Reconfigurable Physical Reservoir Enabled by Polarization of Ferroelectric Polymer P(VDF–TrFE) and Interface Charge-Trapping/Detrapping in Dual-Gate IGZO Transistor

Neuromorphic computers promise to enhance computing efficiency by eliminating conventional von Neumann architecture bottlenecks. Bio-inspired artificial neural networks, such as feedforward neural networks and reservoir computing (RC), face challenges due to the unique memristor requirements. In this study, a dual-gate ferroelectric polymer P(VDF–TrFE)-coupled thin film transistor (DG–TFT) with an IGZO channel is presented. It yields complementary short- and long-term memory functionalities are derived from the charge-trapping/detrapping process at the IGZO-SiO₂ dielectric interface and ferroelectric polarization. These memory functionalities can be switched using different gated modes to meet the requirements of the reservoir and readout layers in RC. The bottom-gated mode (BG-mode) exhibits short-term memory effects and nonlinear dynamics, whereas the top-gated mode (TG-mode) displays improved long-term memory characteristics. To evaluate the long-term memory properties, Python is used for pattern recognition. For the nonlinear dynamics and short-term memory response of the BG-mode, the DG–TFT is employed as a reservoir layer to handle various temporal tasks. Notably, the polarization level of the ferroelectric layer is coupled to improve the richness of the reservoir states, providing a reconfigurable RC system with an expanded capacity to effectively process and accommodate diverse signals. This holds potential for next-generation hybrid intelligent applications.