Efficient Hierarchical mm-Wave System Synthesis with Embedded Accurate Transformer and Balun Machine Learning Models

Integrated circuit design in millimeter-wave (mm- Wave) bands is exceptionally complex and dependent on costly electromagnetic (EM) simulations. Therefore, in the past few years, a growing interest has emerged in developing novel optimization-based methodologies for the automatic design of mm- Wave circuits. However, current approaches lack scalability when the circuit/system complexity increases. Besides, many also depend on EM simulators, which degrade their efficiency. This work resorts to hierarchical system partitioning and bottom-up design approaches, where a precise machine learning model - composed of hundreds of seamlessly integrated sub-models that guarantee high accuracy (validated against EM simulations and measurements) up to 200GHz - is embedded to design passive components, e.g., transformers and baluns. The model generates optimal design surfaces to be fed to the hierarchical levels above or acts as a performance estimator. With the proposed scheme, it is possible to remove the dependency of EM simulations during optimization. The proposed mixed-optimal-surface, performance estimator, and simulation-based bottom-up multiobjective optimization (MOO) are used to fully design aKa-band mm- Wave transmitter from the device up to the system level in 65-nm CMOS for state-of-the-art specifications.