模拟突触器件中ge注入a- si衬底的Cu/SiOx记忆器件的多丝开关

IF 8.6 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY Npg Asia Materials Pub Date : 2023-09-15 DOI:10.1038/s41427-023-00495-8
Keonhee Kim, Jae Gwang Lim, Su Man Hu, Yeonjoo Jeong, Jaewook Kim, Suyoun Lee, Joon Young Kwak, Jongkil Park, Gyu Weon Hwang, Kyeong-Seok Lee, Seongsik Park, Wook-Seong Lee, Byeong-Kwon Ju, Jong Keuk Park, Inho Kim
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引用次数: 0

摘要

在神经形态计算系统中,已经提出了各种各样的记忆装置作为人工突触。在训练过程中具有线性电导更新的模拟突触装置是训练神经网络的有效基础。虽然已经提出了许多不同的模拟忆阻器,但需要一种更可靠的方法来实现模拟突触器件。在这项研究中,我们提出了一种Cu/ siox /植入a- sigex / p++ + c-Si结构的忆阻器,其中包含一个a- si层,通过注入Ge来适当控制电导。a- sige x层通过限制电流过冲、限制操作过程中产生的热量和防止活性金属(Cu)与Si底电极之间的硅化物形成反应,在器件运行中发挥多功能作用。因此,a-SiGe x接口层能够形成多弱细丝并诱导模拟开关行为。TEM观察表明,在SiO x和c-Si之间插入a-SiGe x层显著抑制了硅化铜的形成,确保了可靠的set/reset操作。通过分析电流-电压特性和电子显微镜图像,讨论了模拟开关行为的来源。最后,忆阻-神经网络仿真表明,我们开发的忆阻装置具有很高的学习精度,在未来的神经形态计算硬件中有很大的应用前景。
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Multifilamentary switching of Cu/SiOx memristive devices with a Ge-implanted a-Si underlayer for analog synaptic devices
Abstract Various memristive devices have been proposed for use in neuromorphic computing systems as artificial synapses. Analog synaptic devices with linear conductance updates during training are efficiently essential to train neural networks. Although many different analog memristors have been proposed, a more reliable approach to implement analog synaptic devices is needed. In this study, we propose the memristor of a Cu/SiO x /implanted a-SiGe x /p ++ c-Si structure containing an a-Si layer with properly controlled conductance through Ge implantation. The a-SiGe x layer plays a multifunctional role in device operation by limiting the current overshoot, confining the heat generated during operation and preventing the silicide formation reaction between the active metal (Cu) and the Si bottom electrode. Thus, the a-SiGe x interface layer enables the formation of multi-weak filaments and induces analog switching behaviors. The TEM observation shows that the insertion of the a-SiGe x layer between SiO x and c-Si remarkably suppresses the formation of copper silicide, and reliable set/reset operations are secured. The origin of the analog switching behaviors is discussed by analyzing current-voltage characteristics and electron microscopy images. Finally, the memristive-neural network simulations show that our developed memristive devices provide high learning accuracy and are promising in future neuromorphic computing hardware.
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来源期刊
Npg Asia Materials
Npg Asia Materials MATERIALS SCIENCE, MULTIDISCIPLINARY-
CiteScore
15.40
自引率
1.00%
发文量
87
审稿时长
2 months
期刊介绍: NPG Asia Materials is an open access, international journal that publishes peer-reviewed review and primary research articles in the field of materials sciences. The journal has a global outlook and reach, with a base in the Asia-Pacific region to reflect the significant and growing output of materials research from this area. The target audience for NPG Asia Materials is scientists and researchers involved in materials research, covering a wide range of disciplines including physical and chemical sciences, biotechnology, and nanotechnology. The journal particularly welcomes high-quality articles from rapidly advancing areas that bridge the gap between materials science and engineering, as well as the classical disciplines of physics, chemistry, and biology. NPG Asia Materials is abstracted/indexed in Journal Citation Reports/Science Edition Web of Knowledge, Google Scholar, Chemical Abstract Services, Scopus, Ulrichsweb (ProQuest), and Scirus.
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