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.

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.