  Analog mixed-signal (AMS) circuit architecture has evolved towards more
digital friendly due to technology scaling and demand for higher
flexibility/reconfigurability. Meanwhile, the design complexity and cost of AMS
circuits has substantially increased due to the necessity of optimizing the
circuit sizing, layout, and verification of a complex AMS circuit. On the other
hand, machine learning (ML) algorithms have been under exponential growth over
the past decade and actively exploited by the electronic design automation
(EDA) community. This paper will identify the opportunities and challenges
brought about by this trend and overview several emerging AMS design
methodologies that are enabled by the recent evolution of AMS circuit
architectures and machine learning algorithms. Specifically, we will focus on
using neural-network-based surrogate models to expedite the circuit design
parameter search and layout iterations. Lastly, we will demonstrate the rapid
synthesis of several AMS circuit examples from specification to silicon
prototype, with significantly reduced human intervention.
