Planar p–n Junction Engineering toward Reconfigurable Organic Synaptic Transistors for High‐Accuracy Neuromorphic Recognition

Abstract Synaptic transistors are pivotal for hardware‐level neuromorphic computing. However, the lack of switching behavior diversity has limited the implementation of advanced computing tasks, which are constrained by traditional interfacial or uncontrollable materials engineering. Here, a universal planar p–n junction structure is devised, with rational alignment of energy levels between crosslinkable p‐type poly(indacenodithiophene‐alt‐benzothiadizole)‐based conjugated polymer with hydroxyl groups at the ends of its side chains (OH‐IDTBT‐10%) and different n‐type conjugated polymers, fabricated through efficient solution processing. This structure enables reconfigurable switching of p‐type and n‐type carrier transport by modifying the transistor architecture, along with significant non‐volatile memory and synaptic plasticity. By strategically adjusting crosslinkers, a large memory window up to 48.5 V is achieved, sustained performance over 500 cycles, and a diverse array of synaptic behaviors modulated by electrical pulses. The underlying mechanism involves quantum well‐like structures and discrete physical charge traps at the bilayer interface. The versatility of the strategy is proven across different n‐type polymer systems. An artificial neural network (ANN) constructed by these devices affords a remarkably high facial recognition accuracy of 97.58% using the Yale Face Database with minimized training epochs of 200. This design provides an opportunity for high performance hardware with diverse synaptic behaviors in advanced neuromorphic computing.