Machine learning enhanced design and knowledge discovery for multi-junction photonic power converters

Machine Learning


  • Chen, H., Jia, H., Wang, T. & Yang, J. A gradient-oriented binary search method for photonic device design. J. Light. Technol. 39, 2407–2412. https://doi.org/10.1109/JLT.2021.3050771 (2021).

    Article 
    CAS 

    Google Scholar 

  • Mao, S. et al. Inverse design for silicon photonics: From iterative optimization algorithms to deep neural networks. Appl. Sci. 11, 3822. https://doi.org/10.3390/app11093822 (2021).

    Article 
    CAS 

    Google Scholar 

  • Yao, K., Unni, R. & Zheng, Y. Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale. Nanophotonics 8, 339–366. https://doi.org/10.1515/nanoph-2018-0183 (2019).

    Article 
    PubMed 

    Google Scholar 

  • Ngatchou, P., Zarei, A. & El-Sharkawi, A. Pareto multi objective optimization. 2005, 84–91. https://doi.org/10.1109/ISAP.2005.1599245 (2005).

    Article 

    Google Scholar 

  • Ma, W. et al. Deep learning for the design of photonic structures. Nat. Photonics 15, 77–90 (2021).

    Article 
    CAS 

    Google Scholar 

  • Ren, Y. et al. Genetic-algorithm-based deep neural networks for highly efficient photonic device design. Photonics Res. 9, B247. https://doi.org/10.1364/PRJ.416294 (2021).

    Article 

    Google Scholar 

  • Deng, L., Xu, Y. & Liu, Y. Hybrid inverse design of photonic structures by combining optimization methods with neural networks. Photonics Nanostructures – Fundam. Appl. 52, 101073. https://doi.org/10.1016/j.photonics.2022.101073 (2022).

    Article 

    Google Scholar 

  • Jiang, J., Chen, M. & Fan, J. A. Deep neural networks for the evaluation and design of photonic devices. Nat. Rev. Mater. 6, 679–700. https://doi.org/10.1038/s41578-020-00260-1 (2020).

    Article 

    Google Scholar 

  • Hegde, R. S. Deep learning: a new tool for photonic nanostructure design. Nanoscale Adv. 2, 1007–1023. https://doi.org/10.1039/C9NA00656G (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Adibnia, E., Ghadrdan, M. & Ali Mansouri-Birjandi, M. Chirped apodized fiber bragg gratings inverse design via deep learning. Opt. Laser Technol 181, 111766. https://doi.org/10.1016/j.optlastec.2024.111766 (2025).

    Article 
    CAS 

    Google Scholar 

  • Adibnia, E., Ghadrdan, M. & Mansouri-Birjandi, M. A. Inverse design of fbg-based optical filters using deep learning: A hybrid cnn-mlp approach. J. Light. Technol. 43, 4452–4461. https://doi.org/10.1109/JLT.2025.3534275 (2025).

    Article 

    Google Scholar 

  • Kiarashinejad, Y., Abdollahramezani, S. & Adibi, A. Deep learning approach based on dimensionality reduction for designing electromagnetic nanostructures. npj Comput. Mater. 6(1), 12. https://doi.org/10.1038/s41524-020-0276-y (2020).

    Article 

    Google Scholar 

  • Melati, D. et al. Mapping the global design space of nanophotonic components using machine learning pattern recognition. Nat. Commun. 10, 4775. https://doi.org/10.1038/s41467-019-12698-1 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kamandar Dezfouli, M. et al. Perfectly vertical surface grating couplers using subwavelength engineering for increased feature sizes. Opt. Lett. 45, 3701. https://doi.org/10.1364/OL.395292 (2020).

    Article 
    PubMed 

    Google Scholar 

  • Melati, D. et al. Design of multi-parameter photonic devices using machine learning pattern recognition. In Baets, R. G., O’Brien, P. & Vivien, L. (eds.) Integrated Photonics Platforms: Fundamental Research, Manufacturing and Applications, 7, https://doi.org/10.1117/12.2559583 (SPIE, 2020).

