Design of a neural network-based VCO with high linearity and wide tuning range
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 ca...
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sg-ntu-dr.10356-1064522019-12-06T22:12:07Z Design of a neural network-based VCO with high linearity and wide tuning range Guo, Rui Qian, Kun Wei, Jinping Chen, Tupei Liu, Yanchen Kong, Deyu Wang, J. J. Wu, Yuancong Hu, S. G. Yu, Qi Liu, Yang School of Electrical and Electronic Engineering LC-VCO Neural Network DRNTU::Engineering::Electrical and electronic engineering 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 version 2019-06-24T08:41:31Z 2019-12-06T22:12:07Z 2019-06-24T08:41:31Z 2019-12-06T22:12:07Z 2019 Journal Article 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 https://hdl.handle.net/10356/106452 http://hdl.handle.net/10220/48929 http://dx.doi.org/10.1109/ACCESS.2019.2915335 en IEEE Access © 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. 6 p. application/pdf |
| institution |
Nanyang Technological University |
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NTU Library |
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Singapore |
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| language |
English |
| topic |
LC-VCO Neural Network DRNTU::Engineering::Electrical and electronic engineering |
| spellingShingle |
LC-VCO Neural Network DRNTU::Engineering::Electrical and electronic engineering Guo, Rui Qian, Kun Wei, Jinping Chen, Tupei Liu, Yanchen Kong, Deyu Wang, J. J. Wu, Yuancong Hu, S. G. Yu, Qi Liu, Yang Design of a neural network-based VCO with high linearity and wide tuning range |
| description |
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%. |
| author2 |
School of Electrical and Electronic Engineering |
| author_facet |
School of Electrical and Electronic Engineering Guo, Rui Qian, Kun Wei, Jinping Chen, Tupei Liu, Yanchen Kong, Deyu Wang, J. J. Wu, Yuancong Hu, S. G. Yu, Qi Liu, Yang |
| format |
Article |
| author |
Guo, Rui Qian, Kun Wei, Jinping Chen, Tupei Liu, Yanchen Kong, Deyu Wang, J. J. Wu, Yuancong Hu, S. G. Yu, Qi Liu, Yang |
| author_sort |
Guo, Rui |
| title |
Design of a neural network-based VCO with high linearity and wide tuning range |
| title_short |
Design of a neural network-based VCO with high linearity and wide tuning range |
| title_full |
Design of a neural network-based VCO with high linearity and wide tuning range |
| title_fullStr |
Design of a neural network-based VCO with high linearity and wide tuning range |
| title_full_unstemmed |
Design of a neural network-based VCO with high linearity and wide tuning range |
| title_sort |
design of a neural network-based vco with high linearity and wide tuning range |
| publishDate |
2019 |
| url |
https://hdl.handle.net/10356/106452 http://hdl.handle.net/10220/48929 http://dx.doi.org/10.1109/ACCESS.2019.2915335 |
| _version_ |
1681034418955747328 |