P3OT-Based Organic Polymer Memristors for Artificial Synaptic Behavior and Neuromorphic Computing Applications
Hongguang Zhang, Linkai Li, Aiqian Guo, Jianda Li, Yongtao Li, Wen Li, Mingdong Yi, Liang Xie
Abstract
Organic synaptic memristors have recently attracted considerable interest due to the ease of fabrication enabled by solution processing and their potential roles in neuromorphic electronics. In this research, an organic polymer memristor based on poly(3-octylthiophene-2,5-diyl) (P3OT) was designed, and a systematic characterization of its electrical properties was experimentally demonstrated. The device successfully emulated multiple synaptic behaviors, including paired-pulse facilitation (PPF), paired-pulse depression (PPD), post-tetanic potentiation (PTP), spike-timing-dependent plasticity (STDP), and short-term plasticity (STP) to long-term plasticity (LTP) transition, as well as experience learning. Detailed analysis of the I–V characteristics indicated that resistance switching resulted from a combination of tunneling, space charge-limited conduction (SCLC), and Schottky emission mechanisms. The electrical performance of the device remained stable even after being stored in an air environment for more than 90 days. Furthermore, an artificial neural network (ANN) implemented using this device achieved a recognition accuracy of 91% on the MNIST data set. This study offers valuable theoretical insights and experimental references for advancing the use of organic polymer memristors in simulating synaptic functions and implementing artificial neural networks.