  • Melati, D. et al. Design of compact and efficient silicon photonic micro antennas with perfectly vertical emission. IEEE J Sel. Top. Quantum Electron. 27, 1–10. https://doi.org/10.1109/JSTQE.2020.3013532 (2021).

    Article 

    Google Scholar 

  • Melati, D. et al. Subwavelength metamaterial devices with optimization and machine learning. In He, S. & Vivien, L. (eds.) Smart Photonic and Optoelectronic Integrated Circuits 2023, vol. PC12425, PC1242509, https://doi.org/10.1117/12.2649953. International Society for Optics and Photonics (SPIE, 2023).

  • Al-Digeil, M. et al. Pca-enhanced autoencoders for nonlinear dimensionality reduction in low data regimes. In Proceedings of the 36th Canadian Conference on Artificial Intelligence, https://doi.org/10.21428/594757db.05a13011 (Canadian Artificial Intelligence Association, 2023).

  • Zandehshahvar, M., Hemmatyar, O., Kiarashinejad, Y., Abdollahramezani, S. & Adibi, A. Dimensionality reduction based method for design and optimization of optical nanostructures using neural network. In Frontiers in Optics + Laser Science APS/DLS, FM5C.2, https://doi.org/10.1364/FIO.2019.FM5C.2 (OSA, 2019).

  • Kudyshev, Z. A., Kildishev, A. V., Shalaev, V. M. & Boltasseva, A. Machine learning–assisted global optimization of photonic devices. Nanophotonics 10, 371–383. https://doi.org/10.1515/nanoph-2020-0376 (2020).

    Article 

    Google Scholar 

  • Liu, Z., Raju, L., Zhu, D. & Cai, W. A hybrid strategy for the discovery and design of photonic structures. IEEE J. Emerg. Sel. Top. Circuits Syst. 10, 126–135. https://doi.org/10.1109/JETCAS.2020.2970080 (2020).

    Article 

    Google Scholar 

  • Liu, Z. et al. Compounding meta-atoms into metamolecules with hybrid artificial intelligence techniques. Adv. Mater. 32, 1904790. https://doi.org/10.1002/adma.201904790 (2020).

    Article 
    CAS 

    Google Scholar 

  • Kiarashinejad, Y., Abdollahramezani, S., Zandehshahvar, M., Hemmatyar, O. & Adibi, A. Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices. Adv. Theory Simul. 2, 1900088. https://doi.org/10.1002/adts.201900088 (2019).

    Article 
    CAS 

    Google Scholar 

  • Kiarashinejad, Y. et al. Knowledge discovery in nanophotonics using geometric deep learning. Adv. Intell. Syst. 2, 1900132. https://doi.org/10.1002/aisy.201900132 (2020).

    Article 

    Google Scholar 

  • Hemmatyar, O., Abdollahramezani, S., Kiarashinejad, Y., Zandehshahvar, M. & Adibi, A. Full color generation with Fano-type resonant HfO \(_{\rm 2 }\) nanopillars designed by a deep-learning approach. Nanoscale 11, 21266–21274. https://doi.org/10.1039/C9NR07408B (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Matsuura, M. Recent advancement in power-over-fiber technologies. Photonics 8, 335. https://doi.org/10.3390/photonics8080335 (2021).

    Article 
    CAS 

    Google Scholar 

  • Fafard, S. & Masson, D. P. Perspective on photovoltaic optical power converters. J. Appl. Phys. 130, 160901. https://doi.org/10.1063/5.0070860 (2021).

    Article 
    CAS 

    Google Scholar 

  • Algora, C. et al. Beaming power: Photovoltaic laser power converters for power-by-light. Joule 6, 340–368. https://doi.org/10.1016/j.joule.2021.11.014 (2022).

    Article 

    Google Scholar 

  • Putra, E. P. et al. Technology update on patent and development trend of power over fiber: a critical review and future prospects. J. Photonics Energy 13(1), 011001–011001. https://doi.org/10.1117/1.JPE.13.011001 (2023).

