Energy Efficient Hybrid Reservoir Computing Using Hf 0.5 Zr 0.5 O 2 Ferroelectric Thin‐Film Transistors with an Integrated Optically and Electrically Synaptic Functions

Abstract This study introduces an ultralow power hybrid reservoir computing (HRC) system employing an indium gallium zinc oxide (IGZO)/Hf 0.5 Zr 0.5 O 2 (HZO)‐based ferroelectric thin‐film transistor (FeTFT) for neuromorphic applications. The proposed FeTFT system integrates volatile and nonvolatile functionalities, respectively driven by optical and electrical stimuli, to emulate short‐term and long‐term synaptic behaviors. Leveraging persistent photoconductivity in the IGZO channel under optical excitation, the FeTFT exhibits dynamic reservoir characteristics, while HZO‐induced ferroelectric polarization enables robust long‐term memory for the readout layer. Experimental results demonstrate enhanced energy efficiency with a power consumption of ≈22 pW per device and distinct separation of 4‐ and 5‐bit reservoir states. This system achieves competitive accuracies of 90.48% and 88.23% for Modified National Institute of Standards and Technology (MNIST) and fashion MNIST datasets, respectively, surpassing state‐of‐the‐art hardware‐based implementations. By consolidating reservoir and readout layers within a single device, this study advances the scalability and feasibility of next‐generation neuromorphic computing systems. Furthermore, the implementation of HRC leveraging optical and electrical pulses presents promising prospects for applications involving visual neuron functionalities.