A Multi-Task Neural Network Model for Simultaneous Classification of Light Intensity and Wavelength in 2D Photodetectors

Accurate extraction of light intensity and wavelength from photodetector responses is essential for decoding information, yet the intertwined influence of the two parameters on device performance poses significant challenges. This study presents a neural network-based model that extracts light intensity and wavelength directly from the current-voltage (I-V) characteristics of 2D materials-based photodetectors. Using a shared-bottom multi-task learning framework, a one-dimensional convolutional neural network (1D-CNN) was employed to capture feature representations from the I-V data, enabling precise classification and extraction of incident light parameters. The model was trained and evaluated using a dataset comprising 3750 I-V curves measured from a high-dynamic range and broadband WSe2/MoS2 heterojunction photodetector. The model exhibits an overall classification accuracy of 99.7%, highlighting the deep learning approach as a data-driven alternative to conventional analytical methods for modeling 2D photodetectors.