Review: Machine learning techniques in analog/RF integrated circuit design, synthesis, layout, and test

Abstract

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.