• Resumo

    Explorando Predição da Caracterização Elétrica com Machine Learning

    Data de publicação: 03/05/2023

    With the advancement of integrated circuit manufacturing technology,
    more and more aspects must be considered during the electrical
    characterization of circuits in order to solve challenges such as process
    variability effect. This increases the characterization time due
    to traditional techniques based on exhaustive electrical simulations.
    The adoption of machine learning techniques already helps digital
    design at many levels of abstraction. Thus, the main objective of
    this research is to evaluate machine learning regression algorithms
    as an alternative to exhaustive electrical simulation in the cell characterization
    project. In this step, multiple linear regression, support
    vector regression, decision trees and random forest algorithms were
    considered. This work presents the results of NAND2 and NOT
    gates using bulk CMOS technology. Specifically, the energy values
    and the propagation times of this circuit will be predicted separately.
    A comparative analysis, together with the inference time,
    is made for each dependent variable between the models, in order
    to understand which is the best regression model for the task. The
    algorithm with the lowest cost function and shortest inference time
    proved to be the decision tree for all predicted variables in both

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