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Erschienen in:
2020 | OriginalPaper | Buchkapitel
4. Using ANNs to Size Analog Integrated Circuits
verfasst von : João P. S. Rosa, Daniel J. D. Guerra, Nuno C. G. Horta, Ricardo M. F. Martins, Nuno C. C. Lourenço
Erschienen in: Using Artificial Neural Networks for Analog Integrated Circuit Design Automation
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Abstract
In this chapter, two artificial neural network (ANN) models are proposed for analog integrated circuit (IC) sizing. The first one, a regression-only model, serves as a proof of concept; i.e., the applicability of ANNs to analog IC sizing is tested. In this architecture, we explore how an ANN that is trained using circuit sizing solutions from previous optimizations can learn the design patterns of the circuit (a single circuit topology for differential amplifiers is considered in each training phase). The second one, a classification and regression model, is also presented. This architecture selects not only the most appropriate circuit topology, but also its respective sizing given the target specification (more than one topology is considered in the training phase).