Deep learning for classifying quantum emission signals in WS2 monolayers using wavelet transform
Abstract
This study aimed to develop and evaluate deep learning approaches for the classification of quantum emission signals from WS2 monolayer nanobubbles across multiple spectral bands, addressing challenges in quantum materials characterization and spectral distinguishability assessment. We utilized a dataset of quantum emission signals ranging from 604 to 629 nm, emitted from WS₂ monolayer nanobubbles on gold substrates, categorized into four spectral bands (604.06-608.24 nm, 611.07-616.34 nm, 617.42-623.35 nm, and 624.16-636.57 nm). Our methodology involved signal preprocessing through normalization and moving average smoothing, followed by transformation into 128 × 128 RGB images using Continuous Wavelet Transform (CWT) with Complex Morlet wavelet. Three convolutional neural network architectures (ResNet50, VGG16, and Xception) were implemented and evaluated using fivefold cross-validation across six possible band pair combinations. All models demonstrated exceptional classification performance, with VGG16 achieving the highest overall mean accuracy of 99.4%, followed by Xception (99.1%) and ResNet50 (98.2%). Perfect classification accuracy (100%) was consistently achieved for spectrally distant band pairs, particularly Band 1 versus Band 4 (20.5 nm separation), while the most challenging classification involved adjacent bands (Band 2 vs. Band 3, 6.27 nm separation) with VGG16 achieving 96.5% accuracy. Statistical analysis using Friedman tests confirmed significant performance…
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