Hierarchical Modeling, Optimization, and Synthesis for System-Level Analog and RF Designs

The paper describes the recent state of the art in hierarchical analog synthesis, with a strong emphasis on associated techniques for computer-aided model generation and optimization. Over the past decade, analog design automation has progressed to the point where there are industrially useful and commercially available tools at the cell level-tools for analog components with 10-100 devices. Automated techniques for device sizing, for layout, and for basic statistical centering have been successfully deployed. However, successful component-level tools do not scale trivially to system-level applications. While a typical analog circuit may require only 100 devices, a typical system such as a phase-locked loop, data converter, or RF front-end might assemble a few hundred such circuits, and comprise 10 000 devices or more. And unlike purely digital systems, mixed-signal designs typically need to optimize dozens of competing continuous-valued performance specifications, which depend on the circuit designer\´s abilities to successfully exploit a range of nonlinear behaviors across levels of abstraction from devices to circuits to systems. For purposes of synthesis or verification, these designs are not tractable when considered "flat." These designs must be approached with hierarchical tools that deal with the system\´s intrinsic design hierarchy. This paper surveys recent advances in analog design tools that specifically deal with the hierarchical nature of practical analog and RF systems. We begin with a detailed survey of algorithmic techniques for automatically extracting a suitable nonlinear macromodel from a device-level circuit. Such techniques are critical to both verification and synthesis activities for complex systems. We then survey recent ideas in hierarchical synthesis for analog systems and focus in particular on numerical techniques for handling the large number of degrees of freedom in these designs and for exploring the space of performance tradeoffs ear- - ly in the design process. Finally, we briefly touch on recent ideas for accommodating models of statistical manufacturing variations in these tools and flows