1 research outputs found
Radio-Frequency Inductor Synthesis Using Evolutionary Computation and Gaussian-Process Surrogate Modeling
In recent years, the application of evolutionary computation techniques to electronic circuit design problems, ranging from digital to
analog and radiofrequency circuits, has received increasing attention. The level of maturity runs inversely to the complexity of the
design task, less complex in digital circuits, higher in analog ones and still higher in radiofrequency circuits. Radiofrequency inductors
are key culprits of such complexity. Their key performance parameters are inductance and quality factors, both a function of the
frequency. The inductor optimization requires knowledge of such parameters at a few representative frequencies. Most common
approaches for optimization-based radiofrequency circuit design use analytical models for the inductors. Although a lot of effort has
been devoted to improve the accuracy of such analytical models, errors in inductance and quality factor in the range of 5% to 25% are
usual and it may go as high as 200% for some device sizes. When the analytical models are used in optimization-based circuit design
approaches, these errors lead to suboptimal results, or, worse, to a disastrous non-fulfilment of specifications. Expert inductor
designers rely on iterative evaluations with electromagnetic simulators, which, properly configured, are able to yield a highly accurate
performance evaluation. Unfortunately, electromagnetic simulations typically take from some tens of seconds to a few hours,
hampering their coupling to evolutionary computation algorithms. Therefore, analytical models and electromagnetic simulation
represent extreme cases of the accuracy-efficiency trade-off in performance evaluation of radiofrequency inductors. Surrogate
modeling strategies arise as promising candidates to improve such trade-off. However, obtaining the necessary accuracy is not that
easy as inductance and quality factor at some representative frequencies must be obtained and both performances change abruptly
around the self-resonance frequency, which is particular to each device and may be located above or below the frequencies of interest.
Both, offline and online training methods will be considered in this work and a new two-step strategy for inductor modeling is
proposed that significantly improves the accuracy of offline methods. The new strategy is demonstrated and compared for both, singleobjective
and multi-objective optimization scenarios. Numerous experimental results show that the proposed two-step approach
outperforms simpler application strategies of surrogate modelling techniques, getting comparable performances to approaches based
on electromagnetic simulation but with orders of magnitude less computational effortPeer reviewe