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Reconfigurable multi-band LNA

Part of the Analog Circuits and Signal Processing Series book series (ACSP)

The feasibility of a multi-band RF front-end is to a great extent influenced by the feasibility of a multi-band LNA. The difficulty in the design of a multi-band LNA comes from the fact that it has to provide different functions. The most important are input impedance matching at different frequencies, reconfigurability in order to satisfy different sets of specifications that are influenced by different standards, low noise figure, high voltage gain and sufficient linearity. Additional difficulty is mutual dependency of all these functions. Basically, these depend on the design parameters in such a way that a set of design parameters values that improves one function usually deteriorates others. Hence, a number of trade-offs have to be made in order to obtain satisfactory overall performance. Adding the requirements to achieve a high level of integration, to minimize occupied chip area and to reduce the number of external discrete components makes the LNA design evenmore difficult. These are the reasonswhy the design and implementation of a multi-band LNA are considered as very challenging tasks.

The aim of this chapter is to highlight the design and implementation of a 1.9–2.4GHz DECT/Bluetooth multi-band LNA. Since the design of a multi-band LNA is a rather complex task, the clear definition of the design goals is the first condition for successful implementation of a multi-band LNA. The first goal is to find a design procedure, which should be followed in order to obtain a LNA that achieves a minimal NF with a certain power consumption. The second is to make the trade-off between minimum NF and full integration, and the third is to find a way to realize the multi-band operation without degrading the LNA performance with respect to a single-band LNA.

Keywords

Parasitic Capacitance Noise Factor Voltage Gain Triode Region Propose Design Procedure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media B.V 2008

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