An ion-modulated organic electrochemical synaptic transistor for efficient parallel computing and in-situ training

Parallel computing architectures are urgently needed to speed up the training process of artificial neural networks. This study proposes a novel approach to parallel computing using ion-modulated organic electrochemical transistors (OECTs). Thanks to electrochemical doping and de-doping mechanism, the OECTs demonstrate long-term plasticity and exhibit distinguishable conductive states with high linearity. Moreover, our device array enables efficient weighted sum and convolution operations for image feature extraction and performs effectively in simulating hardware-based Faster R-CNN for object detection via transfer learning. The OECTs array, with its separate read and write features and controllable conductive states, achieves the integration of forward inference and backward training, resulting in successful in-situ training of convolutional neural networks (CNNs). The CNNs based on OECTs achieve accuracies of 96.49 % and 82.57 % on the MNIST and Fashion-MNIST datasets, respectively, showcasing the potential of OECTs in edge computing for enhanced resource utilization and time efficiency.