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

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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Veröffentlicht in:IEEE access 2019, Vol.7, p.60120-60125
Hauptverfasser: 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: Artikel
Sprache:eng
Schlagworte:
Biological neural networks
CMOS
Error analysis
LC-VCO
Linearity
MLP
Multilayer perceptrons
neural network
Neural networks
Training
Tuning
varactor tank
Varactors
Voltage-controlled oscillators
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Beschreibung
Zusammenfassung: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%.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2915335