Efficient Deterministic Multi‐Objective Sizing Algorithm for Analog Circuits Based on Modified Weighted Sum Method

ABSTRACT Deterministic optimization methods offer superior efficiency and reproducibility compared with stochastic approaches. This paper presents an improved deterministic single‐objective (DSO) sizing algorithm that enhances computational efficiency while effectively handling design constraints. Building upon this framework, a deterministic multi‐objective (DMO) sizing algorithm is further developed by integrating a modified weighted sum method to enable high‐quality Pareto front exploration. Experimental results show that the proposed DSO sizing algorithm surpasses the best‐performing baseline in optimization efficiency, yielding improvements of 21.48% and 76.97% for the two‐stage and three‐stage Op‐Amps, respectively. Similarly, the proposed DMO sizing algorithm outperforms NSGA‐II in the spacing metric, achieving improvements of 80.27% and 73.29% for the two‐stage and three‐stage Op‐Amps, respectively, while also producing a higher‐quality Pareto front. These results demonstrate that the proposed deterministic sizing algorithms achieve reliable convergence, efficient design space exploration, and strong competitiveness against existing optimization methods. By providing repeatable, physically interpretable, and computationally efficient optimization results, the proposed deterministic sizing algorithm enables designers to systematically explore performance trade‐offs and make informed decisions in complex analog IC multi‐objective optimization scenarios.