Efficient Approaches to Overcome Non-Convexity Issues in Analog Design Automation

Supriyo Maji and Pradip Mandal
IIT Kharagpur


We propose two approaches of overcoming the limitation of convex programming technique to analog design automation. Analog design performance constraints have to be cast in posynomial inequality format for suitability in convex optimization based application. But in most cases original equations are not in posynomial so, either they are deliberately modeled or approximated. This leads to inaccuracy. Our first approach is based on exploiting the apparent benefit of convex programming based global optimizer but still making use of highly accurate non-posynomial or signomial model. To achieve that, we combine both global and local optimizer. Global optimizer handles less accurate posynomial equation ensuring global optimality. And the initial guess obtained therefore is used by local optimizer to handle accurate signomial model. Second approach demonstrates that the targeted region for circuit sizing to be modeled can be reduced which invariably leads to better model accuracy. We also present one approach of modeling low dropout regulator(LDO)performance metrics, which is fast and developed models are reusable. Accuracy performance is shown in terms of designing LDO, two stage and folded cascode op-amps.