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Titlebook: Speeding-Up Radio-Frequency Integrated Circuit Sizing with Neural Networks; Jo?o L. C. P. Domingues,Pedro J. C. D. C. Vaz,Rica Book 2023 T

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發(fā)表于 2025-3-21 16:29:30 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Speeding-Up Radio-Frequency Integrated Circuit Sizing with Neural Networks
編輯Jo?o L. C. P. Domingues,Pedro J. C. D. C. Vaz,Rica
視頻videohttp://file.papertrans.cn/875/874071/874071.mp4
概述Describes the developments in the field of machine/deep learning and electronic design automation.Presents the process, Voltage and Temperature Corner Performance Estimator using ANNs.Applies deep lea
叢書名稱SpringerBriefs in Applied Sciences and Technology
圖書封面Titlebook: Speeding-Up Radio-Frequency Integrated Circuit Sizing with Neural Networks;  Jo?o L. C. P. Domingues,Pedro J. C. D. C. Vaz,Rica Book 2023 T
描述.In this book, innovative research using artificial neural networks (ANNs) is conducted to automate the sizing task of RF IC design, which is used in two different steps of the automatic design process. The advances in telecommunications, such as the 5th generation broadband or 5G for short, open doors to advances in areas such as health care, education, resource management, transportation, agriculture and many other areas. Consequently, there is high pressure in today’s market for significant communication rates, extensive bandwidths and ultralow-power consumption. This is where radiofrequency (RF) integrated circuits (ICs) come in hand, playing a crucial role. This demand stresses out the problem which resides in the remarkable difficulty of RF IC design in deep nanometric integration technologies due to their high complexity and stringent performances. Given the economic pressure for high quality yet cheap electronics and challenging time-to-market constraints, there is an urgent need for electronic design automation (EDA) tools to increase the RF designers’ productivity and improve the quality of resulting ICs. In the last years, the automatic sizing of RF IC blocks in deep nan
出版日期Book 2023
關(guān)鍵詞machine learning; deep learning; neural networks; computational intelligence; analog circuits
版次1
doihttps://doi.org/10.1007/978-3-031-25099-6
isbn_softcover978-3-031-25098-9
isbn_ebook978-3-031-25099-6Series ISSN 2191-530X Series E-ISSN 2191-5318
issn_series 2191-530X
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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2191-530X urgent need for electronic design automation (EDA) tools to increase the RF designers’ productivity and improve the quality of resulting ICs. In the last years, the automatic sizing of RF IC blocks in deep nan978-3-031-25098-9978-3-031-25099-6Series ISSN 2191-530X Series E-ISSN 2191-5318
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Jo?o L. C. P. Domingues,Pedro J. C. D. C. Vaz,António P. L. Gusm?o,Nuno C. G. Horta,Nuno C. C. Loure
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Convergence Classifier and Frequency Guess Predictor Based on ANNs,ircuit topologies pose unprecedented challenges to the application of these tools. Some of the problems lie in when to set a timeout on the VCO convergence attempts (whose simulator may attempt to converge forever), or, as the . oscillation frequencies have a strong correlation with the steady-state
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