Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106452
Title: Design of a neural network-based VCO with high linearity and wide tuning range
Authors: Guo, Rui
Qian, Kun
Wei, Jinping
Chen, Tupei
Liu, Yanchen
Kong, Deyu
Wang, J. J.
Wu, Yuancong
Hu, S. G.
Yu, Qi
Liu, Yang
Keywords: LC-VCO
Neural Network
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2019
Source: Guo, R., Qian, K., Wei, J., Chen, T., Liu, Y., Kong, D., . . . Liu, Y. (2019). Design of a neural network-based VCO with high linearity and wide tuning range. IEEE Access, 7, 60120-60125. doi:10.1109/ACCESS.2019.2915335
Series/Report no.: IEEE Access
Abstract: 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%.
URI: https://hdl.handle.net/10356/106452
http://hdl.handle.net/10220/48929
DOI: 10.1109/ACCESS.2019.2915335
Rights: © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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