Flexible electrolyte-gated oxide transistors for synaptic memory and neuromorphic computing

Abstract

Flexible neuromorphic hardware integrating learning, memory, and reliable information processing is crucial for next-generation wearable electronics and artificial intelligent 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 memory efficiency via a gate-voltage-controlled phenomenological weighting model, were also emulated. In addition, a convolutional neural network trained using EGOT characteristics achieves high classification accuracy on the Fashion-MNIST dataset for both raw and noise-perturbed inputs. These results highlight the promise of flexible devices for neuromorphic computing, wearable intelligence, and bioinspired artificial intelligence hardware.