Polarization-multiplexed diffractive neural networks for multi-task classification based on liquid crystals

All-optical diffractive neural networks have attracted extensive attention due to their characteristics of high parallelism, high processing speed, and low energy consumption. However, most existing DNN frameworks are designed for single-task operations and lack the flexibility to handle multiple tasks within an artificial intelligence (AI) system. Here, we propose polarization-multiplexed diffractive neural network (PMDNN) based on liquid crystals for multi-task classification. By incorporating liquid crystal into the neural network in the form of Jones matrix and encoding multi-task inputs into multiple polarization channels, the neurons are endowed with polarization modulation capabilities. This approach extends multi-task processing capacity and enhances the polarization multiplexing capability of liquid crystals without complex structural design. As a demonstration, a 3-task PMDNN was designed to perform classification on the MNIST, Fashion-MNIST, and KMNIST datasets. Consistent simulation and experimental results verify the effectiveness of the proposed network framework. Furthermore, we have designed multi-task PMDNNs for more task classification. These results demonstrate that the proposed framework maintains minimal inter-task crosstalk under increasing task complexity. The proposed PMDNN architecture enhances the flexibility of diffractive neural networks, paving the way for the realization of ultra-fast, low-power, and multi-task integration AI systems.