Orthogonally regulated multi-timescale reservoir and linear nonvolatile readout in a vertical transistor for dynamic machine vision

Abstract Implementing analog reservoir computing (RC) at the edge is fundamentally constrained by conflicting hardware requirements for the reservoir (volatile, nonlinear) and readout (nonvolatile, linear) layers. Here, we resolve this dilemma by monolithically integrating a multi-timescale photoelectric reservoir and a linear ferroelectric readout within a single vertical organic field-effect transistor (Fe-vOFET). This architecture functionally decouples photocarrier dynamics from ferroelectric polarization, allowing orthogonal optimization of reservoir dynamics and synaptic weights. In the optical mode, the Rubrene/F 16 CuPc heterojunction underpins reservoir dynamics, exploiting wavelength-dependent relaxation kinetics to construct multi-timescale grouped reservoirs that project inputs into high-dimensional spatiotemporal states. Conversely, in the electrical mode, the metal–ferroelectric–metal–insulator–semiconductor (MFMIS) stack governs readout functionality by mitigating interface trapping, translating remnant polarization into linear, symmetric, nonvolatile weight updates (endurance >10 6 , retention >3000 s, asymmetry nonlinearity 0.31 ± 0.05). The vertical-channel geometry is pivotal, simultaneously amplifying optical feedback and boosting the on/off ratio to ~10 7 , which is essential for high-fidelity weight mapping. Leveraging this dual-mode operation, we demonstrate a compact RC system achieving >90% accuracy in dynamic human-action recognition. This work establishes a scalable device paradigm that unifies dynamical richness with programming precision, paving the way for energy-efficient, all-in-one dynamic machine vision systems.