Recent Developments and Perspectives on Optimization Design Methods for Analog Integrated Circuits

Abstract

As the cornerstone of the modern information industry, designing a high-performance circuit is crucial. Due to the influence of external environmental and asymmetric arrangements, non-ideal factors in analog integrated circuits (ICs) cannot be ignored, which makes the design process heavily reliant on human experience, and the design efficiency is low. Recently, scholars have conducted extensive research on optimization design methods for analog ICs by combining artificial intelligence and optimization algorithms. In this article, the developments and perspectives on optimization design methods for analog ICs are reviewed. In traditional design methods, particle swarm optimization (PSO), the genetic algorithm (GA), and reinforcement learning (RL) have been applied with different computer-aided design (CAD) tools. A variety of circuit simulation software have been developed, such as Cadence, Ngspice, Pspice, etc. Due to its high precision, comprehensive functionality, and full-process simulation, Cadence has been widely used in the design of analog ICs. These methods can improve the design efficiency to a certain extent. In the iterative process, running the simulation software to obtain performance metrics can waste a lot of time. Thus, efficient optimization design methods have been proposed to improve the design efficiency by establishing a proxy model of the circuit, which can replace simulation software. Accordingly, three research directions in this field are proposed. In summary, this article can aid scholars in quickly understanding the current status of optimization design methods for analog ICs and provide guidance for future research.