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 in | IEEJ transactions on electrical and electronic engineering Vol. 19; no. 10; pp. 1711 - 1719 | 
|---|---|
| Main Authors | , , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        Hoboken, USA
          John Wiley & Sons, Inc
    
        01.10.2024
     Wiley Subscription Services, Inc  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 1931-4973 1931-4981  | 
| DOI | 10.1002/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. | 
    
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| 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 Specifications  | 
    
| Title | Novel GA‐OCEAN Framework for Automatically Designing the Charge‐Pump Circuit | 
    
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