Efficient Hierarchical mm-Wave System Synthesis with Embedded Accurate Transformer and Balun Machine Learning Models
Martins, R. M.
; Mendes, L.
Efficient Hierarchical mm-Wave System Synthesis with Embedded Accurate Transformer and Balun Machine Learning Models, Proc Asia and South Pacific Design Automation Conference ASP-DAC, Tokyo, Japan, Vol. , pp. - , January, 2023.
Digital Object Identifier:
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 a Ka-band mm-Wave
transmitter from the device up to the system level in 65-nm CMOS
for state-of-the-art specifications.