rameter m. This value could, for example, be chosen in order to minimize the oscillator's settling time. In summary, the behavioral modeling method here presented helps to derive oscillator models that contribute to boosting overall simulation efficiency. Moreover, these mod- els can also be integrated in procedures or algorithms for making decisions on (optimal) design parameters as they help to focus on that part of the oscillator's dynamic behavior that is often of greatest interest. Conclusions and directions for further research This chapter has presented both the theory and algorithms to describe and model an oscillator's dynamic behavior. By proper partitioning of the circuit equations, it is possible to separately compute the periodic steady-state behavior and the transient dy- namics. The theory builds on perturbation techniques and averaging. It provides a framework for the analysis of an oscillator's entire phase noise, injection locking and settling behavior. All these phenomena can be explained as consequences of the same underlying mechanism: perturbations drive the oscillator's core system from one state to another. Two key applications of the theory above are oscillator phase-noise analysis and the extraction of compact behavioral models for (sets of coupled) harmonic oscillators. The work on phase noise culminated in a semi-analytic method to compute an oscil- lator's phase-noise spectrum in a few minutes time. Moreover, for the first time, a method was presented that enables us to accurately compute the very-close-in phase- noise spectrum. The harmonic oscillator behavioral models can be solved using a large simulation time step. As such, they are better suited for lengthy or repetitive (system- level) simulations than is the oscillator's original set of circuit equations. The accuracy and efficiency of the behavioral models was verified for both a single harmonic oscil- lator and an harmonic quadrature oscillator. The work above is but the beginning of what promises to become a powerful theory 