Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
An infeasible interior-point algorithm for solving primal and dual geometric programs
Mathematical Programming: Series A and B - Special issue: interior point methods in theory and practice
Proceedings of the 39th annual Design Automation Conference
Proceedings of the conference on Design, automation and test in Europe
Optimal design of a CMOS op-amp via geometric programming
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
CMOS op-amp sizing using a geometric programming formulation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Statistical Parameter Identification of Analog Integrated Circuit Reverse Models
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
A convex macromodeling of dynamic comparator for analog circuit synthesis
Analog Integrated Circuits and Signal Processing
Hi-index | 0.00 |
This paper presents a new method to automatically generate posynomial symbolic expressions for the performance characteristics of analog integrated circuits. Both the coefficient set as well as the exponent set of the posynomial expression, for some performance as a function of the design variables, are determined based on performance data extracted from SPICE simulation results with device-level accuracy. Techniques from design of experiments (DOE) are used to generate an optimal set of sample points to fit the models. We will prove that the optimization problem formulated for this problem typically corresponds to a non-convex problem, but has no local minima. The presented method is capable of generating posynomial performance expressions for both linear and nonlinear circuits and circuit characteristics. This approach allows to automatically generate an accurate sizing model that can be used to compose a geometric program that fully describes the analog circuit sizing problem. The automatic generation avoids the time-consuming nature of hand-crafted analytic model generation. Experimental results illustrate the capabilities of the presented modeling technique.