Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
An Analytical Electron Mobility Model based on Particle Swarm Computation for Siliconbased Devices

Authors: F. Djeffal, N. Lakhdar, T. Bendib

Abstract:

The study of the transport coefficients in electronic devices is currently carried out by analytical and empirical models. This study requires several simplifying assumptions, generally necessary to lead to analytical expressions in order to study the different characteristics of the electronic silicon-based devices. Further progress in the development, design and optimization of Silicon-based devices necessarily requires new theory and modeling tools. In our study, we use the PSO (Particle Swarm Optimization) technique as a computational tool to develop analytical approaches in order to study the transport phenomenon of the electron in crystalline silicon as function of temperature and doping concentration. Good agreement between our results and measured data has been found. The optimized analytical models can also be incorporated into the circuits simulators to study Si-based devices without impact on the computational time and data storage.

Keywords: Particle Swarm, electron mobility, Si-based devices, Optimization.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333606

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1171

References:


[1] M.M. Chowdhury, V.P Trivedi, JG. Fossum and L. Mathew, "Carrier mobility/transport in undoped-UTB DG FinFETs," IEEE Trans Electron Devices, vol. 54, pp. 1125-1132, 2007.
[2] S.M. Sze, "Physics of semiconductors devices," second ed, J.Wiley & Sons, New York, 1981.
[3] V. W. L. Chin, R. J. Egan, and T. L. Tansley, "Carrier concentration and compensation ratio dependence of electron drift mobility in InAs1−xSbx," J. of Appl Phys, vol. 72, 1992.
[4] M. Clerc and J. Kennedy, "The particle swarm-explosion, stability, and convergence in a multidimensional complex space," IEEE Trans. Evol. Comput, vol. 6, pp. 58-73, 2002.
[5] F. Djeffal, S. Guessasma, A. Benhaya and M. Chahdi, "An analytical approach based on neural computation to estimate the lifetime of deep submicron MOSFETs," Semicond. Sci. Technol, vol. 20, pp. 158-164, 2005.
[6] F. Djeffal, M. Chahdi, A. Benhaya and M.L. Hafiane, "An approach based on neural computation to simulate the nanoscale CMOS circuits: Application to the simulation of CMOS inverter," Solid State electronics journal, vol. 51, pp. 26-34, 2007
[7] G. Ciuprina, D. Ioan, and I. Munteanu, "Use of intelligentparticle swarm optimization in electromagnetic," IEEE Trans. Magn, vol. 38, pp. 1037- 1040, 2002
[8] D.W. Boeringer and D. H.Werner, "Particle swarm optimization versus genetic algorithms for phased array synthesis," IEEE Trans. Antennas Propag, vol. 52, pp. 771-779, 2004.
[9] K.W. Chau, "A split-step particle swarm optimization algorithm in river stage forecasting," Hydrol, J, vol. 346, pp. 131-135, 2007.
[10] D.M. Caughey and R.E. Thomas, "Carrier mobilities in silicon empirically related to doping and field," Proc. IEEE, vol. 55, pp. 2192- 2193, 1967.
[11] D.C. Farahmand, J.R. Garetto, E. Belotti, K.F. Brennan and M. Goano, "Monte Carlo simulation of electron transport in the III-N wurtzite phase materials system: binaries andternaries," IEEE Electron. Devices, vol. 48, pp. 535-542, 2001.