    Article 
    MathSciNet 
    CAS 

    Google Scholar 

  • Spillman, W., Crowne, D. & Woodward, D. Optically powered and interrogated rotary position sensor for aircraft engine control applications. Opt. Lasers Eng. 16, 105–118. https://doi.org/10.1016/0143-8166(92)90003-P (1992).

    Article 

    Google Scholar 

  • Worms, K. et al. Reliable and lightning-safe monitoring of wind turbine rotor blades using optically powered sensors. Wind Energy 20, 345–360. https://doi.org/10.1002/we.2009 (2017).

    Article 

    Google Scholar 

  • De Nazare, F. V. B. & Werneck, M. M. Hybrid optoelectronic sensor for current and temperature monitoring in overhead transmission lines. IEEE Sens. J. 12, 1193–1194. https://doi.org/10.1109/JSEN.2011.2163709 (2012).

    Article 

    Google Scholar 

  • Zheng, Y. et al. Wireless laser power transmission: Recent progress and future challenges. Space Sol. Power Wirel. Transm. S2950104023000020, https://doi.org/10.1016/j.sspwt.2023.12.001 (2024).

  • Ding, J., Liu, W., I, C.-L., Zhang, H. & Mei, H. Advanced progress of optical wireless technologies for power industry: An overview. Appl. Sci. 10(8), 6463. https://doi.org/10.3390/app10186463 (2020).

  • Zin, A. M., Bongsu, M. S., Idrus, S. M. & Zulkifli, N. An overview of radio-over-fiber network technology. In Int. Conf. Photonics 1–3, 2010. https://doi.org/10.1109/ICP.2010.5604429 (2010) ((IEEE)).

    Article 

    Google Scholar 

  • Aboelala, O., Lee, I. E. & Chung, G. C. A survey of hybrid free space optics (FSO) communication networks to achieve 5g connectivity for backhauling. Entropy 24, 1573. https://doi.org/10.3390/e24111573 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Goto, K., Nakagawa, T., Nakamura, O. & Kawata, S. An implantable power supply with an optically rechargeable lithium battery. IEEE Trans. Biomed. Eng. 48, 830–833. https://doi.org/10.1109/10.930908 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  • Vazquez, C. et al. Multicore fiber scenarios supporting power over fiber in radio over fiber systems. IEEE Access 7, 158409–158418. https://doi.org/10.1109/ACCESS.2019.2950599 (2019).

    Article 

    Google Scholar 

  • Al-Zubaidi, F. M. A., Lopez-Cardona, J. D., Sanchez Montero, D. & Vazquez, C. Optically powered radio-over-fiber systems in support of 5g cellular networks and IoT. J. Light. Technol. 39, 4262–4269. https://doi.org/10.1109/JLT.2021.3074193 (2021).

    Article 

    Google Scholar 

  • Grandidier, J. et al. Feasibility of power beaming through the venus atmosphere. Acta Astronautica 211, 376–381. https://doi.org/10.1016/j.actaastro.2023.06.042 (2023).

    Article 

    Google Scholar 

  • Malaviya, P., Sarvaiya, V., Shah, A., Thakkar, D. & Shah, M. A comprehensive review on space solar power satellite: an idiosyncratic approach. Environ. Sci. Pollut. Res. 29, 42476–42492. https://doi.org/10.1007/s11356-022-19560-w (2022).

    Article 

    Google Scholar 

  • Guiomar, F. P., Fernandes, M. A., Nascimento, J. L., Rodrigues, V. & Monteiro, P. P. Coherent free-space optical communications: Opportunities and challenges. J. Light. Technol. 40, 3173–3186. https://doi.org/10.1109/JLT.2022.3164736 (2022).

    Article 

    Google Scholar 

  • Jahid, A., Alsharif, M. H. & Hall, T. J. A contemporary survey on free space optical communication: Potentials, technical challenges, recent advances and research direction. J. Netw. Comput. Appl. 200, 103311. https://doi.org/10.1016/j.jnca.2021.103311 (2022).

