Small Molecule‐based Memristive Framework with Hybrid Plasticity for Flexible Reservoir Computing Integration

Abstract The rapidly increasing demand for data processing at user interfaces underscores the critical need for efficient time‐series data handling in wearable and portable electronics. Reservoir computing (RC), which emulates biological computation, holds significant promise for processing temporal information. It comprises physical reservoirs and readout layers that transform time‐series signals and enable parallel computation, respectively. However, full integration of hardware RC systems on a single flexible substrate remains challenging due to the distinct functional requirements of reservoir and readout nodes. Here, a small‐molecule‐based memristive framework is presented tailored for flexible RC systems. Flexible memristor arrays incorporating a multifunctional interfacial layer that enables tunable grain distributions in the switching layer exhibit biologically inspired short‐ and long‐term plasticity, key for implementing physical reservoirs and readout networks, in a grain‐size‐dependent manner. In addition, the memristor arrays exhibit high reliability and uniform performance, with low device‐to‐device and cycle‐to‐cycle variations (≈7.87% and ≈5.19%, respectively). RC systems based on this framework exhibit efficient data compression and robust adaptability to temporal variations, such as rotational transformations, in handwritten digit recognition. These small‐molecule memristive platforms provide a promising hardware foundation for intelligent, flexible, and energy‐efficient wearable electronics.