4.8 Article

Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor

期刊

ADVANCED FUNCTIONAL MATERIALS
卷 22, 期 13, 页码 2759-2765

出版社

WILEY-V C H VERLAG GMBH
DOI: 10.1002/adfm.201103148

关键词

memristors; synaptic devices; learning and memory functions; amorphous InGaZnO; oxygen ion migration; diffusion

资金

  1. National Basic Research Program of China (973 Program) [2012CB933703]
  2. National Natural Science Foundation of China [51172041, 60907016]
  3. Program for New Century Excellent Talents in University [NCET-11-0615]
  4. Jilin Province [20121802, 20100339, 20110105]
  5. Fundamental Research Funds for the Central Universities [10SSXT127, 10JCXK002, 10QNJJ005]

向作者/读者索取更多资源

A single synaptic device with inherent learning and memory functions is demonstrated based on an amorphous InGaZnO (a-IGZO) memristor; several essential synaptic functions are simultaneously achieved in such a single device, including nonlinear transmission characteristics, spike-rate-dependent and spike-timing-dependent plasticity, long-term/short-term plasticity (LSP and STP) and learning-experience behavior. These characteristics bear striking resemblances to certain learning and memory functions of biological systems. Especially, a learning-experience function is obtained for the first time, which is thought to be related to the metastable local structures in a-IGZO. These functions are interrelated: frequent stimulation can cause an enhancement of LTP, both spike-rate-dependent and spike-timing-dependent plasticity is the same on this point; and, the STP-to-LTP transition can occur through repeated stimulation training. The physical mechanism of device operation, which does not strictly follow the memristor model, is attributed to oxygen ion migration/diffusion. A correlation between short-term memory and ion diffusion is established by studying the temperature dependence of the relaxation processes of STP and ion diffusion. The realization of important synaptic functions and the establishment of a dynamic model would promote more accurate modeling of the synapse for artificial neural network.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据