Novel Tunable Active Inductor Architectures for RF and Microwave Applications

Authors

Sehmi Saad
RF2S Spectrum Solutions, R&D department, 18 Street of the Faïencerie, 33300 Bordeaux, France.
Aymen Ben Hammadi
Dynamics RF/mmW consulting and design, RF and telecommunication department, 39 Street of Vercors, 38600, Grenoble, France
Hatem Garrab
Electronics and Micro-Electronic Laboratory (LEµE), Bd de l’environnement, Monastir 5000, Tunisia. Higher Institute of Applied Sciences and Technology of Sousse, University of Sousse, Street Taher Ben Achour, 4003 Sousse, Tunisia

Synopsis

In this chapter, the design, implementation, and performance evaluation of a new TAI architecture in both the grounded and floating configurations are presented. The active-inductor circuit comes in a modified gyrator–C model enhanced by including a passive feedback resistor and a tunable capacitive network. This allows independent tuning of the value of inductance and the quality factor. The floating topology extends these concepts by using cross-coupled transistors that provide negative resistance compensation to further enhance the Q-factor and extend the tuning capabilities of the inductor. The proposed designs are implemented and tested in two different standard CMOS processes (90 nm and 130 nm), where the Cadence® SpectreRF is used for simulation. A wide inductance tuning range, high quality factors (of up to 886 and 388 for the single-ended and balanced active inductors, respectively), excellent noise behavior, and intrinsic robustness against process variations can be achieved. In this context, these structures may be quite beneficial to be integrated in RF and microwave systems, as building blocks for tunable filters, oscillators, and low-noise amplifiers.

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Published

2 December 2025

How to Cite

Saad, S. ., Hammadi, A. B. ., & Garrab, H. . (2025). Novel Tunable Active Inductor Architectures for RF and Microwave Applications. In H. . Garrab (Ed.), Tunable Active Inductors for Next-Generation Wireless and Biomedical Systems (pp. 66-84). Deep Science Publishing. https://doi.org/10.70593/978-93-7185-269-2_4