|تعداد مشاهده مقاله||2,478,828|
|تعداد دریافت فایل اصل مقاله||1,746,991|
|Journal of Electrical and Computer Engineering Innovations (JECEI)|
|مقاله 8، دوره 6، شماره 2، مهر 2018، صفحه 199-214 اصل مقاله (1.84 M)|
|نوع مقاله: Original Research Paper|
|شناسه دیجیتال (DOI): 10.22061/jecei.2019.5212.203|
|N. Sayyadi Shahraki1؛ S.H. Zahiri* 2|
|1Department of Electrical and Computer Engineering University of Birjand Birjand, Iran|
|2Department of Electrical and Computer Engineering University of Birjand, Birjand, Iran|
|تاریخ دریافت: 16 تیر 1396، تاریخ بازنگری: 20 دی 1396، تاریخ پذیرش: 26 اردیبهشت 1397|
|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.
©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.
|Low-Area and Low-Power؛ CMOS op-amp؛ Multi-objective optimization؛ Reinforcement learning؛ Total optimality index|
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