Li-Ion Doped Artificial Synaptic Memristor for Highly Linear Neuromorphic Computing

Abstract

Linear weights modulation in neuromorphic memristor plays an important role in high-accuracy image recognition task. Herein, a Li+ doped organic artificial memristor for neuromorphic computing was proposed for linear weights update, which shows active ions diffusive dynamics as Ca2+ diffusion in biological synapse. The memristor exhibits gradual resistive switching, multi-state storage and typical synaptic behaviors. In addition, the synaptic learning capability of letter “T” was demonstrated in a memristors array. By designing consecutive pulse waveforms with enhanced amplitude, the linearity of memristor for weight update in long-term potentiation and depression (LTP/LTD) could be improved from 6.8 to 0.4. Based on the great nonlinearity factor in LTP ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha _{\text {p}}={1.5}$ </tex-math></inline-formula> ) and LTD ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha _{\text {d}}={0.4}$ </tex-math></inline-formula> ), face recognition was achieved with high accuracy of 96% by artificial neural network consisting of ion doped memristors. The ion doped organic memristor with highly linear weights update provides guidelines for the development of bio-inspired ion diffusive neuromorphic computing system.