Ferroelectric‐Assisted van der Waals Charge‐Dynamics Reservoir Arrays for Spatiotemporal Computing

ABSTRACT Physical reservoir computing offers an energy‐efficient framework for spatiotemporal processing by exploiting nonlinear dynamics and fading memory. Two‐dimensional semiconductors provide intrinsic interfacial charge‐trapping dynamics as a potential source for such behaviors. However, purely defect‐driven reservoirs suffer from limited tunability and variability. Here, we demonstrate a ferroelectric‐assisted charge‐trap memory (FA‐CTM) that transforms inherently random interfacial charge dynamics into a highly controllable, designable physical reservoir. By introducing a ferroelectric underlayer and engineering voltage partitioning via capacitance matching, the FA‐CTM is intentionally driven in a volatile regime that strengthens nonlinearity while preserving reproducible fading‐memory behavior. This architecture achieves a 51% enhancement in the memory window together with a 12‐fold increase in on‐current compared to pristine dielectric counterparts. Through an industry‐academia partnership, we validated array‐level scalability using 8‐inch wafer‐scale MOCVD‐synthesized MoS 2 channels integrated via dry transfer processes. The result exhibits exceptional uniformity, with average reservoir state output‐current variation minimized to 3.11%, compared to 15.57% in non‐ferroelectric controls. Consequently, the FA‐CTM reservoir achieves 90.18% accuracy on EMNIST classification and supports a multitasking pipeline for video‐based object recognition and trajectory prediction. These results establish ferroelectric‐assisted field control as a practical method to co‐optimize tunability and uniformity in charge‐trap memories, providing a robust foundation for spatiotemporal processing.