Tunable Redox Dynamics of Organic Electrochemical Transistors for High-Performance Parallel Reservoir Computing.

Organic electrochemical transistor (OECT) performance is determined by the channel material's ability to transport both ions and electrons. However, many high-mobility organic semiconductors are hydrophobic and ion-impermeable, which limits their electrochemical activity. Consequently, achieving controllable redox modulation requires complex molecular design and multistep synthesis, thereby impeding their use in diverse applications. Herein, we establish a facile semiconductor blending strategy that enables hierarchical redox control in OECTs, advancing them toward high-performance parallel reservoir computing (RC). By integrating a hydrophilic organic mixed ionic-electronic conductor (OMIEC) into a hydrophobic semiconductor, the resulting phase-separated microstructure preserves ordered molecular packing for charge transport while governing composition-dependent ion accessibility. This allows on-demand electrochemical doping kinetics to optimize transfer characteristics, overcome the electrochemical inactivity issue of pristine PBTTT-C14, and achieve high current, large on/off ratio (∼2.3 × 104) along with improved cycling stability. More importantly, blended OECTs serve as multitime scale synaptic nodes, and by leveraging these distinct relaxation dynamics, we construct a parallel reservoir via multitime scale fusion, boosting Fashion-MNIST data set accuracy by over 10% compared to a single OECT. This work pioneers a materials-centric paradigm for engineering emergent ion-electron functionalities via programmable microstructures, thereby laying the material foundation for next-generation neuromorphic hardware capable of integrated sensing, memory, and parallel computation.