Engineering incremental resistive switching in TaO based memristors for brain-inspired computing

• Published in: Nanoscale 2016-07, Vol.8 (29), p.1415-1422
• Main Authors: Wang, Zongwei, Yin, Minghui, Zhang, Teng, Cai, Yimao, Wang, Yangyuan, Yang, Yuchao, Huang, Ru
• Format: Article
• ISSN: 2040-3364; 2040-3372
• DOI: 10.1039/c6nr00476h

Brain-inspired neuromorphic computing is expected to revolutionize the architecture of conventional digital computers and lead to a new generation of powerful computing paradigms, where memristors with analog resistive switching are considered to be potential solutions for synapses. Here we propose and demonstrate a novel approach to engineering the analog switching linearity in TaO x based memristors, that is, by homogenizing the filament growth/dissolution rate via the introduction of an ion diffusion limiting layer (DLL) at the TiN/TaO x interface. This has effectively mitigated the commonly observed two-regime conductance modulation behavior and led to more uniform filament growth (dissolution) dynamics with time, therefore significantly improving the conductance modulation linearity that is desirable in neuromorphic systems. In addition, the introduction of the DLL also served to reduce the power consumption of the memristor, and important synaptic learning rules in biological brains such as spike timing dependent plasticity were successfully implemented using these optimized devices. This study could provide general implications for continued optimizations of memristor performance for neuromorphic applications, by carefully tuning the dynamics involved in filament growth and dissolution. The introduction of a nanoscale diffusion limiting layer (DLL) into tantalum oxide based memristors leads to improved analog resistive switching.