Color Sensing and Image Reconstruction Using Intelligent Machine Learning Algorithm with PINIP Radial Junction Imager

The development of a filterless imager has been eagerly awaited to overcome the diffraction limit when pixel sizes decrease to subwavelength scales. We propose an architecture for a filterless imager based on a symmetric inversely stacked radial junction (RJ) PINIP photodetector over silicon nanowires (SiNWs), whereby the diameter of which is less than 500 nm, which preliminarily displays the capability of bias-selected and tunable spectrum responses to the R, G, and B color bands. Assisted via suitably trained deep learning algorithms, the imager can provide more accurate color discrimination and imaging capabilities. Here, we used KNN (k-nearest neighbor) and convolution neural network (CNN) methods to retrieve the RGB ratios from the measured photocurrent value based on the pre-trained bias-tuned spectrum responses and reconstructed the images with high accuracy. Further, we demonstrated the capability of restoring sub-sampling pictures via CNN with a U-net architecture, and satisfactory reconstruction was obtained even with a sampling ratio as low as 20%. Our imaging scheme cannot only be used for high-resolution imaging but can also pave the way for application in single-pixel imaging and compressive sensing.