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.

翻译：随着第五代（5G）及即将到来的第六代（6G）通信对高数据吞吐量与相对低延迟提出巨大需求，毫米波技术已成为实现预期性能与任务的关键使能组件。在此背景下，毫米波集成电路在过去数十年间引起了广泛的研究关注，涵盖从独立模块设计到复杂系统设计的各个层面。然而，高度非线性的特性与错综复杂的权衡关系使得模拟或射频电路的设计过程极为复杂。制造技术的快速演进也因更严格的要求导致设计周期不断延长。本论文首先研究了28GHz收发器电路，提供了详细原理图及相关性能指标。在此案例中，选取并演示了包含异构独立模块的两套目标系统，分别针对发射端与接收端。随后，将若干传统与大规模机器学习方法整合至所选系统的设计流程中，以基于期望规格预测电路参数，从而规避传统方法中典型耗时的迭代过程。最后，从电路设计与机器学习算法的角度探讨了若干潜在研究方向。