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First vertical-cavity surface-emitting laser made entirely in Poland

Marcin Gębski Patrycja Śpiewak Walery Kołkowski Iwona Pasternak Weronika Głowadzka Włodzimierz Nakwaski Robert P. Sarzała Michał Wasiak Tomasz Czyszanowski Włodzimierz Strupiński
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | 3 | e137272 | DOI: 10.24425/bpasts.2021.137272
Keywords semiconductor laser GaAs optical communication VCSEL
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Abstract

The paper presents the first vertical-cavity surface-emitting lasers (VCSELs) designed, grown, processed and evaluated entirely in Poland. The lasers emit at »850 nm, which is the most commonly used wavelength for short-reach (<2 km) optical data communication across multiple-mode optical fiber. Our devices present state-of-the-art electrical and optical parameters, e.g. high room-temperature maximum optical powers of over 5 mW, laser emission at heat-sink temperatures up to at least 95°C, low threshold current densities (<10 kA/cm2) and wall-plug efficiencies exceeding 30% VCSELs can also be easily adjusted to reach emission wavelengths of around 780 to 1090 nm.
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Bibliography

  1.  R.N. Hall, G.E. Fenner, R.J. Kingsley, T.J. Soltys, and R.D. Carlson, “Coherent light emission of radiation from GaAs junctions”, Phys. Rev. Lett. 9(9), 366–368 (1962).
  2.  M.I. Nathan, W.P. Dumke, G. Burns, F.H. Dill Jr., and G. Lasher, “Stimulated emission of radiation from GaAs p-n junctions”, Appl. Phys. Lett. 1(3), 62–64 (1962).
  3.  N. Holonyak, Jr. and S.F. Bevacqua, “Coherent (visible) light emission from Ga(As1-xPx), junctions”, Appl. Phys. Lett. 1(4), 82–83 (1962).
  4.  T.M. Quist et al., “Semiconductor maser of GaAs”, Appl. Phys. Lett. 1(4), 91–92 (1962).
  5.  I. Hayashi, M.B. Panish, P.W. Foy, and S. Sumski, “Junction lasers which operate continuously at room temperature”, Appl. Phys. Lett. 17(3), 109–110 (1970).
  6.  J.A. Lott, “Vertical Cavity Surface Emitting Laser Diodes for Communication, Sensing, and Integration” in Semiconductor Nanophotonics. Springer Series in Solid-State Sciences, vol. 194, Eds. M. Kneissl, A. Knorr, S. Reitzenstein, A. Hoffmann, Springer, Cham, 2020.
  7.  I. Melngailis, “Longitudinal injection plasma laser of InSb”, Appl. Phys. Lett. 6(3), 59–60 (1965).
  8.  R. Dingle, W. Wiegmann, and C.H. Henry, “Quantum states of confined carriers in very thin AlxGa1-xAs-GaAs–AlxGa1-xAs heterostructures”, Phys. Rev. Lett. 33(14), 827–830 (1974).
  9.  J.P. van der Ziel, R. Dingle, R.C. Miller, W. Wiegmann, and W.A. Nordland Jr, “Laser oscillation from quantum states in very thin GaAs- Al0.2Ga0.8As multilayer structures”, Appl. Phys. Lett. 26(8), 463–465 (1975).
  10.  J.P. van der Ziel, and M. Ilegems, “Multilayer GaAs-A10.3Ga0.7As dielectric quarter wave stacks grown by molecular beam epitaxy”, Appl. Opt. 14(11), 2627–2630 (1975).
  11.  D.R. Scifres, R.D. Burnham, and W. Streifer, “Highly collimated laser beams from electrically pumped SH GaAs/GaAlAs distributed- feedback lasers”, Appl. Phys. Lett. 26(2), 48–50 (1975).
  12.  D. Scifres and R.D. Burnham, Distributed feedback diode laser, US Patent US 3983509, 28 Sep 1976.
  13.  H. Soda, K. Iga, C. Kitahara, and Y. Suematsu, “GalnAsP/lnP surface emitting injection lasers”, Jpn. J. Appl. Phys. 18(12), 2329 (1979).
