Skip to main content

Advertisement

Log in

Two-objective metaheuristic optimization for floating gate transistor-based CMOS-MEMS inertial sensors

  • Technical Paper
  • Published:
Microsystem Technologies Aims and scope Submit manuscript

Abstract

In this work, a study case is presented in which the design of the layout for a CMOS sensor cell is partially automated by implementing a metaheuristic algorithm to find the best tradeoff between two conflicting objectives (two quantitative opposite and not totally independent yet desired performance or design qualities) among the set of feasible layout and electronic device configurations within a constricted search space. The feasibility of a solution (a particular configuration) and its capability to fulfill every requested objective, is determined by its compliance to the CMOS-MEMS design rules and fabrication process. Any given solution besides showing optimal or very near-to-the-optimal characteristics, must be suitable to be fabricated in the CMOS conventional process which for this case is a \(0.5\,\mu \hbox {m}\), 3-metal 2-poly N-well fabrication, beside this, since monolithic inertial sensors generally contains embedded movable electromechanical parts a surface micromachining must be considered. Simulation data and behavior of the bio-inspired metaheuristic algorithm used during the design process are presented, as well as electromechanical simulation results based the automatic-generated solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

References

  • Abarca-Jimenez GS, Reyes-Barranca MA, Mendoza-Acevedo S (2013) MEMS capacitive sensor using FGMOS. In: 2013 10th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2013, pp 421–426

  • Abarca-Jimnez GS et al (2018) Inertial sensing MEMS device using a floating-gate MOS transistor as transducer by means of modifying the capacitance associated to the floating gate. Microsyst Technol 24(6):2753–2764

    Article  Google Scholar 

  • Jacob BR (2005) CMOS: circuit design, layout, and simulation

  • Bykov IS, Aleksei LP (2017) On distance Gray codes. J Appl Ind Math 11(2):185–192

    Article  MathSciNet  Google Scholar 

  • Censor Y (1977) Pareto optimality in multiobjective problems. Appl Math Optim 4(1):41–59

    Article  MathSciNet  Google Scholar 

  • Coello-Coello CA (2001) A short tutorial on evolutionary multiobjective optimization. In: International Conference on evolutionary multi-criterion optimization. Springer, pp 21–40

  • Coello-Coello CA (2015) Multi-objective evolutionary algorithms in real-world applications: some recent results and current challenges. In: Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences. Springer, pp 3–18

  • Granados-Rojas B et al. (2016) 3-layered capacitive structure design for MEMS inertial sensing. In: 2016 13th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp 1–5

  • Granados-Rojas B et al. (2017) Basic readout circuit applied on FGMOS-based CMOS-MEMS inertial sensing prototypes. In: 2017 14th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp 1–6

  • Granados-Rojas B (2018) Application and Resulting Suitability of a Genetic Algorithm in the Design of FGMOS-based CMOS-MEMS Transducers. In: et al (2018) 15th International Conference on Electrical Engineering. Computing Science and Automatic Control, CCE, p 2018

  • Lopez-Jaimes CC (2015) Many-objective problems: challenges and methods. Springer handbook of computational intelligence. Springer, New York, pp 1033–1046

    Chapter  Google Scholar 

  • Murata Tadahiko, Ishibuchi Hisao, Tanaka Hideo (1996) Genetic algorithms for flowshop scheduling problems. Comput Ind Eng 30(4):1061–1071

    Article  Google Scholar 

  • ON Semiconductor C5X (2020), 0.5 Micron Technology Design Rules 4500099 Rev. X, p 99

  • Razavi B (2002) Design of analog CMOS integrated circuits. Tata McGraw-Hill Education, London

    Google Scholar 

  • Yang Z, Tang K, Yao X (2008) Large scale evolutionary optimization using cooperative coevolution. Inform Sci 178(15):2985–2999

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

Authors would like to thank to the VLSI group members at Cinvestav for the support given to this project and to Conacyt for the scholarship #295930 granted to B. Granados-Rojas.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. Granados-Rojas.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Granados-Rojas, B., Reyes-Barranca, M.A., González-Navarro, Y.E. et al. Two-objective metaheuristic optimization for floating gate transistor-based CMOS-MEMS inertial sensors. Microsyst Technol 27, 2889–2901 (2021). https://doi.org/10.1007/s00542-020-05194-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00542-020-05194-w

Keywords

Navigation