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基于忆阻器的用于无线物联网的具有原位合成与混合功能的变频器。

VO memristor-based frequency converter with in-situ synthesize and mix for wireless internet-of-things.

作者信息

Liu Chang, Tiw Pek Jun, Zhang Teng, Wang Yanghao, Cai Lei, Yuan Rui, Pan Zelun, Yue Wenshuo, Tao Yaoyu, Yang Yuchao

机构信息

Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.

Center for Brain Inspired Chips, Institute for Artificial Intelligence, Frontiers Science Center for Nano-optoelectronics, Peking University, Beijing, 100871, China.

出版信息

Nat Commun. 2024 Feb 19;15(1):1523. doi: 10.1038/s41467-024-45923-7.

DOI:10.1038/s41467-024-45923-7
PMID:38374302
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10876666/
Abstract

Wireless internet-of-things (WIoT) with data acquisition sensors are evolving rapidly and the demand for transmission efficiency is growing rapidly. Frequency converter that synthesizes signals at different frequencies and mixes them with sensor datastreams is a key component for efficient wireless transmission. However, existing frequency converters employ separate synthesize and mix circuits with complex digital and analog circuits using complementary metal-oxide semiconductor (CMOS) devices, naturally incurring excessive latency and energy consumption. Here we report a highly uniform and calibratable VO memristor oscillator, based on which we build memristor-based frequency converter using 8[Formula: see text]8 VO array that can realize in-situ frequency synthesize and mix with help of compact periphery circuits. We investigate the self-oscillation based on negative differential resistance of VO memristors and the programmability with different driving currents and calibration resistances, demonstrating capabilities of such frequency converter for in-situ frequency synthesize and mix for 2 ~ 8 channels with frequencies up to 48 kHz for low frequency transmission link. When transmitting classical sensor data (acoustic, vision and spatial) in an end-to-end WIoT experimental setup, our VO-based memristive frequency converter presents up to 1.45× ~ 1.94× power enhancement with only 0.02 ~ 0.21 dB performance degradations compared with conventional CMOS-based frequency converter. This work highlights the potential in solving frequency converter's speed and energy efficiency problems in WIoT using high crystalline quality epitaxially grown VO and calibratable VO-based oscillator array, revealing a promising direction for next-generation WIoT system design.

摘要

配备数据采集传感器的无线物联网(WIoT)正在迅速发展,对传输效率的需求也在快速增长。能够合成不同频率信号并将其与传感器数据流混合的变频器是高效无线传输的关键组件。然而,现有的变频器采用单独的合成和混合电路,使用互补金属氧化物半导体(CMOS)器件构建复杂的数字和模拟电路,这自然会导致过多的延迟和能耗。在此,我们报告了一种高度均匀且可校准的氧化钒(VO)忆阻器振荡器,并在此基础上使用8×8 VO阵列构建了基于忆阻器的变频器,该变频器借助紧凑的外围电路即可实现原位频率合成和混合。我们研究了基于VO忆阻器负微分电阻的自振荡以及不同驱动电流和校准电阻下的可编程性,证明了这种变频器能够对2至8个通道进行原位频率合成和混合,低频传输链路的频率高达48kHz。在端到端的WIoT实验装置中传输经典传感器数据(声学、视觉和空间数据)时,与传统的基于CMOS的变频器相比,我们基于VO的忆阻变频器功率提升了1.45倍至1.94倍,而性能仅下降了0.02至0.21dB。这项工作突出了利用高质量外延生长的VO和基于可校准VO的振荡器阵列解决WIoT中变频器速度和能源效率问题的潜力,为下一代WIoT系统设计揭示了一个有前景的方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bca4/10876666/39db6338a2d6/41467_2024_45923_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bca4/10876666/bb26b175845c/41467_2024_45923_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bca4/10876666/901c8b46e477/41467_2024_45923_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bca4/10876666/f6c42324c22e/41467_2024_45923_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bca4/10876666/88fcf7721f18/41467_2024_45923_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bca4/10876666/39db6338a2d6/41467_2024_45923_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bca4/10876666/bb26b175845c/41467_2024_45923_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bca4/10876666/901c8b46e477/41467_2024_45923_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bca4/10876666/f6c42324c22e/41467_2024_45923_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bca4/10876666/88fcf7721f18/41467_2024_45923_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bca4/10876666/39db6338a2d6/41467_2024_45923_Fig5_HTML.jpg

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