Dynamically reconfigurable artificial synapse transistors with organic heterojunctions for multifunctional neuromorphic applications

Abstract

Artificial synapses capable of neuromorphic computation are crucial for improving the processing efficiency of existing information technologies. Most of the current research on artificial synapses only simulates static synapse plasticity, and implementing bipolar artificial synapses with two modes of operation and dynamic reconfigurability remains challenging. In order to efficiently and flexibly simulate the complex behaviour of the human brain, an electret-based organic heterojunction synaptic transistor (EOHST) is designed in this paper. The transistor consists of pentacene and PTCDI-C₁₃ organic semiconductor films as the bipolar channel layer, with a polymer PVN as an electret functional layer. Owing to the combination of bipolar behavior and the charge trapping effect, the transistor can respond to identical gate pulses in different modes (excitatory and inhibitory), demonstrating a dynamically reconfigurable operational state that imitates different synaptic behaviors (potentiation and depression) of the biological nervous system in various modes. Using the Modified National Institute of Standards and Technology (MNIST) dataset images of handwritten digits, the EOHST-based neuromorphic system achieved recognition accuracies of 87.5% and 80% in its various modes. Additionally, by examining learning accuracies in different EOHST modes, the system simulated visual learning and memory processes across various emotional states. Finally, a digital logic processing system is designed to dynamically simulate AND/OR and NAND/NOR logic circuits at the same input voltage through excitatory and inhibitory modes. This dynamically reconfigurable bipolar transistor represents a significant advancement in the future development of neuromorphic computing.