Gaurav Bhargava, Hemant Kumari, Valeria Vadalà, Shubhankar Majumdar, Giovanni Crupi
{"title":"Physics-informed neural network assisted automated design of power amplifier by user defined specifications","authors":"Gaurav Bhargava, Hemant Kumari, Valeria Vadalà, Shubhankar Majumdar, Giovanni Crupi","doi":"10.1002/jnm.3246","DOIUrl":null,"url":null,"abstract":"<p>This article presents a model that can automatically produce a power amplifier's (PA) design parameters, that is, transmission lines (TLs) dimension, from a dataset of user-specified design goals like gain, efficiency, linearity, and scattering (<i>S</i>-) parameters. Based on the applied boundary conditions, a synthetic dataset is generated with the best range of design parameters (<i>W</i> and <i>L</i>). This dataset is utilized for training the physics-informed neural network (PINN) model with user-specified design goals as input and design parameters as target to produce the optimum value of <i>W</i> and <i>L</i> as the resultant output. Furthermore, utilizing the obtained dimensions, design, simulation, fabrication, and measurement of a PA are performed to validate our proposed model. The results of large signal measurements of PA are drain efficiency (DE) of 26.9%, power added efficiency (PAE) of 24.7%, output power (<i>P</i><sub>out</sub>) of 30.98 dBm at an input power <span></span><math>\n <semantics>\n <mrow>\n <mfenced>\n <msub>\n <mi>P</mi>\n <mtext>in</mtext>\n </msub>\n </mfenced>\n </mrow>\n <annotation>$$ \\left({P}_{in}\\right) $$</annotation>\n </semantics></math> of 19 dBm, and gain of 12.41 dB at an operating frequency of 1.625 GHz. It has been observed that the design parameters produced by the model have a significant agreement with the validated output. Also, the statistical error analysis is done by calculating the error metrics between the validated output and the actual output of the PA design.</p>","PeriodicalId":50300,"journal":{"name":"International Journal of Numerical Modelling-Electronic Networks Devices and Fields","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Numerical Modelling-Electronic Networks Devices and Fields","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jnm.3246","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
This article presents a model that can automatically produce a power amplifier's (PA) design parameters, that is, transmission lines (TLs) dimension, from a dataset of user-specified design goals like gain, efficiency, linearity, and scattering (S-) parameters. Based on the applied boundary conditions, a synthetic dataset is generated with the best range of design parameters (W and L). This dataset is utilized for training the physics-informed neural network (PINN) model with user-specified design goals as input and design parameters as target to produce the optimum value of W and L as the resultant output. Furthermore, utilizing the obtained dimensions, design, simulation, fabrication, and measurement of a PA are performed to validate our proposed model. The results of large signal measurements of PA are drain efficiency (DE) of 26.9%, power added efficiency (PAE) of 24.7%, output power (Pout) of 30.98 dBm at an input power of 19 dBm, and gain of 12.41 dB at an operating frequency of 1.625 GHz. It has been observed that the design parameters produced by the model have a significant agreement with the validated output. Also, the statistical error analysis is done by calculating the error metrics between the validated output and the actual output of the PA design.
期刊介绍:
Prediction through modelling forms the basis of engineering design. The computational power at the fingertips of the professional engineer is increasing enormously and techniques for computer simulation are changing rapidly. Engineers need models which relate to their design area and which are adaptable to new design concepts. They also need efficient and friendly ways of presenting, viewing and transmitting the data associated with their models.
The International Journal of Numerical Modelling: Electronic Networks, Devices and Fields provides a communication vehicle for numerical modelling methods and data preparation methods associated with electrical and electronic circuits and fields. It concentrates on numerical modelling rather than abstract numerical mathematics.
Contributions on numerical modelling will cover the entire subject of electrical and electronic engineering. They will range from electrical distribution networks to integrated circuits on VLSI design, and from static electric and magnetic fields through microwaves to optical design. They will also include the use of electrical networks as a modelling medium.