Document Type : Original Research Paper

Authors

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

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

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

Main Subjects

[1] M. Shakibmehr, M. Lotfizad, "Design of an S-band ultra-low-noise amplifier with frequency band switching capability," Journal of Electrical and Computer Engineering Innovations, 5(1): 13–18, 2017.

[2] P. Amiri, M. Kohestani, M. Seifouri, "THD analysis in closed-loop analog PWM class-D amplifiers," Journal of Electrical and Computer Engineering Innovations, 2(1): 1–5, 2014.

[3] B. Shojaei Tabatabaei, P. Amiri, "UWB mixer improvement with   regulated   voltage   source, "   Journal   of   Electrical   and Engineering Innovations (JECEI), 2(2): 93–99, 2014.

[4] M. Basravi, Z. H. Firouzeh, M. Maddahali, "Design of a single-layer circuit analog absorber using double-circular-loop array via the equivalent circuit model," Journal of Electrical and Computer Engineering Innovations, 5(2): 171–178, 2017.

[5] B. Liu, G. Gielen, F. V. Fernández, Automated Design of Analog and High-frequency Circuits, A Computational Intelligence Approach, Springer, Berlin, Heidelberg, 2014.

[6] S. Roostaee, H. R. Ghaffary, "Diagnosis of heart disease based on meta heuristic algorithms and clustering methods," Journal of Electrical and Computer Engineering Innovations, 2(2): 93–99, 2014.

[7] M. Ranjkesh, E. FallahChoolabi, M. Pourjafari, "Optimum design of a SRM using FEM and PSO," Journal of Electrical and Computer Engineering Innovations, 2(1): 29–35, 2014.

[8] R. Omidvar1, H. Parvin, A. Eskandari, "A clustering approach by SSPCO optimization algorithm based on chaotic initial population, " Journal of Electrical and Computer Engineering Innovations, 4(1): 31–38, 2016.

[9] N. S. Shahraki, S. H. Zahiri, "Inclined planes optimization algorithm in optimal architecture of MLP neural networks, " in proc. 3rd IEEE International Conference on Pattern Recognition and Image Analysis (IPRIA): 189-194, 2017.

[10] O. Bozorg-Haddad, M. Solgi, H. A. Loáiciga, Meta-heuristic and evolutionary algorithms for engineering optimization. John Wiley & Sons, 2017.

[11] B. Liu, Y. Wang, Z. Yu, L. Liu, M. Li, Z. Wang, J. Lu, F. V. Fernández, "Analog circuit optimization system based on hybrid evolutionary algorithms, " Integration VLSI Journal, 42(2): 137–148, 2009.

[12] M. Fakhfakh, Y. Cooren, A. Sallem, M. Loulou, P. Siarry, "Analog circuit design optimization through the particle swarm optimization technique," Analog Integrated Circuits and Signal Processing, 63(1): 71–82, 2010.

[13] M. Barros, J. Guilherme, N. Horta, "Analog circuits optimization based on evolutionary computation techniques, " Integration, the VLSI Journal, 43(1): 136-155, 2010.

[14] R. A. Vural, T. Yildirim, "Analog circuit sizing via swarm intelligence," AEU - International Journal of Electronics and Communications, 66(9): 732–740, 2012.

[15] B. Bachir, A. Ali, M. Abdellah, "Multi-objective optimization of an operational amplifier by the ant colony optimization algorithm," Electrical and Electronic Engineering, 2(4): 230–235, 2012.

[16] S. Mallick, R. Kar, D. Mandal, S. P. Ghoshal, "Optimal sizing of CMOS analog circuits using gravitational search algorithm with particle swarm optimization, " International Journal of Machine Learning and Cybernetics, 8(1): 309–331, 2017.

[17] M. Dehbashian, M. Maymandi-Nejad, "A new hybrid algorithm for analog ICs 0optimization based on the shrinking circles technique," Integration, the VLSI Journal, 5: 148-166, 2017.

[18] M. Dehbashian, M. Maymandi-Nejad, "Co-AGSA: An efficient self-adaptive approach for constrained optimization of analog IC based on the shrinking circles technique, " Integration, the VLSI Journal, 59: 218-232, 2017.

[19] W. Lyu, P. Xue, F. Yang, C. Yan, Z. Hong, X. Zeng, D. Zhou, "An efficient Bayesian optimization approach for automated optimization of analog circuits, " IEEE Transactions on Circuits and Systems I, 65(6): 1954-1967, 2018.

