Ferroelectric Charging Tunnel Junctions: Resolving Transport Trade‐Offs via Trap‐Assisted Barrier Modulation for Physical Reservoir Computing

ABSTRACT Dynamic memristors that combine a wide dynamic range, intrinsic self‐rectification, and rich temporal dynamics are key building blocks for physical reservoir computing, but state‐of‐the‐art HfO ₂ ‐based ferroelectric tunnel junctions (FTJs) suffer from thickness‐limited transport trade‐offs and poorly resolved conduction mechanisms. Here, a ferroelectric charging tunnel junction (FCTJ) based on an IGZO/HZO stack is demonstrated as a scalable two‐terminal reservoir node. Introducing a carrier‐charging IGZO interlayer into a W/HZO/W stack modulates the tunneling barrier and couples ferroelectric polarization to interface oxygen‐vacancy trapping, yielding both an I _on /I _off and a rectification ratio exceeding 10 ⁴ and satisfying over 10% read margin in N x N passive crossbar arrays with N = 2000. Temperature‐ and time‐dependent transport, XPS depth profiling, and conductance‐method analysis quantitatively link shallow, while reverse leakage is suppressed by depletion and oxygen‐reservoir effects in IGZO. These composite ferroelectric–trapping dynamics support multi‐timescale synaptic plasticity with modest variability. Leveraging experimentally calibrated device dynamics, FCTJ‐based physical reservoirs outperform a multilayer perceptron using only quasi‐static conductance states for MNIST classification and accurately process UCR gesture signals and chaotic Hénon/Lorenz trajectories with low prediction error. This work establishes IGZO/HZO FCTJs as CMOS‐compatible reservoir nodes and outlines a general design strategy for ferroelectric and oxide‐based physical reservoir computing hardware.