Novel GA‐OCEAN Framework for Automatically Designing the Charge‐Pump Circuit

In the realm of analog integrated circuit (IC) design, due to the intricate nonlinear relationships between circuit performance metrics and design variables, as well as the complex interdependencies and trade‐offs between various performance criteria, it is difficult to determine the appropriate geo...

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Published inIEEJ transactions on electrical and electronic engineering Vol. 19; no. 10; pp. 1711 - 1719
Main Authors Hoang, Trang, Nguyen, Thang Quoc, Phan‐Tran‐Minh, Hieu
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 01.10.2024
Wiley Subscription Services, Inc
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ISSN1931-4973
1931-4981
DOI10.1002/tee.24129

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Abstract In the realm of analog integrated circuit (IC) design, due to the intricate nonlinear relationships between circuit performance metrics and design variables, as well as the complex interdependencies and trade‐offs between various performance criteria, it is difficult to determine the appropriate geometric width and length of the circuit components to meet all performance specifications. This difficulty makes the task of designing analog IC challenging. To address the challenge of analog IC design, this paper introduces a machine learning methodology—specifically employing the genetic algorithm (GA)—to automate the selection of circuit components' geometrical parameters, aiding the work of analog circuit designers in determining the appropriate dimensions for transistors within the charge‐pump circuit. The GA, implemented in Python and integrated with a script program written in the OCEAN language, synergistically collaborates in the GA‐OCEAN framework. The result, which ensures compliance with all specifications with remarkably low error margins, ranging as low as 0.03% and as high as 1.24%, highlights the proposed GA‐OCEAN framework's remarkable capacity to determine optimal dimensions for components inside the charge‐pump circuit. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
AbstractList In the realm of analog integrated circuit (IC) design, due to the intricate nonlinear relationships between circuit performance metrics and design variables, as well as the complex interdependencies and trade‐offs between various performance criteria, it is difficult to determine the appropriate geometric width and length of the circuit components to meet all performance specifications. This difficulty makes the task of designing analog IC challenging. To address the challenge of analog IC design, this paper introduces a machine learning methodology—specifically employing the genetic algorithm (GA)—to automate the selection of circuit components' geometrical parameters, aiding the work of analog circuit designers in determining the appropriate dimensions for transistors within the charge‐pump circuit. The GA, implemented in Python and integrated with a script program written in the OCEAN language, synergistically collaborates in the GA‐OCEAN framework. The result, which ensures compliance with all specifications with remarkably low error margins, ranging as low as 0.03% and as high as 1.24%, highlights the proposed GA‐OCEAN framework's remarkable capacity to determine optimal dimensions for components inside the charge‐pump circuit. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
Author Phan‐Tran‐Minh, Hieu
Nguyen, Thang Quoc
Hoang, Trang
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SubjectTerms analog circuit sizing
Analog circuits
charge‐pump (CP)
circuit design automation
circuit optimization
Complex variables
Design
genetic algorithm (GA)
Genetic algorithms
Integrated circuits
Machine learning
Performance measurement
phase‐locked loop (PLL)
Python
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Title Novel GA‐OCEAN Framework for Automatically Designing the Charge‐Pump Circuit
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