logo
ResearchBunny Logo
VO₂ memristor-based frequency converter with in-situ synthesize and mix for wireless internet-of-things

Engineering and Technology

VO₂ memristor-based frequency converter with in-situ synthesize and mix for wireless internet-of-things

C. Liu, P. J. Tiw, et al.

Explore the future of wireless internet-of-things (WIoT) with a groundbreaking VO₂ memristor-based frequency converter developed by Chang Liu and colleagues. This innovative solution enhances power efficiency significantly while minimizing performance loss compared to traditional CMOS designs, showcasing the promising potential of VO₂ technologies in energy-efficient WIoT systems.

00:00
Playback language: English
Abstract
Wireless internet-of-things (WIoT) networks are rapidly evolving, increasing the demand for transmission efficiency. Frequency converters are crucial for efficient wireless transmission, but existing designs using CMOS devices suffer from high latency and energy consumption. This paper presents a VO₂ memristor-based frequency converter using an 8x8 VO₂ array for in-situ frequency synthesis and mixing. The converter leverages the negative differential resistance of VO₂ memristors for self-oscillation and programmability. Experimental results using acoustic, vision, and spatial sensor data demonstrate power enhancement of 1.45x-1.94x with minimal performance degradation compared to CMOS-based converters, highlighting the potential of VO₂ memristors for energy-efficient WIoT systems.
Publisher
Nature Communications
Published On
Feb 19, 2024
Authors
Chang Liu, Pek Jun Tiw, Teng Zhang, Yanghao Wang, Lei Cai, Rui Yuan, Zelun Pan, Wenshuo Yue, Yaoyu Tao, Yuchao Yang
Tags
Wireless IoT
frequency converter
VO₂ memristor
energy efficiency
self-oscillation
programmability
sensor data
Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs, just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny