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基于氧化石墨烯的忆阻式内存逻辑电路实现常关计算。

Graphene Oxide-Based Memristive Logic-in-Memory Circuit Enabling Normally-Off Computing.

作者信息

Kim Yeongkwon, Jeon Seung-Bae, Jang Byung Chul

机构信息

School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Republic of Korea.

Department of Electronic Engineering, Hanbat National University, 125 Dongseo-daero, Yuseong-gu, Daejeon 34158, Republic of Korea.

出版信息

Nanomaterials (Basel). 2023 Feb 13;13(4):710. doi: 10.3390/nano13040710.

DOI:10.3390/nano13040710
PMID:36839078
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9963271/
Abstract

Memristive logic-in-memory circuits can provide energy- and cost-efficient computing, which is essential for artificial intelligence-based applications in the coming Internet-of-things era. Although memristive logic-in-memory circuits have been previously reported, the logic architecture requiring additional components and the non-uniform switching of memristor have restricted demonstrations to simple gates. Using a nanoscale graphene oxide (GO) nanosheets-based memristor, we demonstrate the feasibility of a non-volatile logic-in-memory circuit that enables normally-off in-memory computing. The memristor based on GO film with an abundance of unusual functional groups exhibited unipolar resistive switching behavior with reliable endurance and retention characteristics, making it suitable for logic-in-memory circuit application. In a state of low resistance, temperature-dependent resistance and I-V characteristics indicated the presence of a metallic Ni filament. Using memristor-aided logic (MAGIC) architecture, we performed NOT and NOR gates experimentally. Additionally, other logic gates such as AND, NAND, and OR were successfully implemented by combining NOT and NOR universal logic gates in a crossbar array. These findings will pave the way for the development of next-generation computer systems beyond the von Neumann architecture, as well as carbon-based nanoelectronics in the future.

摘要

忆阻存内逻辑电路能够提供高能效且低成本的计算,这对于即将到来的物联网时代基于人工智能的应用至关重要。尽管此前已有关于忆阻存内逻辑电路的报道,但需要额外组件的逻辑架构以及忆阻器的非均匀开关特性限制了其仅能实现简单门电路的演示。通过使用基于纳米级氧化石墨烯(GO)纳米片的忆阻器,我们证明了一种实现常关型存内计算的非易失性存内逻辑电路的可行性。基于具有大量异常官能团的GO薄膜的忆阻器表现出单极电阻开关行为,具有可靠的耐久性和保持特性,使其适用于存内逻辑电路应用。在低电阻状态下,温度相关电阻和I-V特性表明存在金属镍细丝。利用忆阻器辅助逻辑(MAGIC)架构,我们通过实验实现了非门和或非门。此外,通过在交叉阵列中组合非门和或非门这两种通用逻辑门,成功实现了与门、与非门和或门等其他逻辑门。这些发现将为超越冯·诺依曼架构的下一代计算机系统以及未来的碳基纳米电子学的发展铺平道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb8/9963271/5e036401c3cb/nanomaterials-13-00710-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb8/9963271/f294ff6f0844/nanomaterials-13-00710-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb8/9963271/4e0e479f63af/nanomaterials-13-00710-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb8/9963271/9f8095f21c5b/nanomaterials-13-00710-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb8/9963271/5e036401c3cb/nanomaterials-13-00710-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb8/9963271/f294ff6f0844/nanomaterials-13-00710-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb8/9963271/4e0e479f63af/nanomaterials-13-00710-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb8/9963271/9f8095f21c5b/nanomaterials-13-00710-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7eb8/9963271/5e036401c3cb/nanomaterials-13-00710-g004.jpg

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BiOSe-Based Memristor-Aided Logic.基于生物硒的忆阻器辅助逻辑
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Memristors Based on 2D Materials as an Artificial Synapse for Neuromorphic Electronics.基于二维材料的忆阻器作为神经形态电子学的人工突触。
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Black Phosphorus Based Multicolor Light-Modulated Transparent Memristor with Enhanced Resistive Switching Performance.具有增强电阻开关性能的基于黑磷的多色光调制透明忆阻器
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