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Nanocrystal Materials for Resistive Memory and Artificial Synapses:Progress and Prospects

电阻随机存取存储器 材料科学 神经形态工程学 记忆电阻器 重置(财务) 电压 兴奋剂 纳米晶 纳米技术 计算机科学 非易失性存储器 CMOS芯片 光电子学 人工神经网络 电子工程 电气工程 人工智能 工程类 金融经济学 经济
作者
Ying‐Chun Chen,Dunkui Chen,Chi Zhang,Xian Zhang
出处
期刊:Recent Patents on Nanotechnology [Bentham Science Publishers]
卷期号:18 (2): 237-255 被引量:1
标识
DOI:10.2174/1872210517666230413092108
摘要

Background: Resistive random-access memory (RRAM) is considered to be the most promising next-generation non-volatile memory because of its low cost, low energy consumption, and excellent data storage characteristics. However, the on/off (SET/RESET) voltages of RRAM are too random to replace the traditional memory. Nanocrystals (NCs) offer an appealing option for these applications since they combine excellent electronic/optical properties and structural stability and can address the requirements of low-cost, large-area, and solution-processed technologies. Therefore, the doping NCs in the function layer of RRAM are proposed to localize the electric field and guide conductance filaments (CFs) growth. Objective: The purpose of this article is to focus on a comprehensive and systematical survey of the NC materials, which are used to improve the performance of resistive memory (RM) and optoelectronic synaptic devices and review recent experimental advances in NC-based neuromorphic devices from artificial synapses to light-sensory synaptic platforms. Methods: Extensive information related to NCs for RRAM and artificial synapses and their associated patents were collected. This review aimed to highlight the unique electrical and optical features of metal and semiconductor NCs for designing future RRAM and artificial synapses. Results: It was demonstrated that doping NCs in the function layer of RRAM could not only improve the homogeneity of SET/RESET voltage but also reduce the threshold voltage. At the same time, it could still increase the retention time and provide the probability of mimicking the bio-synapse. Conclusion: NC doping can significantly enhance the overall performance of RM devices, but there are still many problems to be solved. This review highlights the relevance of NCs for RM and artificial synapses and also provides a perspective on the opportunities, challenges, and potential future directions.
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