A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation

Rayan Mina (1) , Chadi Jabbour (2, 3) , George E Sakr (4)

1 USJ - Université Saint-Joseph de Beyrouth
2 COMELEC - Département Communications & Electronique
3 C2S - Circuits et Systèmes de Communication
4 Virgil Systems Company

Analog integrated circuit design is widely considered a time-consuming task due to the acute dependence of analog performance on the transistors’ and passives’ dimensions. An important research effort has been conducted in the past decade to reduce the front-end design cycles of analog circuits by means of various automation approaches. On the other hand, the significant progress in high-performance computing hardware has made machine learning an attractive and accessible solution for everyone. The objectives of this paper were: (1) to provide a comprehensive overview of the existing state-of-the-art machine learning techniques used in analog circuit sizing and analyze their effectiveness in achieving the desired goals; (2) to point out the remaining open challenges, as well as the most relevant research directions to be explored. Finally, the different analog circuits on which machine learning techniques were applied are also presented and their results discussed from a circuit designer perspective.

Mots clés: automated circuit sizing; machine learning; analog IC design; deep neural networks; automated circuit sizing

Domaines: Electronique; Ingénierie assistée par ordinateur; Apprentissage [cs.LG]

Rayan Mina, Chadi Jabbour, George E Sakr. A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation. Electronics, 2022, 11 (3), pp.435. ⟨10.3390/electronics11030435⟩. ⟨hal-03701431⟩