The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience 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-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9 %, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy.

中文翻译：

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深度学习神经网络的电子能量损失谱数据库合成和铁损边缘识别自动化

电子能量损失光谱 (EELS) 光谱中编码的电离边缘支持高级材料分析，包括成分分析和元素量化。并行 EELS 仪器和快速、灵敏的检测器的发展大大提高了 EELS 光谱的采集速度。然而，传统的铁损边缘识别方式是基于经验和人工依赖的，这限制了处理速度。到目前为止，原始EELS光谱上的低信噪比和低跳变率一直是边缘识别自动化的挑战。在这项工作中，提出了一种卷积双向长短期记忆神经网络 (CNN-BiLSTM)，用于从原始光谱中自动检测和元素识别核心损耗边缘。通过使用我们的前向模型来合成 EELS 光谱数据库，以协助神经网络的训练和验证。为了使合成光谱与真实光谱相似，我们收集了一个大型实验获得的 EELS 核心边缘库。在综合训练库中，通过将多高斯模型拟合到实验中的真实边缘来对边缘进行建模，并模拟和添加噪声和仪器不完美性。训练有素的 CNN-BiLSTM 网络针对模拟光谱和从实验中收集的真实光谱进行了测试。网络的高精度（94.9%）证明，在不对原始光谱进行复杂预处理的情况下，所提出的 CNN-BiLSTM 网络实现了对 EELS 光谱的核心损耗边缘识别的自动化，具有高精度。