Rapid developments in semiconductor technology have substantially increased the computational capability of computers. As a result of this and recent developments in theory, machine learning (ML) techniques have become attractive in many new applications. This trend has also inspired researchers working on integrated circuit (IC) design and optimization. ML-based design approaches have gained importance to challenge/aid conventional design methods since they can be employed at different design levels, from modeling to test, to learn any nonlinear input-output relationship of any analog and radio frequency (RF) device or circuit; thus, providing fast and accurate responses to the task that they have learned. Furthermore, employment of ML techniques in analog/RF electronic design automation (EDA) tools boosts the performance of such tools. In this paper, we summarize the recent research and present a comprehensive review on ML techniques for analog/RF circuit modeling, design, synthesis, layout, and test.

半导体技术的飞速发展大大提高了计算机的计算能力。由于理论上的这种发展和最近的发展，机器学习（ML）技术在许多新应用中变得越来越有吸引力。这一趋势也激发了研究人员从事集成电路（IC）设计和优化的研究。基于ML的设计方法对于挑战/帮助常规设计方法变得越来越重要，因为它们可以用于从建模到测试的不同设计级别，以了解任何模拟和射频（RF）设备或电路的任何非线性输入输出关系。 ; 因此，可以对他们所学的任务提供快速而准确的响应。此外，在模拟/ RF电子设计自动化（EDA）工具中使用ML技术可提高此类工具的性能。