A Fast and Accurate Yield Optimization Method for Designing Operational Amplifier Using Multi-Objective Evolutionary Algorithm Based on Decomposition

Background and Objectives: In recent years, the electronics industry has experienced rapid expansion, leading to increased concerns surrounding the expenses associated with designing and sizing integrated circuits. The reliability of these circuits has emerged as a critical factor influencing the success of production. Consequently, the necessity for optimization algorithms to enhance circuit yield has become increasingly important. This article introduces an enhanced approach for optimizing analog circuits through the utilization of a Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) and includes a thorough evaluation. The main goal of this methodology is to improve both the speed and precision of yield calculations.Methods: The proposed approach includes generating initial designs with desired characteristics in the critical analysis phase. Following this, designs that exceed a predefined yield threshold are replaced with the initial population that has lower yield values, generated using the classical MOEA/D algorithm. This replacement process results in notable improvements in yield efficiency and computational speed compared to alternative Monte Carlo-based methods.Results: To validate the effectiveness of the presented approach, some circuit simulations were conducted on a two-stage class-AB Op-Amp in 180 nm CMOS technology. With a high yield value of 99.72%, the approach demonstrates its ability to provide a high-speed and high-accuracy computational solution using only one evolutionary algorithm. Additionally, the observation that modifying the initial population can improve the convergence speed and yield value further enhances the efficiency of the technique. These findings, backed by the simulation results, validate the efficiency and effectiveness of the proposed approach in optimizing the performance of the Op-Amp circuit.Conclusion: This paper presents an enhanced approach for analog circuit optimization using MOEA/D. By incorporating critical analysis, it generates initial designs with desired characteristics, improving yield calculation efficiency. Designs exceeding a preset yield threshold are replaced with lower yield ones from the initial population, resulting in enhanced computational speed and accuracy compared to other Monte Carlo-based methods. Simulation results for a two-stage class-AB Op-Amp in 180 nm CMOS technology show a yield of 99.72%, highlighting the method’s effectiveness in achieving high speed and accuracy with a single evolutionary algorithm.