Signal Decoupling and Readout in Multimodal Sensing: Materials to Algorithms.

Multimodal sensing is increasingly important for next-generation electronic systems, yet multi-physical coupling across materials, devices, and readout chains often compromises signal identifiability. The key challenge is whether coupled observations can be uniquely, stably, and interpretably mapped back to the underlying physical variables. Here, we organize multimodal sensing according to where decisive identifiability is established along the sensing-readout chain, defining three architectures: Partition-Integrated, Continuum-Routed, and Hybrid Co-Decoupling. Partition-Integrated systems establish stimulus-channel correspondence before substantial mixing; Continuum-Routed systems preserve distinguishable signatures within a shared sensing body; and Hybrid Co-Decoupling systems distribute separation across physical encoding, readout, and computation. Representative strategies are compared in terms of mixed-stimulus validation, residual cross-sensitivity, calibration dependence, stability, and generalization. Finally, we highlight opportunities in front-end identifiability, system resilience, and low-power intelligence toward reliable multimodal sensing under realistic operating conditions.