Dual-Modal Memory Enabled by a Single Vertical N-Type Organic Artificial Synapse for Neuromorphic Computing

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.