A new design methodology for radiofrequency circuits is presented that includes electromagnetic (EM) simulation of the inductors into the optimization flow. This is achieved by previously generating the Pareto-optimal front (POF) of the inductors using EM simulation. Inductors are selected from the Pareto front and their S-parameter matrix is included in the circuit netlist that is simulated using an RF simulator. Generating the EM-simulated POF of inductors is computationally expensive, but once generated, it can be used for any circuit design. The methodology is illustrated both for a single-objective and a multi-objective optimization of a Low Noise Amplifier.
In this paper, a new methodology for the automated generation of the optimal performance trade-offs of integrated inductors is presented. The methodology combines a multiobjective optimization algorithm with electromagnetic simulation to get highly accurate results. A set of sized inductors is obtained showing the best performance trade-offs for a given technology. Unlike reported approaches for inductor synthesis, performance trade-offs are generated offline, i.e., before any specific inductance or quality factor are required. The tight efficiency vs. accuracy trade-off of existing approaches is, in this way, avoided and performance evaluation via electromagnetic simulation becomes affordable.
Abstract-From the point of view of mixed-mode scattering parameters, S mm , a two-port device can be excited using different driving conditions. Each condition leads to a particular set of input reflection and input impedance coefficient definitions that should be carefully applied depending on the type of excitation and symmetry of the two-port device. Therefore, the aim of this paper is to explain the general analytic procedure for the evaluation of such reflection and impedance coefficients in terms of mixed-mode scattering parameters. Moreover, the driving of a two-port device as a one-port device is explained as a particular case of a two-port mixed-mode excitation using a given set of mixed-mode loads. The theory is applied to the evaluation of the quality factor, Q, of symmetrical and nonsymmetrical inductors.
The knowledge-intensive radiofrequency circuit design and the scarce design automation support play against the increasingly stringent time-to-market demands. Optimization algorithms are starting to play a crucial role; however, their effectiveness is dramatically limited by the accuracy of the evaluation functions of objectives and constraints. Accurate performance evaluation of radiofrequency passive elements e.g., inductors, is provided by electromagnetic simulators but their computational cost make their use within iterative optimization loops unaffordable. Surrogate modeling strategies, e.g., Kriging, support vector machines, artificial neural networks, etc, arise as a promising modeling alternative. However, their limited accuracy in this kind of applications has prevented a widespread use. In this paper, inductor performance properties are exploited to develop a two-step surrogate modeling strategy in order to evaluate the behavior of inductors with high efficiency and accuracy. An automated design flow for radio-frequency circuits using this surrogate modeling of passive components is presented. The methodology couples a circuit simulator with evolutionary computation algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA) or Non-Dominated Sorting Genetic Algorithm (NSGA-II). This methodology ensures optimal performances within short computation times by avoiding electromagnetic simulations of inductors during the entire optimization process and using a surrogate model that has less than 1% error in inductance and quality factor when compared against electromagnetic simulations. Numerous real-life experiments of single-objective and multi-objective low-noise amplifier design demonstrate the accuracy and efficiency of the proposed strategies.
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