A Hybrid GA/ML-Based End-to-End Automated Methodology for Design Acceleration of Wireless Communications CMOS LNAs

A new methodology for the RF/mmWave analog design process, automation and acceleration, is presented in this work. The proposed framework was implemented so as to accelerate the design cycle of analog/RF circuits by creating a dataset in a fully automated manner and training a combination of machine...

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Vydané v:Electronics (Basel) Ročník 12; číslo 11; s. 2428
Hlavní autori: Sad, Christos, Michailidis, Anastasios, Noulis, Thomas, Siozios, Kostas
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 27.05.2023
Predmet:
Acceleration
Algorithms
Analog circuits
Analog integrated circuits
Automation
Circuit design
CMOS
Complementary metal oxide semiconductors
Datasets
Design and construction
Design optimization
Design parameters
Design specifications
Design standards
Designers
Efficiency
Genetic algorithms
Machine learning
Mathematical optimization
Millimeter waves
Mobile communication systems
Neural networks
Optimization techniques
Polynomials
Simulation
Transistors
Transmission efficiency
Wireless communication systems
Wireless communications
Greece
ISSN:2079-9292, 2079-9292
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Shrnutí:A new methodology for the RF/mmWave analog design process, automation and acceleration, is presented in this work. The proposed framework was implemented so as to accelerate the design cycle of analog/RF circuits by creating a dataset in a fully automated manner and training a combination of machine learning models for the optimal design parameters’ prediction. machine learning polynomial regression was adopted to accelerate the design process, predicting the optimal design parameters’ values while genetic algorithm optimization was exploited for the dataset creation automation. To evaluate the efficiency of the proposed methodology, the framework was implemented for the design of a common source Low-Noise-Amplifier, using a 65 nm CMOS process node. The proposed methodology successfully tackles the design cycle speed-up, automation, and acceleration, utilizing machine learning prediction for the design parameters and genetic algorithm for the dataset creation automation instead of the classical, simulation-based, standard design methodology. The provided experimental results have shown the effectiveness of the proposed hybrid approach, creating very precise RF matching networks for LNA designs and achieving >99.9% wave transmission efficiency while reaching >99% accuracy on the parameters’ prediction task.
Bibliografia:ObjectType-Article-1
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12112428