An Analog Circuit Designing Model via Machine Learning for Stage Classification and Evolutionary Solution Optimization Algorithm

International Journal of Electronics and Communication Engineering

© 2023 by SSRG - IJECE Journal

Volume 10 Issue 6

Year of Publication : 2023

Authors : M. P. Varghese, T. Muthumanickam

10.14445/23488549/IJECE-V10I6P103

How to Cite?

M. P. Varghese, T. Muthumanickam, "An Analog Circuit Designing Model via Machine Learning for Stage Classification and Evolutionary Solution Optimization Algorithm," SSRG International Journal of Electronics and Communication Engineering, vol. 10,  no. 6, pp. 17-26, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I6P103

Abstract:

This work aims to propose a bottom-up, two-step process that streamlines the design of analogue devices by using machine learning techniques. The complicated nature of these difficulties, which involve numerous variables and objectives, necessitates using designers' skills and knowledge while designing analogue complementary metal-oxide-semiconductor (CMOS) integrated circuits. The study offers a framework detailing the unique characteristics of creating analogue circuits using machine learning, and it looks into the potential of libraries that contain open machine-learning models to assist designers. Traditionally, commercial CMOS or software simulations have been used to create neural network designs; however, these methods may not always provide the best results. A three-stage device design is used to validate the suggested method. Using a machine learning technique called the decision tree; the stage type is correctly predicted with an accuracy of 89.74% in the first phase. To create prediction logic, two rule induction techniques are also used. In the second step, four learning techniques, decision trees, random forests, gradient-boosted trees, and support vector machines, are used to forecast the typical parameters for each stage type. The support vector machine yields the best results and has the lowest error rates of all these methods.

Keywords:

Artificial neural network, Analog system, CMOS circuit, Signal processing, Learning algorithm.

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