ULTRA WIDEBAND LOW NOISE AMPLIFIER WITH RESISTIVE FEEDBACK AND SHUNT INDUCTIVE PEAKING

Author(s) : Athira K.B, Prameela B

Volume & Issue : VOLUME 2 / 2017 , ISSUE 1

Page(s) : 40-46


Abstract

Ultra Wideband (UWB) technology is capable of transmitting data over 3.1-10.6GHz frequency band with very low power consumption and high data processing rates. Low noise amplifier (LNA) is typically the first stage of a receiver whose performance greatly affects the overall receiver performance. This work presents the design of UWB LNA with resistive feedback and shunt peaking techniques. A Cascode inductive degeneration LNA has been initially designed. Resistive shunt feedback LNA is generated from this basic topology. Bandwidth extension technique using shunt inductive peaking has been employed on the resistive shunt feedback LNA. The designs are simulated using Cadence Virtuoso Spectre using CMOS gpdk180 for 3.1-5GHz lower UWB applications. The resistive shunt feedback LNA shows a maximum voltage gain of 23.3 at 4GHz and a minimum NF of 1.67 at 4.2GHz using 1.8 V supply. The resistive feedback LNA which employs shunt inductive peaking technique has an extended 3-dB bandwidth of 4.21GHz compared to the basic resistive shunt feedback LNA that achieves 3.5GHz bandwidth.



Keywords

Bandwidth extension, inductive degeneration, Low noise amplifier (LNA), resistive shunt feedback, Shunt inductive peaking, Ultra wideband technology(UWB).

References

[1]R. Roy, and T. Kailath, “ESPRIT _ estimation of signal parameters via rotational invariance techni-ques,” IEEE Trans. Acoust. Speech Signal Process, Vol. 37, no. 7, pp. 984_95, Jul. 1989.
[2]V. Wowk, Machinery Vibration, Measurement and Analysis. New York: McGraw-Hill, 1991.
[3]B. Li, M.-Y. Chow, Y. Tipsuwan, and J. C. Hung, “Neural network based motor rolling bearing fault diagnosis,” IEEE Trans. Ind. Electron.,Vol. 47, no. 5, pp. 1060_9, Oct. 2000.
[4]J. Altmann, and J. Mathew, “Multiple band-pass auto regressive demodulation for rolling element bearing fault diagnosis,” Mech. Syst. Signal Process., Vol. 15, no. 5, pp. 963_77, Sep. 2001.
[5]B. Samanta, and K. R. Al-Balushi, “Artificial neural network based fault diagnostics of rolling ele-ment bearings using time domain features,” Mech. Syst. Signal Process., Vol. 17, no. 2, pp. 317_28, Mar. 2003.
[6]B. S.Yang, T. Han, and J. L. An, “Art_Kohonen neural network for fault diagnosis of rotating mach-inery,” Mech. Syst. Signal Process., Vol. 18, no. 3, pp. 645_57, May 2004.
[7]L. Zhang, L. B. Jack, and A. K. Nandi, “Fault detection using genetic programming,” Mech. Syst. Signal Process., Vol. 19, no. 2, pp. 271_89, Mar. 2005.
[8]A. Saxena, and A. Saad, “Evolving an artificial neural network classifier for condition monitoring of rotating mechanical systems,” Appl. Soft Computer., Vol. 7, no. 1, pp. 441_54, Jan. 2007.

[9]B. Zhang, C. Sconyers, C. Byington, R. Patrick, M. Orchard, and G. Vachtsevanos, “A probabilistic fault detection approach: Application to bearing fault detection,” IEEE Trans. Ind. Electron., Vol. 58, no. 5,pp. 2011_8, May 2011.
[10]Y.Jianbo, “Local and nonlocal preserving projection for bearing defect classification and perfor-mance assessment,” IEEE Trans. Ind. Electron., Vol. 59, no. 5, pp. 2363_76, May 2012.
[11]M. D. Prieto, G. Cirrincione, A. G Espinosa, J. A. Ortega, and H. Henao, “Bearing fault detection by a novel condition-monitoring scheme based on statis-tical-time features and neural networks,”IEEE Trans. Ind. Electron., Vol. 60, no. 8, pp. 3398_407, Aug. 2013.