    Article 

    Google Scholar 

  • Dahrouj, H., Douik, A., Rayal, F., Al-Naffouri, T. Y. & Alouini, M.-S. Cost-effective hybrid RF/FSO backhaul solution for next generation wireless systems. IEEE Wirel. Commun. 22, 98–104. https://doi.org/10.1109/MWC.2015.7306543 (2015).

    Article 

    Google Scholar 

  • Fafard, S. & Masson, D. P. High-efficiency and high-power multijunction InGaAs/InP photovoltaic laser power converters for 1470 nm. Photonics 9, 438. https://doi.org/10.3390/photonics9070438 (2022).

    Article 
    CAS 

    Google Scholar 

  • Lozano, J. F. et al. A new path towards ultra-high efficient laser power converters: Silicon carbide-based multijunction devices. Results Eng. 21, 101987. https://doi.org/10.1016/j.rineng.2024.101987 (2024).

    Article 
    CAS 

    Google Scholar 

  • Helmers, H. et al. Photovoltaic cells with increased voltage output for optical power supply of sensor electronics. In Proceedings SENSOR 2015, 519–524. https://doi.org/10.5162/sensor2015/D1.4 (2015) ((AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany)).

  • Forcade, G. P. et al. 53.6% efficient multi-junction laser power converter for extended telecom range operation. Manuscript submitted for publication (2024).

  • Zhao, Y., Sun, Y., He, Y., Yu, S. & Dong, J. Design and fabrication of six-volt vertically-stacked GaAs photovoltaic power converter. Sci. Rep. 6, 38044. https://doi.org/10.1038/srep38044 (2016).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Fafard, S. et al. Power and spectral range characteristics for optical power converters. Energies 14, 4395. https://doi.org/10.3390/en14154395 (2021).

    Article 
    CAS 

    Google Scholar 

  • Fafard, S. et al. High-efficiency photovoltaic power converters and application to optical power transmission. In 2021 26th Microoptics Conference (MOC), 1–2, https://doi.org/10.23919/MOC52031.2021.9598073 (IEEE, 2021).

  • Hunter, R. F. H., Valdivia, C. E., Baribeau, L. S. & Hinzer, K. Unlocking the potential for extreme solar concentration via subcell segmentation. In 17th International Conference on Concentrator Photovoltaic Systems (CPV)), 020006, https://doi.org/10.1063/5.0099470 (2022).

  • Valdivia, C. E. & Hinzer, K. Subcell segmentation for current matching and design flexibility in multijunction solar cells. IEEE J. Photovolt. 10, 1329–1339. https://doi.org/10.1109/JPHOTOV.2020.3005630 (2020).

    Article 

    Google Scholar 

  • Baribeau, L. S., Hunter, R. F., Valdivia, C. E. & Hinzer, K. Drift-diffusion modelling of four-junction ingap/ingaas/sigesn/ge solar cells. In 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC), 1031–1031, https://doi.org/10.1109/PVSC48317.2022.9938603 (2022).

  • Fafard, S. et al. Ultrahigh efficiencies in vertical epitaxial heterostructure architectures. Appl. Phys. Lett. 108, 071101. https://doi.org/10.1063/1.4941240 (2016).

    Article 
    CAS 

    Google Scholar 

  • Fafard, S. & Masson, D. Vertical multi-junction laser power converters with 61% efficiency at 30 w output power and with tolerance to beam non-uniformity, partial illumination, and beam displacement. Photonics 10, https://doi.org/10.3390/photonics10080940 (2023).

  • Mukherjee, J., Jarvis, S., Perren, M. & Sweeney, S. J. Efficiency limits of laser power converters for optical power transfer applications. J. Phys. D Appl. Phys. 46, 264006. https://doi.org/10.1088/0022-3727/46/26/264006 (2013).

    Article 
    CAS 

    Google Scholar 

  • Beattie, M. N. et al. InP- and GaAs-based photonic power converters under o-band laser illumination: Performance analysis and comparison. IEEE J. Photovolt. 13, 113–121. https://doi.org/10.1109/JPHOTOV.2022.3218938 (2023).

    Article 

    Google Scholar 

  • Synopsys Inc. Sentaurus TCAD user guide version S-2021.06 (2021).

