Dual-Modal Memory Enabled by a Single Vertical N-Type Organic Artificial Synapse for Neuromorphic Computing
Zhichao Xie, Chenyu Zhuge, Chunyang Li, Yanfei Zhao, Jiandong Jiang, Jianhong Zhou, Yujun Fu, Yingtao Li, Zhuang Xie, Qi Wang, Lu Lin, Yazhou Wang, Wan Yue, Deyan He
Abstract
Complementary neural network circuits combining multifunctional high-performance p-type with n-type organic artificial synapses satisfy sophisticated applications such as image cognition and prosthesis control. However, implementing the dual-modal memory features that are both volatile and nonvolatile in a synaptic transistor is challenging. Herein, for the first time, we propose a single vertical n-type organic synaptic transistor (VNOST) with a novel polymeric organic mixed ionic-electronic conductor as the core channel material to achieve dual-modal synaptic learning/memory behaviors at different operating current densities via the formation of an electric double layer and the reversible ion doping. As a volatile synaptic device, the resulting VNOST demonstrated an unprecedented operating current density of MA cm –2 . Meanwhile, it is capable of 150 analog states, symmetric conductance modulation, and good state retention (100 s) for a nonvolatile synapse. Importantly, the artificial neural networks (ANNs) for recognition accuracy of the handwritten digital data sets recognition rate up to 94% based on its nonvolatile feature. This study provides a promising platform for building organic neuromorphic network circuits in complex application scenarios where high-performing n-type organic synapse transistors with dual-mode memory characters are necessitated.