[20]  S. Dash, D. Joshi, A. Sharma, G. Trivedi, "A hierarchy in mutation of genetic algorithm and its application to multi-objective analog/RF circuit optimization," Analog Integrated Circuits and Signal Processing, 94(1): 27-47, 2018.

[21] S. Dash, D. Joshi, G. Trivedi, "Multi-Objective analog/RF circuit sizing using an improved brain storm optimization algorithm," Memetic Computing: 1-18, 2018.

[22] A. C. Sanabria-Borbón, E. Tlelo-Cuautle, "Sizing analogue integrated circuits by integer encoding and NSGA-II," IETE Technical Review, 4602(March): 1–7, 2017.

[23] A. C. Sanabria-Borbón, E. Tlelo-Cuautle, L. G. de la Fraga, "Optimal sizing of amplifiers by evolutionary algorithms with integer encoding and GM/ID design method, " in proc. NEO 2016, Springer, Cham. 731: 263-279, 2018.

[24] N. S. Shahraki, A. Mohammadi, S. Mohammadi-Esfahrood, S. H. Zahiri, "Improving the performance of analog integrated circuits using multi-objective metaheuristic algorithms, " in proc. 5th IEEE Conference on Knowledge Based Engineering and Innovation (KBEI): 822-826, 2019.

[25] E. Afacan, "Inversion coefficient optimization based analog/RF circuit design automation," Microelectronics Journal, 83: 86-93, 2019.

[26] M. Hasanzadeh-Mofrad, A. Rezvanian, "Learning automata clustering," Journal of Computational Science, 24: 379–388, 2018.

[27] M. Ahangaran, N. Taghizadeh, H. Beigy, "Associative cellular learning automata and its applications," Applied Soft Computing, 53: 1-18, 2017.

[28] B. Damerchilu, M. S. Norouzzadeh, M. R. Meybodi, "Motion estimation using learning automata," Machine Vision and Applications, 27(7): 1047-1061, 2016.

[29] N. Kumar, J. H. Lee, J. J. Rodrigues, "Intelligent mobile video surveillance system as a Bayesian coalition game in vehicular sensor networks: Learning automata approach," IEEE Transactions on Intelligent Transportation Systems, 16(3): 1148-1161, 2015.

[30] A. L. Bazzan, "Aligning individual and collective welfare in complex socio-technical systems by combining metaheuristics and reinforcement learning engineering," Applications of Artificial Intelligence, 79: 23-33, 2019.

[31] M. Rezapoor Mirsaleh, M. R. Meybodi, "Balancing exploration and exploitation in memetic algorithms: A learning automata approach," Computational Intelligence, 34(1): 282-309, 2018.

[32] W. Li, E. Özcan, R. John, "A learning automata based multiobjective hyper-heuristic," IEEE Transactions on Evolutionary Computation, 21(1): 59–73, 2017.

[33]  M. L. Tsetlin, Automaton Theory and Modeling of Biological Systems, 102 of Mathematics in Science and Engineering. Academic Press, New York, 1973.

[34] F. Hourfar, H. J. Bidgoly, B. Moshiri, K. Salahshoor, A. Elkamel, "A reinforcement learning approach for waterflooding optimization in petroleum reservoirs," Engineering Applications of Artificial Intelligence. 77: 98-116, 2019.

[35] H. L. Liao, Q. H. Wu, "Multi-objective optimization by learning automata, "Journal of Global Optimization, 55(2): 459–487, 2013.

[ 36] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, "A fast and elitist multi-objective genetic algorithm: NSGA-II, " IEEE Transactions on Evolutionary Computation, 6(2): 182–197, 2002.

[37] C. A. Coello Coello, G. T. Pulido, M. S. Lechuga, "Handling multiple objectives with particle swarm optimization," IEEE Transactions on Evolutionary computation, 8(3): 256–279, 2004.

[38] A. Mohammadi, M. Mohammadi, S. H. Zahiri, "Design of optimal CMOS ring oscillator using an intelligent optimization tool," Soft Computing, 22(4): 8151-8166, 2018.

[39] J. Kennedy, R. Eberhart, "Particle swarm optimization," in Proc. IEEE International Conference on Neural Networks, 4): 1942–1948, 1995.

[40] M. H. Mozaffari, H. Abdy, S. H. Zahiri, "IPO: An inclined planes system optimization algorithm," Computing and Informatics, 35(1): 222-240, 2016.


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