We describe a method for predicting detection limits of minority
elements in electron energy loss spectroscopy (EELS), and its
implementation as a software package that gives quantitative
predictions for user-specified materials and experimental
conditions. The method is based on modeling entire energy loss
spectra, including shot noise as well as instrumental noise,
and taking into account all the relevant experimental parameters.
We describe the steps involved in modeling the entire spectrum,
from the zero loss up to inner shell edges, and pay particular
attention to the contributions to the pre-edge background. The
predicted spectra are used to evaluate the signal-to-noise ratios
(SNRs) for inner shell edges from user-specified minority elements.
The software also predicts the minimum detectable mass (MDM)
and minimum mass fraction (MMF). It can be used to ascertain
whether an element present at a particular concentration should
be detectable for given experimental conditions, and also to
quickly and quantitatively explore ways of optimizing the
experimental conditions for a particular EELS analytical task.
We demonstrate the usefulness of the software by confirming
the recent empirical observation of single atom detection using
EELS of phosphorus in thin carbon films, and show the effect
on the SNR of varying the acquisition parameters. The case of
delta-doped semiconductors is also considered as an important
example from materials science where low detection limits and
high spatial resolution are essential, and the feasibility of
such characterization using EELS is assessed.