ANALYZING LINE SCAN EELS DATA WITH NEURAL PATTERN-RECOGNITION

Neural Pattern Recognition was used for extracting chemical state info rmation from electron energy-loss (EEL) spectra. The purpose was to ob tain a quantitative composition profile from sets of low-loss and core -loss EEL spectra measured along a line across an amorphous inclusion at a grain boundary in a silicon bicrystal. The spectra were presented serially to the artificial neural network to obtain the number and sh ape of the spectra, whose linear combinations reproduce each single sp ectrum. The results indicate the existence of a different chemical env ironment at the interfaces between inclusion and crystal. The data ana lysis proved to be fast, robust, relatively immune to noise or artifac ts and capable of extracting relevant information from subtle spectral features.