Coevolving systems are notoriously difficult to understand. This is largely due to the Red Queen effect that dictates heterospecific fitness interdependence. In simulation studies of coevolving systems, master tournaments are often used to obtain more informed fitness measures by testing evolved individuals against past and future opponents. However, such tournaments still contain certain ambiguities. We introduce the use of a phenotypic cluster analysis to examine the distribution of opponent categories throughout an evolutionary sequence. This analysis, adopted from widespread usage in the bioinformatics community, can be applied to master tournament data. This allows us to construct behavior-based category trees, obtaining a hierarchical classification of phenotypes that are suspected to interleave during cyclic evolution. We use the cluster data to establish the existence of switching-genes that control opponent specialization, suggesting the retention of dormant genetic adaptations, that is, genetic memory. Our overarching goal is to reiterate how computer simulations may have importance to the broader understanding of evolutionary dynamics in general. We emphasize a further shift from a component-driven to an interaction-driven perspective in understanding coevolving systems. As yet, it is unclear how the sudden development of switching-genes relates to the gradual emergence of genetic adaptability. Likely, context genes gradually provide the appropriate genetic environment wherein the switching-gene effect can be exploited.

This paper proposes the use of coupled electric and magnetic circuits in the schematic models of high frequency integrated circuits, as an effective method to model the global inductive effects and coupling. These pairs of coupled circuits, called magneto-electro-equivalent circuit (MEEC) models, are obtained by partitioning the computational domain into several subdomains, each having its own electromagnetic field regime. The model reduction method we propose can be used not only to derive the full coupled electric and magnetic circuit model of a device starting from its layout, but also to correct the electric schematic by adding inductive parasitic effects. The MEEC approach uses special boundary conditions-called magnetic/electric hooks-on the interfaces between the subparts in which the computational domain is partitioned. The success of the correct extraction of inductive effects rely on the terminal reduction, i.e., the correct placement of magnetic hooks on the interfaces. In order to find this placement, a heuristic approach based on IC layout analysis is proposed. In order to consider the parasitic inductive couplings, the electric schematic circuit graph is enhanced with geometric information from the layout (e.g., node coordinates). The MEEC-based approach is validated for a real example of a low noise amplifier. Its initial layout design proved to be wrong only after the fabrication and characterization of the first prototype. The use of the inductive parasitic extraction tool, based on the MEEC approach, would have prevented this. MEEC is an alternative approach to vector potential equivalent circuit, with respect to which advantages and disadvantages are discussed.

We present a formulation of transient field solving that allows for the inclusion of semiconducting materials whose dynamic responses are prescribed by drift-diffusion modeling. The robustness and the feasibility is demonstrated by applying the scheme to compute accurately the large-signal response of an integrated inductor.