Energy-Efficient Organic Ferroelectric Tunnel Junction Memristors for Neuromorphic Computing

Abstract

Energy efficiency, parallel information processing, and unsupervised learning make the human brain a model computing system for unstructured data handling. Different types of oxide memristors can emulate synaptic functions in artificial neuromorphic circuits. However, their cycle-to-cycle variability or strict epitaxy requirements remain a challenge for applications in large-scale neural networks. Here, solution-processable ferroelectric tunnel junctions (FTJs) with P(VDF-TrFE) copolymer barriers are reported showing analog memristive behavior with a broad range of accessible conductance states and low energy dissipation of 100 fJ for the onset of depression and 1 pJ for the onset of potentiation by resetting small tunneling currents on nanosecond timescales. Key synaptic functions like programmable synaptic weight, long- and short-term potentiation and depression, paired-pulse facilitation and depression, and Hebbian and anti-Hebbian learning through spike shape and timing-dependent plasticity are demonstrated. In combination with good switching endurance and reproducibility, these results offer a promising outlook on the use of organic FTJ memristors as building blocks in artificial neural networks.