Flexible Automatic Design of GaN PA Based on Gaussian Process-Assisted Non-Dominated Sorting Genetic Algorithm

This paper introduces a new method for designing power amplifiers (PAs) using a flexible, automatic approach. It employs a Gaussian process-assisted fast non-dominated sorting genetic algorithm (GP-NSGA-II) for both circuit synthesis and layout optimization. Traditional genetic algorithms are comput...

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Published in:	IEEE transactions on computer-aided design of integrated circuits and systems p. 1
Main Authors:	Wang, Weiwei, Sun, Bingjie, Chen, Shichang, Xu, Kuiwen, Cai, Jialin, Raffo, Antonio, Donato, Nicola, Crupi, Giovanni, Wang, Gaofeng
Format:	Journal Article
Language:	English
Published:	IEEE 2025
Subjects:	Bayes methods Convergence Electronic design automation gallium nitride Genetic algorithms Heuristic algorithms Layout layout automation Machine learning algorithms non-dominated sorting genetic algorithm Optimization power amplifier Power amplifiers Power generation Radio frequency
ISSN:	0278-0070, 1937-4151
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Abstract	This paper introduces a new method for designing power amplifiers (PAs) using a flexible, automatic approach. It employs a Gaussian process-assisted fast non-dominated sorting genetic algorithm (GP-NSGA-II) for both circuit synthesis and layout optimization. Traditional genetic algorithms are computationally intensive and have poor convergence, making them difficult to use with three-dimensional full-wave simulators. To address these issues, the authors incorporate Gaussian process regression from Bayesian optimization into NSGA-II, improving convergence and computational efficiency. The method integrates layout simulation and optimization, enabling automatic PA design with direct layout optimization. To demonstrate the method's effectiveness, the authors design a wide-band PA and a tri-band PA using a 10-W gallium-nitride (GaN) high electron mobility transistor (HEMT). The tests show that the prototypes perform well: the wide-band PA has a power-added efficiency (PAE) of over 61% and an output power greater than 41.5 dBm in the 2-3 GHz band, while the tri-band PA achieves PAE values of over 59%, 55%, and 57% in the 0.7-1.1 GHz, 2.3-2.5 GHz, and 3.4-3.5 GHz bands, respectively, with output powers exceeding 41.2 dBm, 40.6 dBm, and 40.6 dBm.
AbstractList	This paper introduces a new method for designing power amplifiers (PAs) using a flexible, automatic approach. It employs a Gaussian process-assisted fast non-dominated sorting genetic algorithm (GP-NSGA-II) for both circuit synthesis and layout optimization. Traditional genetic algorithms are computationally intensive and have poor convergence, making them difficult to use with three-dimensional full-wave simulators. To address these issues, the authors incorporate Gaussian process regression from Bayesian optimization into NSGA-II, improving convergence and computational efficiency. The method integrates layout simulation and optimization, enabling automatic PA design with direct layout optimization. To demonstrate the method's effectiveness, the authors design a wide-band PA and a tri-band PA using a 10-W gallium-nitride (GaN) high electron mobility transistor (HEMT). The tests show that the prototypes perform well: the wide-band PA has a power-added efficiency (PAE) of over 61% and an output power greater than 41.5 dBm in the 2-3 GHz band, while the tri-band PA achieves PAE values of over 59%, 55%, and 57% in the 0.7-1.1 GHz, 2.3-2.5 GHz, and 3.4-3.5 GHz bands, respectively, with output powers exceeding 41.2 dBm, 40.6 dBm, and 40.6 dBm.
Author	Raffo, Antonio Wang, Weiwei Cai, Jialin Crupi, Giovanni Sun, Bingjie Chen, Shichang Wang, Gaofeng Xu, Kuiwen Donato, Nicola
Author_xml	– sequence: 1 givenname: Weiwei orcidid: 0000-0001-8694-6179 surname: Wang fullname: Wang, Weiwei organization: Shaoxing Integrated Circuit Institute, Hangzhou Dianzi University, Zhejiang, China – sequence: 2 givenname: Bingjie surname: Sun fullname: Sun, Bingjie organization: Shaoxing Integrated Circuit Institute, Hangzhou Dianzi University, Zhejiang, China – sequence: 3 givenname: Shichang orcidid: 0000-0003-1628-5429 surname: Chen fullname: Chen, Shichang email: eechensc@hdu.edu.cn organization: Shaoxing Integrated Circuit Institute, Hangzhou Dianzi University, Zhejiang, China – sequence: 4 givenname: Kuiwen surname: Xu fullname: Xu, Kuiwen organization: Shaoxing Integrated Circuit Institute, Hangzhou Dianzi University, Zhejiang, China – sequence: 5 givenname: Jialin surname: Cai fullname: Cai, Jialin organization: Shaoxing Integrated Circuit Institute, Hangzhou Dianzi University, Zhejiang, China – sequence: 6 givenname: Antonio orcidid: 0000-0002-8228-6561 surname: Raffo fullname: Raffo, Antonio organization: Engineering Department, University of Ferrara, Ferrara, Italy – sequence: 7 givenname: Nicola orcidid: 0000-0002-1554-2182 surname: Donato fullname: Donato, Nicola organization: Engineering Department, University of Messina, Messina, Italy – sequence: 8 givenname: Giovanni orcidid: 0000-0002-6666-6812 surname: Crupi fullname: Crupi, Giovanni organization: BIOMORF Department, University of Messina, Messina, Italy – sequence: 9 givenname: Gaofeng orcidid: 0000-0001-8599-7249 surname: Wang fullname: Wang, Gaofeng organization: Shaoxing Integrated Circuit Institute, Hangzhou Dianzi University, Zhejiang, China
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SubjectTerms	Bayes methods Convergence Electronic design automation gallium nitride Genetic algorithms Heuristic algorithms Layout layout automation Machine learning algorithms non-dominated sorting genetic algorithm Optimization power amplifier Power amplifiers Power generation Radio frequency
Title	Flexible Automatic Design of GaN PA Based on Gaussian Process-Assisted Non-Dominated Sorting Genetic Algorithm
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