Enhancing AI-Inspired Analog Circuit Design: Optimizing Component Sizes with the Firefly Algorithm and Binary Firefly Algorithm
This paper explores the use of the Firefly Algorithm (FA) and its binary variant (BFA) in optimizing analog circuit component sizing, specifically as a case study for a two-stage operational amplifier (op-amp) designed with a 65nm CMOS process. Recognizing the limitations of traditional optimizatio...
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| Language: | English |
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European Alliance for Innovation (EAI)
2025-01-01
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| Series: | EAI Endorsed Transactions on Industrial Networks and Intelligent Systems |
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| Online Access: | https://publications.eai.eu/index.php/inis/article/view/7859 |
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| author | Trang Hoang |
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This paper explores the use of the Firefly Algorithm (FA) and its binary variant (BFA) in optimizing analog circuit component sizing, specifically as a case study for a two-stage operational amplifier (op-amp) designed with a 65nm CMOS process. Recognizing the limitations of traditional optimization approaches in handling complex analog design requirements, this study implements both FA and BFA to enhance convergence speed and accuracy within multi-dimensional search spaces. The Python-Spectre framework in this paper
facilitates automatic, iterative simulation and data collection, driving the optimization process. Through extensive benchmarking, the BFA outperformed traditional FA, balancing exploration and exploitation while achieving superior design outcomes across key parameters such as voltage gain, phase margin, and unity-gain bandwidth. Comparative analysis with existing optimization methods, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), underscores the efficiency and accuracy of BFA in optimizing circuit metrics, particularly in power-constrained environments. This study demonstrates the potential of swarm intelligence in advancing automatic analog design and establishes a foundation for future enhancements in analog circuit automation.
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| format | Article |
| id | doaj-art-b172095eb3454b3796377be8a2328819 |
| institution | Kabale University |
| issn | 2410-0218 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Industrial Networks and Intelligent Systems |
| spelling | doaj-art-b172095eb3454b3796377be8a23288192025-01-08T20:50:38ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Industrial Networks and Intelligent Systems2410-02182025-01-0112210.4108/eetinis.v12i2.7859Enhancing AI-Inspired Analog Circuit Design: Optimizing Component Sizes with the Firefly Algorithm and Binary Firefly AlgorithmTrang Hoang0Ho Chi Minh City University of Technology This paper explores the use of the Firefly Algorithm (FA) and its binary variant (BFA) in optimizing analog circuit component sizing, specifically as a case study for a two-stage operational amplifier (op-amp) designed with a 65nm CMOS process. Recognizing the limitations of traditional optimization approaches in handling complex analog design requirements, this study implements both FA and BFA to enhance convergence speed and accuracy within multi-dimensional search spaces. The Python-Spectre framework in this paper facilitates automatic, iterative simulation and data collection, driving the optimization process. Through extensive benchmarking, the BFA outperformed traditional FA, balancing exploration and exploitation while achieving superior design outcomes across key parameters such as voltage gain, phase margin, and unity-gain bandwidth. Comparative analysis with existing optimization methods, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), underscores the efficiency and accuracy of BFA in optimizing circuit metrics, particularly in power-constrained environments. This study demonstrates the potential of swarm intelligence in advancing automatic analog design and establishes a foundation for future enhancements in analog circuit automation. https://publications.eai.eu/index.php/inis/article/view/7859Firefly AlgorithmBinary Firefly Algorithmsimulation-based optimization methodtwo-stage op-amp |
| spellingShingle | Trang Hoang Enhancing AI-Inspired Analog Circuit Design: Optimizing Component Sizes with the Firefly Algorithm and Binary Firefly Algorithm EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Firefly Algorithm Binary Firefly Algorithm simulation-based optimization method two-stage op-amp |
| title | Enhancing AI-Inspired Analog Circuit Design: Optimizing Component Sizes with the Firefly Algorithm and Binary Firefly Algorithm |
| title_full | Enhancing AI-Inspired Analog Circuit Design: Optimizing Component Sizes with the Firefly Algorithm and Binary Firefly Algorithm |
| title_fullStr | Enhancing AI-Inspired Analog Circuit Design: Optimizing Component Sizes with the Firefly Algorithm and Binary Firefly Algorithm |
| title_full_unstemmed | Enhancing AI-Inspired Analog Circuit Design: Optimizing Component Sizes with the Firefly Algorithm and Binary Firefly Algorithm |
| title_short | Enhancing AI-Inspired Analog Circuit Design: Optimizing Component Sizes with the Firefly Algorithm and Binary Firefly Algorithm |
| title_sort | enhancing ai inspired analog circuit design optimizing component sizes with the firefly algorithm and binary firefly algorithm |
| topic | Firefly Algorithm Binary Firefly Algorithm simulation-based optimization method two-stage op-amp |
| url | https://publications.eai.eu/index.php/inis/article/view/7859 |
| work_keys_str_mv | AT tranghoang enhancingaiinspiredanalogcircuitdesignoptimizingcomponentsizeswiththefireflyalgorithmandbinaryfireflyalgorithm |