Optimization of a low noise amplifier with two technology nodes using an interactive evolutionary approach

dc.contributor.authorDE LIMA MORETO, R. A.
dc.contributor.authorMARIANO A.
dc.contributor.authorTHOMAZ, C. E.
dc.contributor.authorSalvador Gimenez
dc.description.abstract© 2021, Springer Science+Business Media, LLC, part of Springer Nature.Nowadays, wireless communications at frequencies of gigahertz have an increasing demand due to the ever-increasing number of electronic devices that uses this type of communication. However, the design of Radio Frequency (RF) circuits is difficult, time-consuming and based on designer knowledge and experience. This work proposes an interactive evolutionary approach based on genetic algorithm, implemented in the in-house iMTGSPICE optimization tool, to perform the optimization process of a Low-Power Low Noise Amplifier (LNA) dedicated to Wireless Sensor Networks (WSN), which is robust through the corner and Monte Carlo analyses and implemented in two Bulk CMOS technology nodes: 130 nm and 65 nm. Regarding each technology node, we performed two experimental studies to optimize the LNA. The first one used the conventional non-interactive approach of iMTGSPICE, which was not assisted by a designer during the optimization process. The second one used the interactive approach of iMTGSPICE, which was monitored and assisted by a beginner designer during the optimization process. The obtained results demonstrated that the interactive approach of iMTGSPICE performed the optimization process of the robust LNA from 16 to 94% faster than the non-interactive evolutionary approach. The design regarding the technology node of 130 nm took 341 min for the non-interactive and 20 min for the interactive optimization process, whereas the design in the 65 nm took 537 min for the non-interactive and 454 min for the interactive approach.
dc.identifier.citationDE LIMA MORETO, R. A.; MARIANO A.; THOMAZ, C. E.; GIMENEZ, S. Optimization of a low noise amplifier with two technology nodes using an interactive evolutionary approach. Analog Integrated Circuits and Signal Processing, v. 106, n. 1, p. 307-319, Jan. 2021.
dc.relation.ispartofAnalog Integrated Circuits and Signal Processing
dc.rightsAcesso Restrito
dc.subject.otherlanguageDesign of robust analog CMOS ICs
dc.subject.otherlanguageElectronic design automation (EDA)
dc.subject.otherlanguageInteractive genetic algorithm
dc.subject.otherlanguageLow noise amplifier (LNA)
dc.titleOptimization of a low noise amplifier with two technology nodes using an interactive evolutionary approach
fei.scopus.subjectEvolutionary approach
fei.scopus.subjectInteractive approach
fei.scopus.subjectInteractive optimization
fei.scopus.subjectKnowledge and experience
fei.scopus.subjectMonte carlo analysis
fei.scopus.subjectOptimization tools
fei.scopus.subjectRadio frequency circuit
fei.scopus.subjectWireless communications