A two-dimensional MoS2 array based on artificial neural network learning for high-quality imaging

As the basis of machine vision, the biomimetic image sensing devices are the eyes of artificial intelligence. In recent years, with the development of two-dimensional (2D) materials, many new optoelectronic devices are developed for their outstanding performance. However, there are still little sensing arrays based on 2D materials with high imaging quality, due to the poor uniformity of pixels caused by material defects and fabrication technique. Here, we propose a 2D MoS2 sensing array based on artificial neural network (ANN) learning. By equipping the MoS2 sensing array with a "brain " (ANN), the imaging quality can be effectively improved. In the test, the relative standard deviation (RSD) between pixels decreased from about 34.3% to 6.2% and 5.49% after adjustment by the back propagation (BP) and Elman neural networks, respectively. The peak signal to noise ratio (PSNR) and structural similarity (SSIM) of the image are improved by about 2.5 times, which realizes the re-recognition of the distorted image. This provides a feasible approach for the application of 2D sensing array by integrating ANN to achieve high quality imaging.