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LIMA JACINTO, GABRIEL; YUKI IMAMURA, LUCAS ; GRELLERT, MATEUS ; MEINHARDT, CRISTINA . Explorando Predição da Caracterização Elétrica com Machine Learning. In: Computer on the Beach, 2023, Florianópolis - Santa Catarin. Anais do XIV Computer on the Beach - COTB'23, 2023. v. 1. p. 194-8.
Abstract
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 gates.