Abstract
This chapter describes the optimization-based approach to analog integrated circuit (IC) sizing taken in AIDA-C. The multi-objective optimization methods implemented in AIDA-C are the non-dominated sorting genetic algorithm II (NSGA-II), the multi-objective simulated annealing (MOSA) and the multi-objective particle swarm optimization (MOPSO). Additionally, the algorithm implementations share a common interface; easing the intermingling of tentative solutions between optimization technics in order to, not only, use the different approaches by themselves, but also to explore combinations between them. Section 4.1 describes how the circuit design specifications are mapped into the multi-objective optimization problem; and Sect. 4.2 describes the optimization kernels implemented in AIDA-C. Finally, Sect. 4.3 describes how the optimization process is enhanced with the usage of machine learning techniques that automatically add design knowledge to guide the optimization.
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Lourenço, N., Martins, R., Horta, N. (2017). Multi-objective Optimization Kernel. In: Automatic Analog IC Sizing and Optimization Constrained with PVT Corners and Layout Effects. Springer, Cham. https://doi.org/10.1007/978-3-319-42037-0_4
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DOI: https://doi.org/10.1007/978-3-319-42037-0_4
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