# Spectrum Imaging

Systematic method to generate a spatially resolved distribution of electron energy loss spectroscopy (EELS) data.

Overview:

Spectrum imaging (SI) is a technique that generates a spatially resolved distribution of electron energy loss spectroscopy (EELS) data. A typical experiment involves the creation of a data cube where two of the cube axes correspond to spatial information, while the third dimension represents the energy loss spectrum. The resultant dataset is referred to as a spectrum image (or spectrum line scan for the 1D case), which you can acquire and visualize in a number of ways. To create this data cube, you can acquire a complete spectrum at each spatial pixel during scanning transmission electron microscope mode (STEM SI) or collect a complete 2D image over a narrow band of energies at a single energy slice of the data cube during energy-filtered TEM mode (EFTEM SI).

A key advantage of spectrum imaging is the ability to process decisions after acquisition. When a complete spectrum is available at each data point, this allows creation of quantitative images and profiles to identify and correct data artifacts, understand image contrast, as well as determine dataset limitations.

During a STEM experiment, the electron beam focuses into a small probe, then scans over the sample to acquire spatial information (X,Y) in a serial manner. In the STEM SI mode, you can acquire a complete spectrum at each pixel position to build the spectrum image up on a spectrum-by-spectrum basis.

Alternatively, EFTEM SI uses a broad parallel beam (e.g., TEM) to acquire spectral data image plane-by-image plane, while changing the energy of each plane. In this mode, you acquire the image in parallel while the spectrum is built in a serial manner.

Once acquisition is complete, you can visualize the spectrum image (either EFTEM SI or STEM SI) either spectrum-by-spectrum (X,Y) or plane-by-plane ($\Delta$E). The combination of spectral and spatial information in a single dataset opens up a wide range of data analysis possibilities. You can apply any single spectrum analysis method to the entire spectrum image dataset to perform a spatially resolved spectral analysis. This increase in information provides a powerful tool for material analysis and characterization.