Variability-aware multilevel integrated spiral inductor synthesis

To successfully design spiral inductors in increasingly complex and integrated mixed-signal systems, effective design automation techniques must be created. In this paper, the authors develop an automated synthesis methodology for integrated spiral inductors to efficiently generate Pareto-optimal designs based on application requirements. At its core, the synthesis approach employs a scalable multilevel single-objective optimization,engine that integrates the flexibility of deterministic pattern search optimization with the rapid convergence of local nonlinear convex optimization. Multiobjective optimization techniques and surrogate functions are utilized to approximate Pareto surfaces in the design space to locate Pareto-optimal spiral inductor designs. Using the synthesis methodology, the authors also demonstrate how to reduce the impact of process variation and other sources of modeling error on spiral inductors. The results indicate that the multilevel single-objective optimization engine locates near-optimal spiral inductor geometries with significantly fewer function evaluations than current techniques, whereas the overall synthesis methodology efficiently optimizes inductor designs with an improvement of up to 51% in key design constraints while reducing the impact of process variation and modeling error.