Modeling of Semiconductors Refractive Indices Using Hybrid Chemometric Model

Modeling of Semiconductors Refractive Indices Using Hybrid Chemometric Model

Luqman E. Oloore Taoreed O. Owolabi  Sola Fayose  Muideen Adegoke  Kabiru O. Akande  Sunday O. Olatunji 

Department of Physics and Engineering Physics, Obafemi Awolowo University, Ile-Ife A234, Nigeria

Physics and Electronics Department, Adekunle Ajasin University, Akungba Akoko 342111, Nigeria

Physics Department, King Fahd University of Petroleum and Minerals, Dhahran 34464, Saudi Arabia

Department of System Engineering, King Fahd University of Petroleum and Minerals, Dhahran 34464, Saudi Arabia

Institute for Digital Communications, School of Engineering, University of Edinburgh, Edinburgh, Postal code EH8 9AB, United Kingdom

Computer Science Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam 31433, Saudi Arabia

Corresponding Author Email: 
owolabi@kfupm.edu.sa
Page: 
95-103
|
DOI: 
https://doi.org/10.18280/mmc_a.910301
Received: 
12 August 2018
|
Accepted: 
20 September 2018
|
Published: 
30 September 2018
| Citation

OPEN ACCESS

Abstract: 

A support vector regression (SVR)-based model and its hybrid (HSVR), both optimized with gravitational search algorithm (GSA), for accurate estimation of refractive indices of semiconductors using their energy gaps as descriptors are presented. The proposed GSA-HSVR model demonstrates a better predictive and generalization ability than ordinary GSA-SVR model. The performances of the proposed models are compared with the existing Moss and Ravindra’s models and a better agreement with the experimental values were observed coupled with lowest mean absolute error of GSA-HSVR model. Considerable high coefficient of correlation and very small root mean square error also characterize GSA-HSVR model. The proposed GSA-HSVR model proves its identity and effectiveness compared to existing predictive models, in terms of accuracy, using simply accessible descriptor. It also reduces the estimation challenges accompanying determination of refractive indices of semiconductors.

Keywords: 

support vector regression, gravitational search algorithm, energy gaps, refractive indices and hybrid intelligent

1. Introduction
2. Theoretical Background
3. Development of GSA-SVR and GSA-HSVR Models
4. Results and Discussion
5. Conclusions
Acknowledgment

The contribution of Akande O Kabiru is acknowledged.

  References

[1] Tripathy SK. (2015). Refractive indices of semiconductors from energy gaps. Opt. Mater. (Amst). 46: 240–246. http://dx.doi.org/10.1016/j.optmat.2015.04.026

[2] Paskov PP. (1997). Refractive indices of InSb, InAs, GaSb, InAs[sub x]Sb[sub 1−x], and In[sub 1−x]Ga[sub x]Sb: Effects of free carriers. J. Appl. Phys 81(4): 1890.

[3] Rappl PHO, McCann PJ. (2003). Development of a novel epitaxial-layer segmentation method for optoelectronic device fabrication. IEEE Photonics Technol. Lett 15(3): 374–376.

[4] Reddy RR. et al. (2008). Correlation between optical electronegativity and refractive index of ternary chalcopyrites, semiconductors, insulators, oxides and alkali halides. Opt. Mater. (Amst) 31(2): 209–212. http://dx.doi.org/10.1016/j.optmat.2008.03.010

[5] Reddy RR, Nazeer Ahammed Y. (1995). A study on the Moss relation. Infrared Phys. Technol 36(5): 825–830. http://dx.doi.org/10.1016/1350-4495(95)00008-M

[6] Reddy et al. (2009). Interrelationship between structural, optical, electronic and elastic properties of materials. J. Alloys Compd. 473(1): 28–35. http://dx.doi.org/10.1016/j.jallcom.2008.06.037

[7] Reddy RR, Anjaneyulu S. (1992). Analysis of the moss and ravindra relations. Phys. status solidi 174(2): K91–K93. http://dx.doi.org/10.1002/pssb.2221740238

[8] Ravindra NM, Auluck S, Srivastava VK. (1979). On the penn gap in semiconductors. Phys. Status Solidi 93(2): K155–K160. http://dx.doi.org/10.1002/pssb.2220930257

[9] Ravindra NM, Ganapathy P, Choi J. (2006). Energy gap–refractive index relations in semiconductors – An overview. http://dx.doi.org/10.1016/j.infrared.2006.04.001

[10] Penn DR. (1962). Wave-number-dependent dielectric function of semiconductors. Phys. Rev. 128(5): 2093–2097.

