Integrated DC - DC converter design methodology for design cycle speed up

Gogolou, Vasiliki; Kozalakis, Konstantinos; Noulis, Thomas; Siskos, S.

doi:10.1016/j.vlsi.2022.09.003

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…As aforementioned, the aim to design a new converter circuit is to improve the performance while having less complexity and less component counts. Although there are lots of research, modelling and prototypes have been made to improve the efficiency of DC-DC converter as published in [17][18][19][20], even though the published designs improve greatly, but the circuit complexity is high and requires many additional circuits to trigger the main DC-DC converter circuits.…”

Section: Discussionmentioning

confidence: 99%

Development of Converter for Linear Wave Energy Stabilizer by Replacing the Inductor Type from the Existing Modified SEPIC Circuit

Putra Ahmad Khalifa Mohamed Salleh,

Taib Ibrahim,

Nursyarizal Mohd Nor

et al. 2023

ARAM

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Wave is one of the renewable resources that can replace non-renewable resources to reduce the Green House Gas (GHG) emission. Other than wave geographical location and condition, the electronics technical design and components used also plays an important role to maximize the capture of wave energy. DC-DC converter topologies have been widely used in power conversions. However, wave energy has no uniform pattern making the electricity energy generated by it is unclean. Furthermore, there is no small-scale converter specifically for pico-scale linear generator which require high static gain to capture wave energy. With the aim to improve the performance of energy conversion, this paper proposed a new converter design based on the existing topology which is modified SEPIC. The simulation for the two converters is performed to identify the percentage of performance improvement by using MATLAB Simulink software before fabricating a prototype. The comparison of experimental and simulation data for the converters is conducted and analysed in this paper to validate its percentage of error. From the results obtained, it is proven that the modification made from the existing topology improved significantly to the overall performance of the converter.

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Section: Discussionmentioning

confidence: 99%

Development of Converter for Linear Wave Energy Stabilizer by Replacing the Inductor Type from the Existing Modified SEPIC Circuit

Putra Ahmad Khalifa Mohamed Salleh,

Taib Ibrahim,

Nursyarizal Mohd Nor

et al. 2023

ARAM

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“…A great number of recent studies have been focused on analog design automation and design cycle speedup. Some of them use modeling and simulation techniques and approaches that can be performed only inside the standard simulation suite [5,6]. In the majority, the design automation and acceleration methodologies rely on heuristics, metaheuristics, and machine learning techniques optimization with the purpose of generating new, alternative yet accurate, efficient, and fast design tools and methodologies outside the industry-standard simulation suite.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid GA/ML-Based End-to-End Automated Methodology for Design Acceleration of Wireless Communications CMOS LNAs

et al. 2023

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A new methodology for the RF/mmWave analog design process, automation and acceleration, is presented in this work. The proposed framework was implemented so as to accelerate the design cycle of analog/RF circuits by creating a dataset in a fully automated manner and training a combination of machine learning models for the optimal design parameters’ prediction. machine learning polynomial regression was adopted to accelerate the design process, predicting the optimal design parameters’ values while genetic algorithm optimization was exploited for the dataset creation automation. To evaluate the efficiency of the proposed methodology, the framework was implemented for the design of a common source Low-Noise-Amplifier, using a 65 nm CMOS process node. The proposed methodology successfully tackles the design cycle speed-up, automation, and acceleration, utilizing machine learning prediction for the design parameters and genetic algorithm for the dataset creation automation instead of the classical, simulation-based, standard design methodology. The provided experimental results have shown the effectiveness of the proposed hybrid approach, creating very precise RF matching networks for LNA designs and achieving >99.9% wave transmission efficiency while reaching >99% accuracy on the parameters’ prediction task.

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