Behavior-level yield enhancement approach for large-scaled analog circuits

In traditional yield enhancement approaches, a lot of computation efforts have to be paid first to find the feasible regions and the Pareto fronts, which will become a heavy cost for large analog circuits. In order to reduce the computation efforts, this work tries to finish all iteration steps of the yield enhancement flow at behavior level. First, a novel force-directed nominal point moving (NPM) algorithm is proposed to find a better nominal point without building the feasible regions. Then, an equation-based behavior-level sizing approach is proposed to map the NPM results at performance level to behavior-level parameters. A fast behavior-level Monte Carlo simulation is also proposed to shorten the iterative yield enhancement flow. Finally, using the obtained behavioral parameters as the sizing targets of each sub-block, the device sizing time is significantly reduced instead of sizing from the system-level specifications directly. As demonstrated on a complex CPPLL design, this behavior-level approach could be another efficient methodology to help designers improve their analog circuits toward better yield.