Optimization of four-wave mixing wavelength conversion in a quantum-dot semiconductor optical amplifier based on the genetic algorithm


A novel approach based on the artificial neural network (ANN) and the genetic algorithm (GA) is presented for optimization of four-wave mixing (FWM) wavelength conversion in a quantum dot semiconductor optical amplifier (QD-SOA). First of all, we propose a simple, accurate, and fast model based on the feedforward ANN for the characteristics of FWM in a QD-SOA. To train the ANN, we collect the required data from a numerical model. In this model, the efficiency of FWM is obtained numerically taken into account the effect of pump/probe and the occupation probability of energy levels by using the slice technique. Then, the optimal design of QD-SOA as the FWM wavelength converter is performed using the GA.

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  1. Ababneh, J.I., et al.: Simple model for quantum-dot semiconductor optical amplifiers using artificial neural networks. IEEE J. Trans. Electron Devices 53(7), 1543–1550 (2006)

    ADS  Article  Google Scholar 

  2. Abid, S., et al.: "A fast feedforward training algorithm using a modified form of the standard backpropagation algorithm. IEEE J. Trans. Neural Netw. 12(2), 424–430 (2001)

    Article  Google Scholar 

  3. Akiyama, T., et al.: Symmetric highly efficient 0 dB wavelength conversion based on four-wave mixing in quantum dot optical amplifiers. IEEE J. Photon. Technol. Lett. 14(8), 1139–1141 (2002)

    ADS  Article  Google Scholar 

  4. Flayyih, A.H., et al.: Four-Wave Mixing in Quantum Dot SOAs: Theory of Carrier Heating. ELSEVIER J. Phys. 7, 1339–1345 (2017)

    Google Scholar 

  5. Hakimian, F., et al.: A proposal for a new method of modeling of the quantum-dot semiconductor optical amplifiers. J. Optoelectr. Nanostructu. 4(3), 1–16 (2019)

    Google Scholar 

  6. Hakimian, F., et al.: Optimization of a quantum-dot semiconductor optical amplifier (QD-SOA) design using the genetic algorithm. Springer J. Opt. Quantum Electr. 52(48), 1–19 (2020)

    Google Scholar 

  7. Hakimiyan, F., et al.: Design of quantum dot semiconductor optical amplifier by intelligence methods. Proc. Comput. Sci. 3, 449–452 (2011)

    Article  Google Scholar 

  8. Izadyar, S. M. et al.: Quantum dot semiconductor optical amplifier: investigation of amplified spontaneous emission and noise figure in the presence of second excited state. J. Opt. Quantum Electr. 50(1), 5 (2018). https://doi.org/10.1007/s11082-017-1265-3

    Article  Google Scholar 

  9. Lingnau, B., Zajnulinam, M., Lüdge, K.: Four-Wave Mixing and Rabi Oscillations in Quantum-Dot Semiconductor Optical Amplifiers. In: 2017 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD), Copenhagen, pp. 31–32, (2017)

  10. Lv, C., et al.: Levenberg–marquardt backpropagation training of multilayer neural networks for state estimation of a safety-critical cyber-physical system. IEEE Trans. Industr. Inf. 14(8), 3436–3446 (2018)

    Article  Google Scholar 

  11. Norman, J.C., et al.: A review of high-performance quantum dot lasers on silicon. IEEE J. Quantum Electron. 55(2), 1–11 (2019)

    Article  Google Scholar 

  12. Nosratpour, A., et al.: Numerical analysis of four wave mixing in photonic crystal semiconductor optical amplifier. ELSEVIER J. Opt. Commun. 433, 104–110 (2019)

    ADS  Article  Google Scholar 

  13. Qasaimeh, O.: Theory of four-wave mixing wavelength conversion in quantum dot semiconductor optical amplifiers. IEEE J. Photon. Technol. Lett. 16(4), 993–995 (2004)

    ADS  Article  Google Scholar 

  14. Qasaimeh, O.: Linewidth enhancement factor of quantum dot lasers. SPRINGER J. Opt. Quantum Electr. 37(5), 495–507 (2005)

    Article  Google Scholar 

  15. Qasaimeh, O.: Wide wavelength conversion in P-type doped quantum dot semiconductor optical amplifiers. ELSEVIER J. Opt. Commun. 305, 1–7 (2013)

    ADS  Article  Google Scholar 

  16. Svozil, D., et al.: Introduction to multi-layer feed-forward neural networks. Chemometr. Intell. Lab. Syst. 39(1), 43–62 (1997)

    Article  Google Scholar 

  17. Zajnulina, M., et al.: Four-wave mixing in quantum-dot semiconductor optical amplifiers: a detailed analysis of the nonlinear effects. IEEE J. Sel. Top. Quantum Electron. 23(6), 1–12 (2017)

    Article  Google Scholar 

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Correspondence to Mohammad Reza Shayesteh.

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Hakimian, F., Shayesteh, M.R. & Moslemi, M.R. Optimization of four-wave mixing wavelength conversion in a quantum-dot semiconductor optical amplifier based on the genetic algorithm. Opt Quant Electron 53, 140 (2021). https://doi.org/10.1007/s11082-021-02763-9

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  • Quantum dot semiconductor optical amplifier (QD-SOA)
  • Four-wave mixing
  • Wavelength conversion
  • Artificial neural network
  • Genetic algorithm