In this book innovative research using artificial neural networks (ANNs) is conducted to
automate the sizing task of RF IC design which is used in two different steps of the automatic
design process. The advances in telecommunications such as the 5th generation broadband or 5G
for short open doors to advances in areas such as health care education resource management
transportation agriculture and many other areas. Consequently there is high pressure in
today's market for significant communication rates extensive bandwidths and ultralow-power
consumption. This is where radiofrequency (RF) integrated circuits (ICs) come in hand playing
a crucial role. This demand stresses out the problem which resides in the remarkable difficulty
of RF IC design in deep nanometric integration technologies due to their high complexity and
stringent performances. Given the economic pressure for high quality yet cheap electronics and
challenging time-to-market constraints there is an urgent need for electronic design
automation (EDA) tools to increase the RF designers' productivity and improve the quality of
resulting ICs. In the last years the automatic sizing of RF IC blocks in deep nanometer
technologies has moved toward process voltage and temperature (PVT)-inclusive optimizations to
ensure their robustness. Each sizing solution is exhaustively simulated in a set of PVT corners
thus pushing modern workstations' capabilities to their limits. Standard ANNs applications
usually exploit the model's capability of describing a complex harder to describe relation
between input and target data. For that purpose ANNs are a mechanism to bypass the process of
describing the complex underlying relations between data by feeding it a significant number of
previously acquired input output data pairs that the model attempts to copy. Here and firstly
the ANNs disrupt from the most recent trials of replacing the simulator in the simulation-based
sizing with a machine deep learning model by proposing two different ANNs the first
classifies the convergence of the circuit for nominal and PVT corners and the second predicts
the oscillating frequencies for each case. The convergence classifier (CCANN) and frequency
guess predictor (FGPANN) are seamlessly integrated into the simulation-based sizing loop
accelerating the overall optimization process. Secondly a PVT regressor that inputs the
circuit's sizing and the nominal performances to estimate the PVT corner performances via
multiple parallel artificial neural networks is proposed. Two control phases prevent the
optimization process from being misled by inaccurate performance estimates. As such this book
details the optimal description of the input output data relation that should be fulfilled. The
developed description is mainly reflected in two of the system's characteristics the shape of
the input data and its incorporation in the sizing optimization loop. An optimal description of
thesecomponents should be such that the model should produce output data that fulfills the
desired relation for the given training data once fully trained. Additionally the model should
be capable of efficiently generalizing the acquired knowledge in newer examples i.e.
never-seen input circuit topologies.
