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

Abstract

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.