Design of a Neural Network-Based VCO With High Linearity and Wide Tuning Range

Rui Guo; Kun Qian; Jinping Wei; Tupei Chen; Yanchen Liu; Deyu Kong; J. J. Wang; Yuancong Wu; S. G. Hu; Qi Yu; Yang Liu

doi:10.1109/ACCESS.2019.2915335

IEEE Access (Jan 2019)

Design of a Neural Network-Based VCO With High Linearity and Wide Tuning Range

Rui Guo,
Kun Qian,
Jinping Wei,
Tupei Chen,
Yanchen Liu,
Deyu Kong,
J. J. Wang,
Yuancong Wu,
S. G. Hu,
Qi Yu,
Yang Liu

Affiliations

Rui Guo: ORCiD; State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
Kun Qian: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
Jinping Wei: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
Tupei Chen: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Yanchen Liu: ORCiD; State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
Deyu Kong: ORCiD; State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
J. J. Wang: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
Yuancong Wu: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
S. G. Hu: ORCiD; State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
Qi Yu: State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China
Yang Liu: ORCiD; State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China

DOI: https://doi.org/10.1109/ACCESS.2019.2915335
Journal volume & issue: Vol. 7
pp. 60120 – 60125

Abstract

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In this paper, a 2 GHz LC-VCO with neural network (Multilayer Perceptron) has been designed in a 0.13 ţm CMOS technology. With the integrated neural network, the linearity and tuning range of the LC-VCO has been substantially improved. Compared to a conventional VCO design, the proposed technique can improve the linearity by selecting optimized bias voltages obtained from the output of the neuron network. The result shows that the tuning nonlinearity of the proposed VCO is further optimized from 0.335% to 0.254%.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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