Deep Learning Inverse Design of Phase-Change Reconfigurable Terahertz Metadevices for Multidimensional Secure Communication.

Abstract

The exponential rise in data exchange and cyber threats in next-generation 6G networks demands communication systems that are inherently secure at the physical layer. Terahertz (THz) waves combine huge bandwidth with strong directionality, offering a fertile platform for high-capacity and covert data transfer. Here, we introduce a deep-learning-enabled inverse-design framework that enables the creation of dynamically reconfigurable THz metadevices capable of adaptive, multidimensional encryption. By using a residual neural network trained to directly map target electromagnetic responses to device geometries across continuous material phase transitions, our approach eliminates traditional iterative design bottlenecks and enables rapid, high-precision generation of versatile meta-architectures. The resulting devices exhibit multiplexed control over polarization, depth, and phase transitions in Ge₂Sb₂Te₅ (GST), enabling eight-channel encrypted holography with minimal crosstalk and near-diffraction-limited fidelity. Furthermore, we realize a reconfigurable diffractive THz neural metadevice that performs universal logic operations under a dual-key security protocol, requiring both the physical hardware and a digital key sequence for accurate decryption. This combination of intelligent design automation and physical-layer encryption establishes a new paradigm for secure, high-capacity, and adaptive THz communication, paving the way for dynamically reconfigurable wireless architectures in 6G and beyond.