000			04740nam a2200361Ia 4500
000			05638nam a22003855i 4500
001			978-3-031-25099-6
003			DE-He213
005			20240319120846.0
007			cr nn 008mamaa
008			230320s2023 sz | s |||| 0|eng d
020			_a9783031250996 _9978-3-031-25099-6
082			_a6.31
100			_aDomingues, João L. C. P. _930782
245			_aSpeeding-Up Radio-Frequency Integrated Circuit Sizing with Neural Networks _cby João L. C. P. Domingues, Pedro J. C. D. C. Vaz, António P. L. Gusmão, Nuno C. G. Horta, Nuno C. C. Lourenço, Ricardo M. F. Martins. _h[electronic resource] /
250			_a1st ed. 2023.
260			_aCham _bSpringer International Publishing _c2023
300			_aVIII, 109 p. 70 illus., 59 illus. in color. _bonline resource.
520			_aIn 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 nanometer technologies has moved toward process, voltage and temperature (PVT)-inclusive optimizations to ensure their robustness. Each sizing solution is exhaustively simulated in a set of PVT corners, thus pushing modern workstations' capabilities to their limits. Standard ANNs applications usually exploit the model's capability of describing a complex, harder to describe, relation between input and target data. For that purpose, ANNs are a mechanism to bypass the process of describing the complex underlying relations between data by feeding it a significant number of previously acquired input/output data pairs that the model attempts to copy. Here, and firstly, the ANNs disrupt from the most recent trials of replacing the simulator in the simulation-based sizing with a machine/deep learning model, by proposing two different ANNs, the first classifies the convergence of the circuit for nominal and PVT corners, and the second predicts the oscillating frequencies for each case. The convergence classifier (CCANN) and frequency guess predictor (FGPANN) are seamlessly integrated into the simulation-based sizing loop, accelerating the overall optimization process. Secondly, a PVT regressor that inputs the circuit's sizing and the nominal performances to estimate the PVT corner performances via multiple parallel artificial neural networks is proposed. Two control phases prevent the optimization process from being misled by inaccurate performance estimates. As such, this book details the optimal description of the input/output data relation that should be fulfilled. The developed description is mainly reflected in two of the system's characteristics, the shape of the input data and its incorporation in the sizing optimization loop. An optimal description of these components should be such that the model should produce output data that fulfills the desired relation for the given training data once fully trained. Additionally, the model should be capable of efficiently generalizing the acquired knowledge in newer examples, i.e., never-seen input circuit topologies.
650			_aComputational Science and Engineering. _930783
650			_aMachine learning. _930784
650			_aMachine Learning. _930785
650			_aMathematical Models of Cognitive Processes and Neural Networks. _930786
650			_aMathematics _930787
650			_aNeural networks (Computer science) . _930788
700			_aGusmão, António P. L. _930789
700			_aHorta, Nuno C. G. _930790
700			_aLourenço, Nuno C. C. _930791
700			_aMartins, Ricardo M. F. _930792
700			_aVaz, Pedro J. C. D. C. _930793
856			_uhttps://doi.org/10.1007/978-3-031-25099-6
942			_cEBK _2ddc
999			_c15159 _d15159