Flexible Electrolyte-Gated Oxide Transistors for Synaptic Memory and Neuromorphic Computing

Muhammad Sadiq, Ayesha Touqeer, Muhammad Zahid, Jia Sun, Zhenhao Chen

Flexible neuromorphic hardware integrating learning, memory, and reliable information processing is crucial for next-generation wearable electronics and AI systems. Here, we developed flexible electrolyte-gated oxide transistors (EGOTs) for neuromorphic computing and memory applications. The optimized devices demonstrate key synaptic functionalities under electrical stimulation. The cognitive processes, such as repetitive learning, forgetting, and emotion-modulated memory efficiency via a gate-voltage-controlled emotional weighting model, were also emulated. In addition, a convolutional neural network (CNN) trained using EGOT characteristics achieves high classification accuracy on the Fashion-MNIST data set for both raw and noise-perturbed inputs. These results highlight the promise of flexible devices for neuromorphic computing, wearable intelligence, and bioinspired AI hardware.