Author:

Keywords:

Science & Technology, Technology, Computer Science, Hardware & Architecture, Engineering, Electrical & Electronic, Computer Science, Engineering, DC-DC converters, Expensive optimization, Active learning sample selection, Layout-based modeling, Tapered inductor layout, ON-CHIP, EVOLUTIONARY COMPUTATION, COMPONENTS, DESIGN, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies, Electrical & Electronic Engineering, 4008 Electrical engineering, 4009 Electronics, sensors and digital hardware

Abstract:

The evolution of computer-aided design tools has extended the capabilities of a designer by pushing the optimality of complex circuits beyond the ad hoc manual implementation. This work presents a framework to co-optimize the circuit and the layout parameters of fully integrated inductive DC-DC converters. The framework comprises expensive optimization that is speeded up by active learning sample selection and evolutionary techniques to acquire an optimal converter. A tapered inductor topology is used to increase the quality of the on-chip inductor and to improve the efficiency of the overall monolithic DC-DC converter. The optimization framework is validated by co-optimizing the design parameters and the tapered inductor layout for a fully-integrated DC-DC boost converter in a 0.13 μm CMOS technology. The power loss in the circuit is reduced with 27 % resulting in a 7 % efficiency improvement, compared to a fully-integrated DC-DC boost converter with a regular inductor topology. © 2013 Springer Science+Business Media New York.