An Analytical Electron Mobility Model based on Particle Swarm Computation for Siliconbased Devices
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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:56411", author = "F. Djeffal and N. Lakhdar and T. Bendib", title = "An Analytical Electron Mobility Model based on Particle Swarm Computation for Siliconbased Devices", 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.", volume = "3", number = "9", pages = "1696-4", }