  14.  M. Ogura, T. Hata, N.J. Kawai, and T. Yao, “GaAs/AlxGa1−xAs multilayer reflector for surface emitting laser diode”, Jpn. J. Appl. Phys. 22(2A), L112–L114 (1983).
  15.  M. Ogura, T. Hata, and T. Yao, “Distributed feed back surface emitting laser diode with multilayeredheterostructure”, Jpn. J. Appl. Phys. 23(7A), L512–L514 (1984).
  16.  M. Ogura and T. Yao, “Surface emitting laser diode with AlxGa1−xAs/GaAs multilayered heterostructure”, J. Vac. Sci. Technol. B 3(2), 784–787 (1985).
  17.  F. Koyama, F. Kinoshita, and K. Iga, “Room temperature cw operation of GaAs vertical cavity surface emitting laser”, Trans. IEICE Jpn. E71(11), 1089–1090 (1988).
  18.  P. Boulay, “After 20 years the VCSEL business has found its killer application – and is likely to explode”, European VCSEL Day, Brussels, 2019.
  19.  M. Gębski, P.S. Wong, M. Riaziat, and J.A. Lott, “30 GHz bandwidth temperature stable 980 nm VCSELs with AlAs/GaAs bottom DBRs for optical data communication”, J. Phys. Photonics, 2(3), 035008 (2020).
  20.  N. Haghighi, P. Moser, and J.A. Lott, “Power, bandwidth, and efficiency of single VCSELs and small VCSEL arrays”, IEEE J. Sel. Top. Quantum Electron. 25(6), 1–15 (2019).
  21.  S. Okur, M. Scheller, J.F. Seurin, A. Miglo, G. Xu, D. Guo, R. Van Leeuwen, B. Guo, H. Othman, L. Watkins, and C. Ghosh, “High-power VCSEL arrays with customized beam divergence for 3D-sensing applications”, in Vertical-Cavity Surface-Emitting Lasers XXIII 2019, International Society for Optics and Photonics, 2019, vol. 10938, p. 109380F.
  22.  I. Fujioka, Z. Ho, X. Gu, and F. Koyama, “Solid state LiDAR with sensing distance of over 40m using a VCSEL beam scanner”, In 2020 Conference on Lasers and Electro-Optics (CLEO) 2020, 2020, art. 10(1–2).
  23.  B. Darek, B. Mroziewicz, and J. Świderski. “Polish-made laser using a gallium arsenide junction (Gallium arsenide laser design using p-n junction obtained by diffusion of zinc in tellurium doped n-GaAs single crystal)”, Archiwum Elektrotechniki 15(1), 163–167 (1966).
  24.  P. Prystawko et al., “Blue-Laser Structures Grown on Bulk GaN Crystals”, Phys. Status Solidi A 192(2), 320–324 (2002).
  25.  K. Kosiel et al., “77 K Operation of AlGaAs/GaAs Quantum Cascade Laser at 9 mm”, Photonics Letters of Poland 1(1), 16–18, 2009.
  26.  J. Muszalski et al., “InGaAs resonant cavity light emitting diodes (RC LEDs)”, 9th Int. Symp. “Nanostructures: Physics and Technology” MPC.04, St Petersburg, Russia, 2001.
  27.  A.G. Baca and C.I. Ashby, “Fabrication of GaAs devices, chapter 10 “Wet oxidation for optoelectronic and MIS GaAs devices”, IET, London, United Kingdom, 2005.
  28.  Trumpf, Single and multiple-mode VCSELs. [Online] https://www.trumpf.com/en_US/products/vcsel-solutions-photodiodes/single- multiple-mode-vcsels/single-mode-vcsels/
  29.  F.A.I. Chaqmaqchee and J.A. Lott, “Impact of oxide aperture diameter on optical output power, spectral emission, and bandwidth for 980 nm VCSELs”, OSA Continuum, 3(9), 2602–2613 (2020).
  30.  J. Lavrencik et al., “Error-free 850 nm to 1060 nm VCSEL links: feasibility of 400Gbps and 800Gbps 8λ-SWDM”, Proceedings 45th European Conference on Optical Communication (ECOC), Dublin, Ireland, 2019, P84.
  31.  E. Simpanen et al., “1060 nm single-mode VCSEL and single-mode fiber links for long-reach optical interconnects”, J. Lightwave Technol. 37(13), 2963–2969 (2019).
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Authors and Affiliations

