Parameter Extraction of PSP MOSFET Model Using Particle Swarm Optimization - SoC Approach

  • Amit RathodEmail author
  • Rajesh Thakker
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 892)


System on Chip (SoC) architecture offers performance acceleration by offloading compute-intensive functions in FPGA logic together with application specific instruction set processor (ASIP). In this paper, we report a novel approach of SoC implementation for MOSFET parameter extraction. Xilinx’s Zynq 7000 SoC supplied with AVNET ZedboardTM is used in this work. Extraction of PSP MOSFET Model parameters for 65 nm technology devices has been carried out using Particle Swarm Optimization (PSO) algorithm. PSP MOSFET Model code is executed by ARM Cortex A9 processor available on Zynq 7000 SoC. Parameter extraction of PSP MOSFET model is carried out for two different cases of implementations of PSO algorithm using: (1) ARM Cortex A9 and (2) FPGA. In both cases, excellent agreement between measured data of 65 nm technology MOSFET devices and PSP MOSFET model is observed. We have measured time taken by PSO algorithm in both cases for its execution. Implementation of PSO algorithm using FPGA is found to be 3.71 times faster on average compared to that on ARM Cortex A9. In both cases, the RMS error between measured and PSP MOSFET model is found to be less than 10%.


System on chip MOSFET parameter extraction Particle swarm optimization PSP MOSFET model Zynq 7000soc 



The authors are thankful to Gujarat Council for Science and Technology (GUJCOST) for providing financial support to this project under Minor Research Project [Grant no: GUJCOST/MRP/15-16/1091].


  1. 1.
    Balodi, D., Saha, C., Govidacharyulu, P.A.: Effect of parameter optimization effort over MOSFET models’ performances in analog circuits’ simulation. In: International Conference on Devices, Circuits and Systems (ICDCS), pp. 389–394 (2012)Google Scholar
  2. 2.
    Zhou, Q., Yao, W., Wu, W., Li, X., Zhu, Z., Gildenblat, G.: Parameter extraction for the PSP MOSFET model by the combination of genetic and Levenberg-Marquardt algorithms. In: IEEE International Conference on Microelectronic Test Structures, pp. 137–142 (2009)Google Scholar
  3. 3.
    Thakker, R.A., Patil, M.B., Anil, K.G.: Parameter extraction for PSP MOSFET model using hierarchical particle swarm optimization. sci. Dierct J. Eng. Appl. Artif. Intell. 2, 317–328 (2009)CrossRefGoogle Scholar
  4. 4.
    Arabas, J., Bartnik, L., Szostak, S., Tomaszewski, D.: Global extraction of MOSFET parameters using the EKV model: some properties of the underlying optimization task. In: International Conference Mixed Design of Integrated Circuits Systems, pp. 67–72 (2009)Google Scholar
  5. 5.
    Le, D.-H., Pham, C.-K., Nguyen, T.T.T., Bui, T.: Parameter extraction and optimization using Levenberg-Marquardt algorithm. In: Fourth International Conference on Communications and Electronics (ICCE), pp. 434–437 (2012)Google Scholar
  6. 6.
    Wu, T., et al.: Model-adaptable MOSFET parameter extraction with a hybrid genetic algorithm. In: International Conference on Solid-State and Integrated Circuit Technology Proceedings, pp. 1299–1302 (2006)Google Scholar
  7. 7.
    Chopde, A.M., Khandelwal, S., Thakker, R.A., Patil, M.B., Anil, K.G.: Parameter extraction for mos model 11 using Particle Swarm Optimization. In: International Workshop on Physics of Semiconductor Devices, pp. 253–256 (2007)Google Scholar
  8. 8.
    Makryniotis, T., Dasygenis, M.: Implementation of a motion estimation hardware accelerator on Zynq SoC. In: 6th International Conference on Modern Circuits and Systems Technologies (MOCAST), pp. 1–4 (2017)Google Scholar
  9. 9.
    Domínguez, A., Carballo, P.P., Núñez, A.: Programmable SoC platform for deep packet inspection using enhanced Boyer-Moore algorithm. In: International Symposium on Reconfigurable Communication-Centric Systems-on-Chip (ReCoSoC), pp. 1–8 (2017)Google Scholar
  10. 10.
    Chang, A.X.M., Culurciello, E.: Hardware accelerators for recurrent neural. In: IEEE International Symposium on Circuit and Systems (ISCAS), pp. 1–4 (2017)Google Scholar
  11. 11.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  12. 12.
    Gildenblat, G., et al.: PSP: an advanced surface-potential-based MOSFET model for circuit simulation. IEEE Trans. Electron Devices 53, 1979–1993 (2009)CrossRefGoogle Scholar
  13. 13.
    Crockett, L.H., Elliot, R., Enderwitz, M.: The Zynq Book: embedded Processing with the arm Cortex-A9 on the Xilinx Zynq-7000 All Programmable SoC. Strathclyde Academic Media (2016)Google Scholar
  14. 14.
    Powell, A., Silage, D.: Statistical performance of the ARM Cortex A9 accelerator coherency port in the xilinx zynq SoC for real-time applications. In: International Conference on ReConFigurable Computing and FPGAs (ReConFig), pp. 1–6 (2015)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Government Engineering CollegeBhavnagarIndia
  2. 2.Vishwakarma Government Engineering CollegeChandkhedaIndia

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