Pareto surfaces in the performance space determine the range of feasible performance values for a circuit topology in a given technology. We present a non-dominated sorting based global optimization algorithm to generate the nominal pareto front efficiently using a simulator-in-a-loop approach. The solutions on this pareto front combined with efficient Monte Carlo approximation ideas are then used to compute the yield-aware pareto fronts. We show experimental results for both the nominal and yield-aware pareto fronts for power and phase noise for a voltage controlled oscillator (VCO) circuit. The presented methodology computes yield-aware pareto fronts in approximately 5-6 times the time required for a single circuit synthesis run and is thus practically efficient. We also show applications of yield-aware paretos to find the optimal VCO circuit to meet the system level specifications of a phase locked loop.

Abstract — In this paper we present a parameterized reduction technique for non-linear systems. Our approach combines an existing non-parameterized trajectory piecewise linear method for non-linear systems, with an existing moment matching parameterized technique for linear systems. Results and comparisons are presented for two examples: an analog non-linear circuit, and aMEMswitch. I.

Abstract — Trajectory-based methods offer an attractive methodology for automated, on-demand generation of macromodels for custom circuits. These models are generated by sampling the state trajectory of a circuit as it simulates in the time domain, and building macromodels by reducing and interpolating among the linearizations created at a suitably spaced subset of the time points visited during training simulations. However, a weak point in conventional trajectory models is the reliance on a single, global reduction matrix for the state space. We develop a new, faster method that generates and weaves together a larger set of smaller localized linearizations for the trajectory samples. The method not only improves speedups to 30X over SPICE, but as a side benefit also provides a platform for parametric small-signal simulation of circuits with variational device/process parameters, at a speedup of roughly 200X over SPICE. I.

Abstract—In this paper, we present a technique for automatically extracting nonlinear macromodels of biomedical microelectromechanical systems devices from physical simulation. The technique is a modification of the recently developed trajectory piecewise-linear approach, but uses ideas from balanced truncation to produce much lower order and more accurate models. The key result is a perturbation analysis of an instability problem with the reduction algorithm, and a simple modification that makes the algorithm more robust. Results are presented from examples to demonstrate dramatic improvements in reduced model accuracy and show the limitations of the method. Index Terms—Biomedical microelectromechanical devices (bioMEMS), microelectromechanical devices (MEMS), model order reduction, nonlinear dynamical systems, perturbation methods, piecewise linear models. I.