Marcin Gębski
1
ORCID: ORCID
Patrycja Śpiewak
1
ORCID: ORCID
Walery Kołkowski
2
Iwona Pasternak
2
Weronika Głowadzka
1
Włodzimierz Nakwaski
1
Robert P. Sarzała
1
ORCID: ORCID
Michał Wasiak
1
ORCID: ORCID
Tomasz Czyszanowski
1
Włodzimierz Strupiński
2
  1. Photonics Group, Institute of Physics, Lodz University of Technology, ul. Wólczańska 219, 90-924 Łódź
  2. Vigo System S.A., ul. Poznańska 129/133, 05-850 Ożarów Mazowiecki

Aspect-based sentiment classification model employing whale-optimized adaptive neural network

Nallathambi Balaganesh K. Muneeswaran
Bulletin of the Polish Academy of Sciences Technical Sciences | 2021 | 69 | 3 | e137271 | DOI: 10.24425/bpasts.2021.137271
Keywords aspect-based sentiment analysis whale optimization algorithm artificial neural network opinion mining
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Abstract

Nowadays in e-commerce applications, aspect-based sentiment analysis has become vital, and every consumer started focusing on various aspects of the product before making the purchasing decision on online portals like Amazon, Walmart, Alibaba, etc. Hence, the enhancement of sentiment classification considering every aspect of products and services is in the limelight. In this proposed research, an aspect-based sentiment classification model has been developed employing sentiment whale-optimized adaptive neural network (SWOANN) for classifying the sentiment for key aspects of products and services. The accuracy of sentiment classification of the product and services has been improved by the optimal selection of weights of neurons in the proposed model. The promising results are obtained by analyzing the mobile phone review dataset when compared with other existing sentiment classification approaches such as support vector machine (SVM) and artificial neural network (ANN). The proposed work uses key features such as the positive opinion score, negative opinion score, and term frequency-inverse document frequency (TF-IDF) for representing each aspect of products and services, which further improves the overall effectiveness of the classifier. The proposed model can be compatible with any sentiment classification problem of products and services.
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Bibliography

