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The ionization edges encoded in the electron energy loss spectroscopy (EELS)spectra enable advanced material analysis including composition analyses andelemental quantifications. The development of the parallel EELS instrument andfast, sensitive detectors have greatly improved the acquisition speed of EELSspectra. However, the traditional way of core-loss edge recognition isexperience based and human labor dependent, which limits the processing speed.So far, the low signal-noise ratio and the low jump ratio of the core-lossedges on the raw EELS spectra have been challenging for the automation of edgerecognition. In this work, a convolutional-bidirectional long short-term memoryneural network (CNN-BiLSTM) is proposed to automate the detection and elementalidentification of core-loss edges from raw spectra. An EELS spectral databaseis synthesized by using our forward model to assist in the training andvalidation of the neural network. To make the synthesized spectra resemble thereal spectra, we collected a large library of experimentally acquired EELS coreedges. In synthesize the training library, the edges are modeled by fitting themulti-gaussian model to the real edges from experiments, and the noise andinstrumental imperfectness are simulated and added. The well-trained CNN-BiLSTMnetwork is tested against both the simulated spectra and real spectra collectedfrom experiments. The high accuracy of the network, 94.9 %, proves that,without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTMnetwork achieves the automation of core-loss edge recognition for EELS spectrawith high accuracy.

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks