Low-Area/Low-Power CMOS Op-Amps Design Based on Total Optimality Index Using Reinforcement Learning Approach

Sayyadi Shahraki, N.; Zahiri, S.H.

doi:10.22061/jecei.2019.5212.203

Document Type : Original Research Paper

Authors

N. Sayyadi Shahraki ¹
S.H. Zahiri ²

¹ Department of Electrical and Computer Engineering University of Birjand Birjand, Iran

² Department of Electrical and Computer Engineering University of Birjand, Birjand, Iran

https://doi.org/10.22061/jecei.2019.5212.203

Abstract

Background and Objectives: Today, the use of methods derived from Reinforcement learning-based approaches, due to their powerful in learning and extracting optimal/desirable solutions to various problems, shows a significant wideness and success. This paper presents the application of reinforcement learning in automatic analog integrated circuit design.
Methods: In this work, the multi-objective approach by learning automata is evaluated for accommodating required functionalities and performance specifications considering optimal minimizing the MOSFETs area and power consumption for two famous CMOS op-amps.
Results: The performance of the circuits is evaluated through HSPICE and the approach is implemented in MATLAB, so a combination of MATLAB and HSPICE is performed. The two-stage and single-ended folded-cascode op-amps are designed in 0.25μm and 0.18μm CMOS technologies, respectively.
According to the simulation results, a power of 560.42 and an area of 72.825 are obtained for a two-stage CMOS op-amp, and also a power of 214.15 and an area of 13.76 are obtained for a single-ended folded-cascode op-amp. In addition, in terms of total optimality index, MOLA for both cases has the best performance between the applied methods, and other research works with values of -25.683 and -34.162 dB, respectively.
Conclusion: The results shown the ability of the proposed method to optimize aforementioned objectives, compared with three multi-objective well-known algorithms.

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Copyrights
©2018 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.
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Keywords

20.1001.1.23223952.2018.6.2.7.4

Main Subjects

Computational Intelligence

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Journal of Electrical and Computer Engineering Innovations (JECEI)

Low-Area/Low-Power CMOS Op-Amps Design Based on Total Optimality Index Using Reinforcement Learning Approach

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References

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Volume 6, Issue 2
July 2018
Pages 199-214

Low-Area/Low-Power CMOS Op-Amps Design Based on Total Optimality Index Using Reinforcement Learning Approach

References

References

Send comment about this article

Volume 6, Issue 2July 2018Pages 199-214

Volume 6, Issue 2
July 2018
Pages 199-214