Low drop-out (LDO) voltage regulators are widely used in many portable applications, such as cell phones, laptops, and earplugs. However, the design and simulation of the circuit takes a long time. Hence, the behavioral model for an LDO regulator is greatly needed. In this paper, the hybrid modeling method has been illustrated for generating a behavioral model for an LDO regulator circuit. The hybrid modeling method integrates a table-based modeling method and a circuit simplification method for generating an LDO linear regulator behavioral model. Based on research of the automation topologies and algorithms a behavioral model generation tool for an LDO linear regulator, LDOCad, is also described, including the software architecture and different modules of LDOCad. This tool provides the modeler, or circuit designer, with an approach to quickly and automatically generate a behavioral model for an LDO linear regulator. When the netlist processing algorithm, the table generation algorithm, and the modeled node and topology device extraction algorithm are executed, the hybrid model can be generated through the model topology formulator. The algorithms are generic and robust. The models tested demonstrate an accurate match with the performance of the original circuits and achieve from a 5 to 19 times speed improvement in simulations.
| Published in | International Journal of Systems Engineering (Volume 5, Issue 1) |
| DOI | 10.11648/j.ijse.20210501.14 |
| Page(s) | 25-33 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2021. Published by Science Publishing Group |
LDO Regulator, Modeling, Automation
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APA Style
Yong Gao, Chuanfeng Wei. (2021). LDOCad – A Behavioral Model Generation Tool for an LDO Linear Regulator. International Journal of Systems Engineering, 5(1), 25-33. https://doi.org/10.11648/j.ijse.20210501.14
ACS Style
Yong Gao; Chuanfeng Wei. LDOCad – A Behavioral Model Generation Tool for an LDO Linear Regulator. Int. J. Syst. Eng. 2021, 5(1), 25-33. doi: 10.11648/j.ijse.20210501.14
AMA Style
Yong Gao, Chuanfeng Wei. LDOCad – A Behavioral Model Generation Tool for an LDO Linear Regulator. Int J Syst Eng. 2021;5(1):25-33. doi: 10.11648/j.ijse.20210501.14
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Download@article{10.11648/j.ijse.20210501.14,
author = {Yong Gao and Chuanfeng Wei},
title = {LDOCad – A Behavioral Model Generation Tool for an LDO Linear Regulator},
journal = {International Journal of Systems Engineering},
volume = {5},
number = {1},
pages = {25-33},
doi = {10.11648/j.ijse.20210501.14},
url = {https://doi.org/10.11648/j.ijse.20210501.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijse.20210501.14},
abstract = {Low drop-out (LDO) voltage regulators are widely used in many portable applications, such as cell phones, laptops, and earplugs. However, the design and simulation of the circuit takes a long time. Hence, the behavioral model for an LDO regulator is greatly needed. In this paper, the hybrid modeling method has been illustrated for generating a behavioral model for an LDO regulator circuit. The hybrid modeling method integrates a table-based modeling method and a circuit simplification method for generating an LDO linear regulator behavioral model. Based on research of the automation topologies and algorithms a behavioral model generation tool for an LDO linear regulator, LDOCad, is also described, including the software architecture and different modules of LDOCad. This tool provides the modeler, or circuit designer, with an approach to quickly and automatically generate a behavioral model for an LDO linear regulator. When the netlist processing algorithm, the table generation algorithm, and the modeled node and topology device extraction algorithm are executed, the hybrid model can be generated through the model topology formulator. The algorithms are generic and robust. The models tested demonstrate an accurate match with the performance of the original circuits and achieve from a 5 to 19 times speed improvement in simulations.},
year = {2021}
}
TY - JOUR T1 - LDOCad – A Behavioral Model Generation Tool for an LDO Linear Regulator AU - Yong Gao AU - Chuanfeng Wei Y1 - 2021/05/15 PY - 2021 N1 - https://doi.org/10.11648/j.ijse.20210501.14 DO - 10.11648/j.ijse.20210501.14 T2 - International Journal of Systems Engineering JF - International Journal of Systems Engineering JO - International Journal of Systems Engineering SP - 25 EP - 33 PB - Science Publishing Group SN - 2640-4230 UR - https://doi.org/10.11648/j.ijse.20210501.14 AB - Low drop-out (LDO) voltage regulators are widely used in many portable applications, such as cell phones, laptops, and earplugs. However, the design and simulation of the circuit takes a long time. Hence, the behavioral model for an LDO regulator is greatly needed. In this paper, the hybrid modeling method has been illustrated for generating a behavioral model for an LDO regulator circuit. The hybrid modeling method integrates a table-based modeling method and a circuit simplification method for generating an LDO linear regulator behavioral model. Based on research of the automation topologies and algorithms a behavioral model generation tool for an LDO linear regulator, LDOCad, is also described, including the software architecture and different modules of LDOCad. This tool provides the modeler, or circuit designer, with an approach to quickly and automatically generate a behavioral model for an LDO linear regulator. When the netlist processing algorithm, the table generation algorithm, and the modeled node and topology device extraction algorithm are executed, the hybrid model can be generated through the model topology formulator. The algorithms are generic and robust. The models tested demonstrate an accurate match with the performance of the original circuits and achieve from a 5 to 19 times speed improvement in simulations. VL - 5 IS - 1 ER -
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