  1.  L. Cui, S. Huang, F. Wei, C. Tan, C. Duan, and M. Zhou, “Superagent: A customer service chatbot for E-commerce websites,” in ACL 2017 – 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of System Demonstrations, 2017, pp. 97–102, doi: 10.18653/v1/P17-4017.
  2.  M. Afzaal, M. Usman, and A. Fong, “Tourism mobile app with aspect-based sentiment classification framework for tourist reviews,” IEEE Trans. Consum. Electron. 65(2), 233–242, 2019, doi: 10.1109/TCE.2019.2908944.
  3.  M.S. Akhtar, T. Garg, and A. Ekbal, “Multi-task learning for aspect term extraction and aspect sentiment classification,” Neurocomputing 398, pp. 247–256, 2020, doi: 10.1016/j.neucom.2020.02.093.
  4.  M. Pontiki, D. Galanis, J. Pavlopoulos, H. Papageorgiou, I. Androutsopoulos, and S. Manandhar, “SemEval-2014 Task 4: Aspect Based Sentiment Analysis,” in Proceedings ofthe 8th International Workshop on Semantic Evaluation (SemEval 2014), 2014, pp. 27–35, doi: 10.3115/v1/s14-2004.
  5.  M. Ghiassi, J. Skinner, and D. Zimbra, “Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network,” Expert Syst. Appl. 40(16), 6266–6282, 2013, doi: 10.1016/j.eswa.2013.05.057.
  6.  M. Mladenović, J. Mitrović, C. Krstev, and D. Vitas, “Hybrid sentiment analysis framework for a morphologically rich language,” J. Intell. Inf. Syst. 46(3), 599–620, 2016, doi: 10.1007/s10844-015-0372-5.
  7.  Y. Kai, Y. Cai, H. Dongping, J. Li, Z. Zhou, and X. Lei, “An effective hybrid model for opinion mining and sentiment analysis,” in IEEE International Conference on Big Data and Smart Computing, BigComp 2017, 2017, pp. 465–466, doi: 10.1109/BIGCOMP.2017.7881759.
  8.  F. Iqbal et al., “A Hybrid Framework for Sentiment Analysis Using Genetic Algorithm Based Feature Reduction,” IEEE Access 7, pp. 14637–14652, 2019, doi: 10.1109/ACCESS.2019.2892852.
  9.  J.R. Alharbi and W.S. Alhalabi, “Hybrid approach for sentiment analysis of twitter posts using a dictionary-based approach and fuzzy logic methods: Study case on cloud service providers,” Int. J. Semant. Web Inf. Syst. 16(1), 116–145, 2020, doi: 10.4018/IJSWIS.2020010106.
  10.  S.C. Cagan, M. Aci, B.B. Buldum, and C. Aci, “Artificial neural networks in mechanical surface enhancement technique for the prediction of surface roughness and microhardness of magnesium alloy,” Bull. Polish Acad. Sci. Tech. Sci. 67(4), 729–739, 2019, doi: 10.24425/ bpasts.2019.130182.
  11.  B. Paprocki, A. Pregowska, and J. Szczepanski, “Optimizing information processing in brain-inspired neural networks,” Bull. Polish Acad. Sci. Tech. Sci. 68(2), 225–233, 2020, doi: 10.24425/bpasts.2020.131844.
  12.  I. Rojek and E. Dostatni, “Machine learning methods for optimal compatibility of materials in ecodesign,” Bull. Polish Acad. Sci. Tech. Sci. 68(2), 199–206, 2020, doi: 10.24425/bpasts.2020.131848.
  13.  S. Kirkpatrick, C.D. Gelatt, and M.P. Vecchi, “Optimization by simulated annealing,” Science 220(4598), 671–680, 1983, doi: 10.1126/ science.220.4598.671.
  14.  F.F. Moghaddam, R.F. Moghaddam, and M. Cheriet, “Curved Space Optimization: A Random Search based on General Relativity Theory,” pp. 1–16, 2012, [Online]. Available: http://arxiv.org/abs/1208.2214.
  15.  S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Adv. Eng. Softw. 95, pp. 51–67, 2016, doi: 10.1016/j.advengsoft.2016.01.008.
  16.  T. Brychcín, M. Konkol, and J. Steinberger, “UWB: Machine Learning Approach to Aspect-Based Sentiment Analysis,” in Proc. 8th Int. Workshop Semantic Eval. (SemEval) (2014), 2015, no. SemEval, pp. 817–822, doi: 10.3115/v1/s14-2145.
  17.  J. Singh, G. Singh, and R. Singh, “Optimization of sentiment analysis using machine learning classifiers,” Human-centric Comput. Inf. Sci. 7(1), 2017, doi: 10.1186/s13673-017-0116-3.
  18.  M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh, and B. Gupta, “Deep Recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews,” J. Comput. Sci. 27, pp. 386‒393, 2018, doi: 10.1016/j.jocs.2017.11.006.
  19.  P. Kalarani and S. Selva Brunda, “Sentiment analysis by POS and joint sentiment topic features using SVM and ANN,” Soft Comput. 23(16), 7067–7079, 2019, doi: 10.1007/s00500-018-3349-9.
  20.  L. Haghnegahdar and Y. Wang, “A whale optimization algorithm-trained artificial neural network for smart grid cyber intrusion detection,” Neural Comput. Appl. 32(13), 9427–9441, 2020, doi: 10.1007/s00521-019-04453-w.
  21.  J. Zhou, Q. Chen, J.X. Huang, Q. V. Hu, and L. He, “Position-aware hierarchical transfer model for aspect-level sentiment classification,” Inf. Sci. (Ny). 513, pp. 1–16, 2020, doi: 10.1016/j.ins.2019.11.048.
  22.  A.K. J and S. Abirami, “Aspect-based opinion ranking framework for product reviews using a Spearman’s rank correlation coefficient method,” Inf. Sci. (Ny). 460–461, pp. 23–41, 2018, doi: 10.1016/j.ins.2018.05.003.
  23.  C. Cortes and V. Vapnik, “Support-Vector Networks,” Mach. Learn. 20, pp. 273–297, 1995, doi: 10.1109/64.163674.
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Authors and Affiliations

Nallathambi Balaganesh
1
ORCID: ORCID
K. Muneeswaran
1
ORCID: ORCID
  1. Department of Computer Science & Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, Tamilnadu, India

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