[11] Pal S, Kumar Tiwari R, Chandra Gupta D, Singh Verma A. (2014). Simplistic theoretical model for optoelectronic properties of compound semiconductors. J. Mater. Phys. Chem. 2(2): 20–27.

[12] Moss, Smoluchowski R. (1954). Photoconductivity in the elements. Phys. Today 7(2): 18.

[13] Moss TS. (1985). Relations between the refractive index and energy gap of semiconductors. Phys. status solidi 131(20): 415–427.

[14] Moss TS. (1950). A relationship between the refractive index and the infra-red threshold of sensitivity for photoconductors. Proc. Phys. Soc. Sect. B 63(3):167–176.

[15] Kumar V, Singh JK. (2010). Model for calculating the refractive index of different materials. IJPAP 48(8).

[16] Kumar V, Sinha A, Singh BP, Sinha AP, Jha V. (2015). Refractive index and electronic polarizability of ternary chalcopyrite semiconductors. Chinese Phys. Lett 32(12): 127701.

[17] Hervé PJL, Vandamme LKJ. (1995). Empirical temperature dependence of the refractive index of semiconductors. J. Appl. Phys. 77(10): 5476.

[18] Gupta VP, Ravindra NM. (1980). Comments on the moss formula. Phys. status solidi 100(2): 715–719.

[19] Anani M, Mathieu C, Lebid S, Amar Y, Chama Z, Abid H. (2008). Model for calculating the refractive index of a III–V semiconductor. Comput. Mater. Sci 41(4): 570–575.

[20] Blakemore JS. (1982). Semiconducting and other major properties of gallium arsenide. J. Appl. Phys. 53(10): R123. http://dx.doi.org/10.1063/1.331665

[21] Akande KO, Owolabi TO, Olatunji SO, AbdulRaheem A. (2016). A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir. J. Pet. Sci. Eng 0–1 http://dx.doi.org/10.1016/j.petrol.2016.11.033

[22] Cui Y, Dy JG, Alexander B, Jiang SB. (2008). Fluoroscopic gating without implanted fiducial markers for lung cancer radiotherapy based on support vector machines. Phys. Med. Biol 53: 315–327. http://dx.doi.org/10.1088/0031-9155/53/16/N01

[23] Cai CZ, Xiao TT, Tang JL, Huang SJ. (2013). Analysis of process parameters in the laser deposition of YBa2Cu3O7 superconducting films by using SVR. Phys. C Supercond 493: 100–103. http://dx.doi.org/10.1016/j.physc.2013.03.038

[24] Akande KO, Owolabi TO, Olatunji SO. (2015). Investigating the effect of correlation-based feature selection on the performance of support vector machines in reservoir characterization. J. Nat. Gas Sci. Eng 22: 515–522. http://dx.doi.org/10.1016/j.jngse.2015.01.007

[25] Majid A, Khan A, Javed G, Mirza AM. (2010). Lattice constant prediction of cubic and monoclinic perovskites using neural networks and support vector regression. Comput. Mater. Sci. 50: 363–372. http://dx.doi.org/10.1016/j.commatsci.2010.08.028

[26] Owolabi TO, Akande KO, Olatunji SO. (2016). Application of computational intelligence technique for estimating superconducting transition temperature of YBCO superconductors. Appl. Soft Comput 43(2016): 143–149

[27] Akande KO, Owolabi TO, Olatunji SO. (2014). Estimation of superconducting transition temperature T C for superconductors of the doped MgB2 system from the crystal lattice parameters using support vector regression. J. Supercond. Nov. Magn. http://dx.doi.org/10.1007/s10948-014-2891-7

[28] Cai CZ, Wang GL, Wen YF, Pei JF, Zhu XJ, Zhuang WP. (2010). Superconducting transition temperature T c estimation for superconductors of the doped mgb2 system using topological index via support vector regression. J. Supercond. Nov. Magn 23(5): 745–748, Jan. 2010. http://dx.doi.org/10.1007/s10948-010-0727-7

[29] Owolabi TO, Akande KO, Olatunji SO. (2015). Development and validation of surface energies estimator (SEE) using computational intelligence technique. Comput. Mater. Sci. 101: 143–151. http://dx.doi.org/10.1016/j.commatsci.2015.01.020

[30] Owolabi TO, Akande KO, Olatunji SO. (2015). Estimation of surface energies of hexagonal close packed metals using computational intelligence technique. Appl. Soft Comput. 31: 360–368. http://dx.doi.org/10.1016/j.asoc.2015.03.009

[31] Owolabi TO, Akande KO, Sunday OO. (2015). Modeling of average surface energy estimator using computational intelligence technique. Multidiscip. Model. Mater. Struct 11(2): 284–296.

