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Spectrally selective photodetection has been used in many areas where only band‐specific optical signals are aimed to be detected. Organic photodiodes (OPDs) based on internal light depletion mechanisms have been utilized to realize narrowband photodetection. However, the fabrication of the photoactive layers by conventional solution methods can result in the deterioration of the underneath organic depletion layers and cause high‐density defect states at their interface. Herein, a new approach is presented to realize efficient charge transfer by adopting a self‐assembly strategy to prepare donor/acceptor bulk heterojunctions as the photoactive layers for narrowband OPDs. This method is able to prepare highly smooth, phase‐uniform, and low‐defect‐density thin films with controlled thickness. With this strategy, a narrowband near‐infrared OPD centered at 760 nm with a full‐width‐at‐half‐maximum of around 60 nm, peak external quantum efficiency of 49%, and peak specific detectivity of over 1013 Jones under −5 V is achieved. Multiple material combinations for the depletion and photoactive layers are examined, and the results show the effectiveness to realize the spectrally selective photodetection (from 740 to 900 nm) in general. More importantly, the narrowband OPDs can be integrated into a matrix for imaging, which demonstrates the mass‐scale fabrication feasibility of these OPDs.

We propose a physical information neural network with learning rate decay strategy (LrD-PINN) to predict the dynamics of symmetric, asymmetric, and antisymmetric solitons of the self-defocusing saturable nonlinear Schrödinger equation with the PT-symmetric potential and boost the predicted evolutionary distance by an order of magnitude. Taking symmetric solitons as an example, we explore the advantages of the learning rate decay strategy, analyze the anti-interference performance of the model, and optimize the network structure. In addition, the coefficients of the saturable nonlinearity strength and the modulation strength in the PT-symmetric potential are reconstructed from the dataset of symmetric soliton solutions. The application of more advanced machine learning techniques in the field of nonlinear optics can provide more powerful tools and richer ideas for the study of optical soliton dynamics.