Machine Learning for Analog IC Design Automation: A Review
Keywords:
Analog IC design, Automated circuit sizing, Deep neural networks, Integrated circuit (IC) technology, Machine learningAbstract
Analog performance is significantly dependent on the dimensions of transistors and passive components, rendering analog integrated circuit design a typically protracted endeavour. Over the last decade, researchers have conducted significant studies to reduce the duration required for analog circuit front-end design using automation. The substantial advancements in high-performance computers have rendered machine learning accessible to a broader audience. This work aims to evaluate the current use of machine-learning techniques in analog circuit sizing and assess their effectiveness in achieving their intended goals. It also explores areas that need further research and highlights recent trends in the field. In addition, the study looks at the different types of analog circuits involved in these machine-learning approaches and discusses the results from a circuit designer’s point of view.
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