[32] Owolabi TO, Akande KO, Olatunji SO, Alqahtani A, Aldhafferi N. (2016). Estimation of curie temperature of manganite-based materials for magnetic refrigeration application using hybrid gravitational based support vector regression. AIP Adv 6(10): 105009. http://dx.doi.org/10.1063/1.4966043

[33] Rashedi E, Nezamabadi-pour H, Saryazdi S. (2009). GSA: A gravitational search algorithm. Inf. Sci. (Ny)., 179(13): 2232–2248. http://dx.doi.org/10.1016/j.ins.2009.03.004

[34] Niu P, Liu C, Li P. (2015). Optimized support vector regression model by improved gravitational search algorithm for flatness pattern recognition. Neural Comput. Appl. 2015:1167–1177

[35] Ju FY, Hong WC. (2013). Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting. Appl. Math. Model 37(23): 9643–9651. http://dx.doi.org/10.1016/j.apm.2013.05.016

[36] Owolabi TO, Akande KO, Olatunji SO, Alqahtani A, Aldhafferi N. (2017). Modeling energy band gap of doped TiO2 semiconductor using homogeneously hybridized support vector regression with gravitational search algorithm hyper- parameter optimization, AIP Adv 115225, http://dx.doi.org/10.1063/1.5009693

[37] Owolabi TO, Zakariya YF, Akande KO, Olatunji SO. (2016). Estimation of melting points of fatty acids using homogeneously 3 hybridized support vector regression. Neural Comput. Appl., 2016

[38] Owolabi TO, Akande KO, Olatunji SO, Alqahtani A. (2016). A novel homogenous hybridization scheme for performance improvement of support vector machines regression in reservoir characterization. Appl. Comput. Intell. Soft Comput 2016: 1–10. http://dx.doi.org/10.1155/2016/2580169

[39] Drucker H, Burges CJC, Kaufman L, Smola AJ, Vapnik VN. (1996). Support vector regression machines. Adv. Neural Inf. Process. Syst 9.

[40] Basak D, Pal S, Patranabis DC. (2007). Support Vector Regression, Neural Inf. Process. – Lett. Rev 11(2007).

[41] Vapnik VN. (1995). The nature of statistical learning theory. Springer-Verlag New York, Inc. http://dx.doi.org/10.1007/978-1-4757-3264-1

[42] Owolabi TO, Akande KO, Olatunji SO. (2016). Computational intelligence method of estimating solid-liquid interfacial energy of materials at their melting temperatures. J. Intell. fuzzy Syst 31: 519–527. http://dx.doi.org/10.3233/IFS-162164

[43] Thenmozhi M, Sarath Chand G. (2016). Forecasting stock returns based on information transmission across global markets using support vector machines. Neural Comput. Appl 27: 805–824. http://dx.doi.org/10.1007/s00521-015-1897-9

[44] Verma AS, Singh RK, Rathi SK. (2009). An empirical model for dielectric constant and electronic polarizability of binary (ANB8-N) and ternary (ANB2+NC27-N) tetrahedral semiconductors. J. Alloys Compd 486(1-2): 795–800. http://dx.doi.org/10.1016/j.jallcom.2009.07.067

[45] Manukonda D, Srinivasa RG. (2018). A fuzzy logic controller based vortex wind turbine for commercial applications. Modelling, Measurement and Control A 91(2): 54-58. http://dx.doi.org/10.18280/mmc_a.910204

[46] Mehta D, Prasanta K, Anandita C. (2018). Development of energy efficient, cost-optimized transformer with low partial discharges. Modelling, Measurement and Control A 91(2): 59-65. https://doi.org/10.18280/mmc_a.910205

[47] Samala RK, Mercy RK. (2018). Optimal DG sizing and siting in radial system using hybridization of GSA and firefly algorithms. Modelling, Measurement and Control A 91(2): 77-82. https://doi.org/10.18280/mmc_a.910208