Circuit optimization driven by worst-case distances

A novel method for circuit optimization in the face of manufacturing process variations is presented. It is based on the characterization of the feasible design space by worst-case points and related gradients. The expense for this characterization is linear with the number of circuit performances. A deterministic optimization procedure based on the so-called worst-case distances is introduced, combining nominal and tolerance design in a single design objective. The entire optimization process with regard to performance, yield, and robustness uses sensitivity analyses and requires a much smaller number of simulations than the Monte-Carlo-based approaches. Moreover, the proposed method takes into account the partitioning of the parameter space into deterministic and statistical parameters, which is an inherent property of integrated circuit design.