Model development for lattice properties of gallium arsenide using parallel genetic algorithm

In the last few years, evolutionary computing (EC) approaches have been successfully used for many real world optimization applications in scientific and engineering areas. One of these areas is computational nanoscience. Semi-empirical models with physics-based symmetries and properties can be developed by using EC to reproduce theoretically the experimental data. One of these semi-empirical models is the Valence Force Field (VFF) method for lattice properties. An accurate understanding of lattice properties provides a stepping stone for the investigation of thermal phenomena and has large impact in thermoelectricity and nano-scale electronic device design. The VFF method allows for the calculation of static properties like the elastic constants as well as dynamic properties like the sound velocity and the phonon dispersion. In this paper a parallel genetic algorithm (PGA) is employed to develop the optimal VFF model parameters for gallium arsenide (GaAs). This methodology can also be used for other semiconductors. The achieved results agree qualitatively and quantitatively with the experimental data.

[1]  Martina Gorges-Schleuter,et al.  Explicit Parallelism of Genetic Algorithms through Population Structures , 1990, PPSN.

[2]  Marco Tomassini,et al.  The Parallel Genetic Cellular Automata: Application to Global Function Optimization , 1993 .

[3]  Chrisila C. Pettey,et al.  A Theoretical Investigation of a Parallel Genetic Algorithm , 1989, ICGA.

[4]  Majid Nili Ahmadabadi,et al.  SOPC-Based Parallel Genetic Algorithm , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[5]  J. Pople,et al.  A general valence force field for diamond , 1962, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[6]  Majid Nili Ahmadabadi,et al.  A Representation for Genetic-Algorithm-Based Multiprocessor Task Scheduling , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[7]  Kane Eo,et al.  Phonon spectra of diamond and zinc-blende semiconductors. , 1985 .

[8]  D Strauch,et al.  Phonon dispersion in GaAs , 1990 .

[9]  Sui,et al.  Effect of strain on phonons in Si, Ge, and Si/Ge heterostructures. , 1993, Physical review. B, Condensed matter.

[10]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[11]  P. N. Keating,et al.  Effect of Invariance Requirements on the Elastic Strain Energy of Crystals with Application to the Diamond Structure , 1966 .

[12]  P. Y. Yu,et al.  Fundamentals of Semiconductors , 1995 .

[13]  Trevor York,et al.  A distributed genetic algorithm environment for UNIX workstation clusters , 1997 .

[14]  Gerhard Klimeck,et al.  Strain-induced, off-diagonal, same-atom parameters in empirical tight-binding theory suitable for [110] uniaxial strain applied to a silicon parametrization , 2010 .

[15]  Gerhard Klimeck,et al.  An atomistic model for the simulation of acoustic phonons, strain distribution, and Grüneisen coefficients in zinc-blende semiconductors , 2003 .

[16]  Erick Cantú-Paz,et al.  A Survey of Parallel Genetic Algorithms , 2000 .

[17]  Gerhard Klimeck,et al.  Modified valence force field approach for phonon dispersion: from zinc-blende bulk to nanowires , 2010, 1009.6188.

[18]  Adrian Stoica,et al.  Si Tight-Binding Parameters from Genetic Algorithm Fitting , 2000 .

[19]  Richard M. Martin,et al.  Elastic Properties of ZnS Structure Semiconductors , 1970 .

[20]  Kane.  Phonon spectra of diamond and zinc-blende semiconductors. , 1985, Physical review. B, Condensed matter.

[21]  A. A. Maradudin,et al.  Theory of lattice dynamics in the harmonic approximation , 1971 .