  As fifth-generation (5G) and upcoming sixth-generation (6G) communications
exhibit tremendous demands in providing high data throughput with a relatively
low latency, millimeter-wave (mmWave) technologies manifest themselves as the
key enabling components to achieve the envisioned performance and tasks. In
this context, mmWave integrated circuits (IC) have attracted significant
research interests over the past few decades, ranging from individual block
design to complex system design. However, the highly nonlinear properties and
intricate trade-offs involved render the design of analog or RF circuits a
complicated process. The rapid evolution of fabrication technology also results
in an increasingly long time allocated in the design process due to more
stringent requirements. In this thesis, 28-GHz transceiver circuits are first
investigated with detailed schematics and associated performance metrics. In
this case, two target systems comprising heterogeneous individual blocks are
selected and demonstrated on both the transmitter and receiver sides.
Subsequently, some conventional and large-scale machine learning (ML)
approaches are integrated into the design pipeline of the chosen systems to
predict circuit parameters based on desired specifications, thereby
circumventing the typical time-consuming iterations found in traditional
methods. Finally, some potential research directions are discussed from the
perspectives of circuit design and ML algorithms.
