  Analog and radio-frequency circuit design requires extensive exploration of
both circuit topology and parameters to meet specific design criteria like
power consumption and bandwidth. Designers must review state-of-the-art
topology configurations in the literature and sweep various circuit parameters
within each configuration. This design process is highly specialized and
time-intensive, particularly as the number of circuit parameters increases and
the circuit becomes more complex. Prior research has explored the potential of
machine learning to enhance circuit design procedures. However, these studies
primarily focus on simple circuits, overlooking the more practical and complex
analog and radio-frequency systems. A major obstacle for bearing the power of
machine learning in circuit design is the availability of a generic and diverse
dataset, along with robust metrics, which are essential for thoroughly
evaluating and improving machine learning algorithms in the analog and
radio-frequency circuit domain. We present AICircuit, a comprehensive
multi-level dataset and benchmark for developing and evaluating ML algorithms
in analog and radio-frequency circuit design. AICircuit comprises seven
commonly used basic circuits and two complex wireless transceiver systems
composed of multiple circuit blocks, encompassing a wide array of design
scenarios encountered in real-world applications. We extensively evaluate
various ML algorithms on the dataset, revealing the potential of ML algorithms
in learning the mapping from the design specifications to the desired circuit
parameters.
