期刊
MATERIALS CHEMISTRY FRONTIERS
卷 6, 期 8, 页码 1098-1106出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/d2qm00151a
关键词
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资金
- National key R&D plan nano frontier'' key special project [2021YFA1200502]
- Cultivation projects of national major RD project [92164109]
- National Natural Science Foundation of China [61874158, 62004056, 62104058]
- Special project of strategic leading science and technology of the Chinese Academy of Sciences [XDB44000000-7]
- Hebei Basic Research Special Key Project [F2021201045]
- Support Program for the Top Young Talents of Hebei Province [70280011807]
- Supporting Plan for 100 Excellent Innovative Talents in Colleges and Universities of Hebei Province [SLRC2019018]
- Outstanding Young Scientific Research and Innovation team of Hebei University [605020521001]
- High-level Talent Research Startup Project of Hebei University [521000981426]
- Science and Technology Project of Hebei Education Department [QN2020178, QN2021026]
- Postgraduate's Innovation Fund Project of Hebei Province [CXZZBS2022020]
- Special support funds for national high level talents [041500120001]
This study proposes the use of carbon dots to improve the uniformity of memristor parameters, resulting in more stable and efficient devices. Furthermore, carbon dots based memristor devices are able to simulate various neural network learning rules and successfully achieve simulation of the preview and review learning method.
With the rapid development of information technology for big data, memristors have become more and more popular nanoscale devices for storing a large amount of information and neural learning. However, random formation of conductive filaments (CFs) in a memristor leads to a broad distribution of device parameters, which leads to a high error rate in the iteration process of neural network learning. In this work, carbon dots (CDs) are proposed to improve the uniformity of several different oxide memristor parameters, and obviously obtain more stable high and low resistances, lower power consumption, and fast response speed. What's more, three different spike-timing-dependent plasticity (STDP) learning rules, paired-pulse facilitation (PPF), supervised learning and interest-based learning activities are simulated by carbon dots based memristor devices (CDMDs). And the preview and review learning method simulation by the PQ4R strategy are also achieved faithfully for the first time. This work provides a new way to improve the performance of memristors and develop new neuromorphic functions, which could significantly facilitate the development of artificial nervous chip systems.
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