Inverse design of thermally tunable metasurfaces for selective multi-channel terahertz wavefront manipulation
The emergence of deep neural networks (DNNs) has greatly alleviated the time-consuming and phase-discretization problems in conventional metasurface design processes. However, most DNN-assisted design methods are constrained by predefined target electromagnetic (EM) parameter formats, making retraining unavoidable when the design objective changes. To address this, we develop an objective-configurable inverse design framework paired with thermally tunable metasurfaces for multi-channel terahertz (THz) wavefront manipulation. The designed metasurface combines anisotropic structural responses with thermally tunable VO 2, providing four independent linear polarization (LP) channels through polarization and state multiplexing. The inverse design framework couples a residual convolutional neural network forward surrogate model with an estimation-of-distribution algorithm implemented via the cross-entropy method. By reusing the same surrogate model and reconfiguring only the design objective, different channel combinations and wavefront functions can be selectively activated, which allows on-demand multi-channel wavefront manipulation. As proof-of-concept demonstrations, four addressable LP channels are realized, and two additional circular polarization (CP) channels are further introduced through adaptive phase allocation. With all four LP channels activated, four-channel letter hologram multiplexing is achieved on a single metasurface, with an average imaging efficiency of 70.8%. After extension to CP channels, six-channel wavefront manipulation is achieved with inter-channel crosstalk below 30%. By integrating a thermally tunable metasurface with surrogate modeling and probabilistic optimization, this work establishes a robust and scalable paradigm for next-generation reconfigurable multi-functional THz photonic devices.