Liquid-crystal-based reconfigurable structural nonlinearity

Traditional neural networks (NNs) require substantial computing resources and energy. Photonics-based systems offer faster and more energy-efficient solutions. However, implementing nonlinear activation functions in photonics has been challenging due to the need for high-power optical sources and extended interaction lengths. The proposed solution uses structural nonlinearity, creating nonlinear output patterns with low energy use and simple digital NN training. The research develops a reconfigurable material platform using liquid crystal/polymer composite (LCPC) and metasurface to control scattering potentials dynamically. These results show that the LCPC’s phase distribution can be reliably controlled, enabling reconfiguration and repetition of scattering responses, which is crucial for advancing photonics-based neuromorphic computing.