are the x and y axes of the image, representing the field of view on the sample. The third dimension is the wavelength axis, representing the CL intensity at each particular wavelength, or "color", of light. This is illustrated schematically in Figure 1. Figure 1: Visual representation of an individual panchromatic image, a single spectrum, and a hyperspectral data cube, in which each pixel of the image corresponds to a full CL spectrum. Because of the complexity of data contained within a hyperspectral map, there are many ways to visualize it. One could, for example, explore the individual spectra at each pixel, or integrate the spectra over a subset of the image area. One could also create a color image by assigning different wavelength ranges to different colors in a single image (see Figure 3) or create a series of 2D monochrome images (one for each wavelength or wavelength range, see Figure 4). Hyperspectral imaging can be useful for a variety of applications, including identifying different layers in a film stack, quantifying inhomogeneities in an alloy, classifying defects based on their emission spectrum or even identifying the presence of strain around morphological features. The instrumentation required to create a hyperspectral image is simply the standard equipment required for CL spectroscopy: a scanning electron beam (whether SEM or STEM), a light collection system including lenses for collimation and focusing, and a spectrometer equipped with a spectroscopy camera (though a rotating grating and a point detector such as a PMT is also possible). A schematic of a typical setup is shown in Figure 2. Figure 2: Typical instrumentation for CL hyperspectral imaging The diffraction grating inside the spectrometer can be selected to provide the optimum efficiency and spectral resolution for the desired wavelength range. Typically, a CCD camera is used to record the intensity at each wavelength simultaneously. Benefits and Further Reading - Identification of material composition, including gradients and interfaces [2] - Identification and classification of defects [3] - Quantifying material inhomogeneity [4] - Correlation between strain and morphological features - Failure analysis - Correlation between microstructure and optoelectronic properties [5]
