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Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing.
Nanoscale. 2016 Aug 07; 8(29):14015-22.N

Abstract

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 TaOx based memristors, that is, by homogenizing the filament growth/dissolution rate via the introduction of an ion diffusion limiting layer (DLL) at the TiN/TaOx 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.

Authors+Show Affiliations

Institute of Microelectronics, Peking University, Beijing 100871, China. yuchaoyang@pku.edu.cn ruhuang@pku.edu.cn.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

27143476

Citation

Wang, Zongwei, et al. "Engineering Incremental Resistive Switching in TaOx Based Memristors for Brain-inspired Computing." Nanoscale, vol. 8, no. 29, 2016, pp. 14015-22.
Wang Z, Yin M, Zhang T, et al. Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing. Nanoscale. 2016;8(29):14015-22.
Wang, Z., Yin, M., Zhang, T., Cai, Y., Wang, Y., Yang, Y., & Huang, R. (2016). Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing. Nanoscale, 8(29), 14015-22. https://doi.org/10.1039/c6nr00476h
Wang Z, et al. Engineering Incremental Resistive Switching in TaOx Based Memristors for Brain-inspired Computing. Nanoscale. 2016 Aug 7;8(29):14015-22. PubMed PMID: 27143476.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Engineering incremental resistive switching in TaOx based memristors for brain-inspired computing. AU - Wang,Zongwei, AU - Yin,Minghui, AU - Zhang,Teng, AU - Cai,Yimao, AU - Wang,Yangyuan, AU - Yang,Yuchao, AU - Huang,Ru, Y1 - 2016/05/04/ PY - 2016/5/5/entrez PY - 2016/5/5/pubmed PY - 2016/5/5/medline SP - 14015 EP - 22 JF - Nanoscale JO - Nanoscale VL - 8 IS - 29 N2 - 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 TaOx based memristors, that is, by homogenizing the filament growth/dissolution rate via the introduction of an ion diffusion limiting layer (DLL) at the TiN/TaOx 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. SN - 2040-3372 UR - https://www.unboundmedicine.com/medline/citation/27143476/Engineering_incremental_resistive_switching_in_TaOx_based_memristors_for_brain_inspired_computing_ L2 - https://doi.org/10.1039/c6nr00476h DB - PRIME DP - Unbound Medicine ER -
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