An Integrated MaxFit Genetic Algorithm-SPICE Framework for 2-Stage Op-Amp Design Automation

M, Harsha; Harish, B. P.

doi:10.1109/isvlsi.2018.00040

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…In this approach, the experts have adopted a special software framework to generate the dataset called MaxFit Genetic Algorithm-SPICE. The framework is a genetic multi-objective optimization algorithm based on a genetic multi-objective optimization algorithm that relies on a set of already predefined circuit equations that the designer of the analog circuit has developed [11]. Figure 5 shows the network structure of this ANN model, from which it can be seen that there are many hidden layers as well as a multilayer perceptron of neurons to map the input-output relationship.…”

Section: Supervised Learning With Analog Integrated Circuit Designmentioning

confidence: 99%

Analog integrated circuit design with machine learning

Gao

2023

TNS

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Due to the widespread application of semiconductor technology in integrated circuits, more and more design studies on analog integrated circuits are gradually being implemented. However, due to the nature of analog integrated circuits, it is time-consuming and inefficient. Therefore, there are lots of experts studying how to reduce the design cycle of analog ICs. The use of machine learning in analog circuits stands out, as machine learning-based design methods have significantly reduced the analog cycle time. This review report will first introduce the algorithms related to machine learning, and the second half will outline the existing applications of machine learning in an analog integrated circuit and compare them.

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Section: Supervised Learning With Analog Integrated Circuit Designmentioning

confidence: 99%