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Using Artificial Neural Networks for Analog Integrated Circuit Design Automation (Paperback, 1st ed. 2020)
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Using Artificial Neural Networks for Analog Integrated Circuit Design Automation (Paperback, 1st ed. 2020)
Series: SpringerBriefs in Applied Sciences and Technology
Expected to ship within 10 - 15 working days
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This book addresses the automatic sizing and layout of analog
integrated circuits (ICs) using deep learning (DL) and artificial
neural networks (ANN). It explores an innovative approach to
automatic circuit sizing where ANNs learn patterns from previously
optimized design solutions. In opposition to classical
optimization-based sizing strategies, where computational
intelligence techniques are used to iterate over the map from
devices' sizes to circuits' performances provided by design
equations or circuit simulations, ANNs are shown to be capable of
solving analog IC sizing as a direct map from specifications to the
devices' sizes. Two separate ANN architectures are proposed: a
Regression-only model and a Classification and Regression model.
The goal of the Regression-only model is to learn design patterns
from the studied circuits, using circuit's performances as input
features and devices' sizes as target outputs. This model can size
a circuit given its specifications for a single topology. The
Classification and Regression model has the same capabilities of
the previous model, but it can also select the most appropriate
circuit topology and its respective sizing given the target
specification. The proposed methodology was implemented and tested
on two analog circuit topologies.
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