Artificial Neural Network Assisted Analog IC Sizing Tool

Islamoglu, Gamze; Cakici, Tugberk Ogulcan; Afacan, Engín; Dündar, Günhan

doi:10.1109/smacd.2019.8795293

Cited by 31 publications

(9 citation statements)

References 12 publications

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“…Moreover, layout parasitic effects were included in the prediction, which constituted a clear advantage. A significantly different approach was used in [35], wherein the objective was to reduce the convergence time of the evolutionary algorithm that predicted the circuits' design parameters. Data produced from on-going optimization iterations were used to train an NN in parallel, in order to replace the time-consuming SPICE circuit simulators.…”

Section: Hybrid Ic Design Automation Methodsmentioning

confidence: 99%

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A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation

Mina

Jabbour

Sakr³

2022

Electronics

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Analog integrated circuit design is widely considered a time-consuming task due to the acute dependence of analog performance on the transistors’ and passives’ dimensions. An important research effort has been conducted in the past decade to reduce the front-end design cycles of analog circuits by means of various automation approaches. On the other hand, the significant progress in high-performance computing hardware has made machine learning an attractive and accessible solution for everyone. The objectives of this paper were: (1) to provide a comprehensive overview of the existing state-of-the-art machine learning techniques used in analog circuit sizing and analyze their effectiveness in achieving the desired goals; (2) to point out the remaining open challenges, as well as the most relevant research directions to be explored. Finally, the different analog circuits on which machine learning techniques were applied are also presented and their results discussed from a circuit designer perspective.

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Section: Hybrid Ic Design Automation Methodsmentioning

confidence: 99%

“…Hybrid methods combining global optimization and NN were proposed in [32][33][34][35][36][37]. These techniques achieve enhanced results compared to pure multi-objective optimization methods, at a lower computation time.…”

mentioning

confidence: 99%

A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation

Mina

Jabbour

Sakr³

2022

Electronics

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“…The performance of the circuit is predicted by replacing the trained NN instead of a simulated system in the optimization loop, thus reducing the design time substantially. The execution time issue is faster in this case as the new performance and output result are predicted from the previous data without performing simulation [6].…”

Section: Neural Network Techniquementioning

confidence: 99%

“…Neural networks are very effective tools used in various design steps of analog and RF circuits [43][44][45]. They are as a 'black-box' modeling and are exerted in modeling of onchip inductors [46], semiconductor devices [47], conventional analog circuit building blocks [48,49], analog IC sizing [6,50], and in crucial RF circuit blocks such as power amplifiers [51], RF front-end receivers [52], low noise amplifiers [53], voltage-controlled oscillators [54,55], and multiple-input multiple-output (MIMO) systems [56,57].…”

Section: Neural Network Techniquementioning

confidence: 99%

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Optimization techniques for analog and RF circuit designs: an overview

Kouhalvandi

Ceylan²,

Özoğuz

2020

Analog Integr Circ Sig Process

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Optimizations have gained much consideration from the researchers working in the domains of analog and radio frequency (RF), recently. Dealing with highly nonlinear behavior of active components and aiming to meet design specifications are common issues in all nonlinear circuit designs. As a consequence and accordingly, many studies have been conducted on diverse optimization methods and algorithms for tackling the design problems and meeting optimal solutions with high accuracy. The main purpose of this article is to provide a comprehensive and systematic literature review for the optimization approaches applied by the researchers for designing various analog and microwave circuits. We focus on considering the existing optimization methods from the newly published optimization methods in the last decade. Thus, this study can guide and enlighten complementary metal-oxide-semiconductor analog and RF microwave circuit designers to consider optimization methods commonly used in both areas and to expand conventional performance figures used in their area.

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