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## On the usage of machine-learning techniques for the accurate modeling of integrated inductors for RF applications

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This chapter describes an inductor modeling strategy based on machine-learning techniques. The model developed is based on Kriging functions and uses a novel modeling technique based on a two-step strategy, which is able to obtain an extremely accurate model with less than 1% error when compared to electromagnetic (EM) simulations. Due to its extreme accuracy and efficiency, the model can be used in inductor synthesis processes using single- or multi-objective optimization algorithms in order to obtain a single design or a Pareto-optimal front. Also, the model can describe the inductor behavior in frequency and therefore can also be used in circuit design using modern electrical simulators. This chapter discusses both applications (inductor synthesis and circuit design), performing several singleand multi-objective inductor optimizations, using different inductor topologies and operating frequencies. Furthermore, the model is also used in order to accurately model inductors during the design of a voltage-controlled oscillator (VCO) and a low-noise amplifier (LNA).

Chapter Contents:

• 7.1 Introduction
• 7.3 Surrogate modeling
• 7.3.1 Modeling strategy
• 7.4 Modeling application to RF design
• 7.4.1 Inductor optimization
• 7.4.2 Circuit design
• 7.4.2.1 Voltage-controlled oscillator
• 7.4.2.2 Low-noise amplifier
• 7.5 Conclusions
• Acknowledgment
• References

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