  • Liu, V. & Fan, S. \(\text{ S}^{4}\): A free electromagnetic solver for layered periodic structures. Comput. Phys. Commun. 183, 2233–2244. https://doi.org/10.1016/j.cpc.2012.04.026 (2012).

    Article 
    MathSciNet 
    CAS 

    Google Scholar 

  • Pearce, P. & Liu, V. S4 – RCWA. github https://github.com/phoebe-p/S4 (retrieved Jan. 2023).

  • Pedregosa, F. et al. Scikit-learn: Machine learning in python. J. Mach. Learn. Res. 12, 2825–2830. https://doi.org/10.5555/1953048.2078195 (2011).

    Article 
    MathSciNet 

    Google Scholar 

  • Synopsys Inc. Sentaurus data explorer user guide version S-2021.06 (2021).

  • Wilkins, M. et al. Luminescent coupling in planar opto-electronic devices. J. Appl. Phys. 118, 143102. https://doi.org/10.1063/1.4932660 (2015).

    Article 
    CAS 

    Google Scholar 

  • Forcade, G. P. et al. High-performance multi-junction c-band photonic power converters: Calibrated optoelectronic model for next generation designs. In 2023 IEEE 50th Photovoltaic Specialists Conference (PVSC), https://doi.org/10.1109/PVSC48320.2023.10359786 (2023).

  • Helmers, H. et al. Advancing solar energy conversion efficiency to 47.6% and exploring the spectral versatility of III-V photonic power converters. In Freundlich, A., Collin, S., Hinzer, K. & Sellers, I. R. (eds.) Physics, Simulation, and Photonic Engineering of Photovoltaic Devices XIII, vol. 12881, 1288103, https://doi.org/10.1117/12.3000352. International Society for Optics and Photonics (SPIE, 2024).

  • Helmers, H. et al. Unlocking 1550 nm laser power conversion by ingaas single- and multi-junction pv cells. In 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC), 9938627, 1235–1235, https://doi.org/10.1109/PVSC48317.2022.9938627 (2022).

  • Beattie, M. N. et al. EQE measurement technique for multi-junction photovoltaics with overlapping subcell absorptance. In Freundlich, A., Collin, S., Hinzer, K. & Sellers, I. R. (eds.) Physics, Simulation, and Photonic Engineering of Photovoltaic Devices XIII, vol. PC12881, PC1288107, https://doi.org/10.1117/12.3001662. International Society for Optics and Photonics (SPIE, 2024).

  • Wilson, P. et al. Quantifying the luminescent coupling process in C-band multi-junction photonic power converters. In Freundlich, A., Collin, S., Hinzer, K. & Sellers, I. R. (eds.) Physics, Simulation, and Photonic Engineering of Photovoltaic Devices XIII, vol. PC12881, PC128810J, https://doi.org/10.1117/12.3000797. International Society for Optics and Photonics (SPIE, 2024).

  • Piprek, J. Simulation-based machine learning for optoelectronic device design: perspectives, problems, and prospects. Opt. Quantum Electron. 53(4), 175. https://doi.org/10.1007/s11082-021-02837-8 (2021).

    Article 

    Google Scholar 

  • Soresi, S. et al. Inp:s/alinas:c tunnel junction grown by movpe for photovoltaic applications. Phys. Status Solidi (A) 215, 1700427. https://doi.org/10.1002/pssa.201700427 (2018).

    Article 
    CAS 

    Google Scholar 

  • Melati, D. et al. Machine learning design of subwavelengh integrated photonic devices. In 2019 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD), 135–136, https://doi.org/10.1109/NUSOD.2019.8806835 (IEEE, 2019).

  • Jolliffe, I. T. & Cadima, J. Principal component analysis: a review and recent developments. Philos. Transactions Royal Soc. A: Math. Phys. Eng. Sci. 374, 20150202. https://doi.org/10.1098/rsta.2015.0202 (2016).

    Article 
    MathSciNet 

    Google Scholar 

  • Pearson, K. L. I. I. I. on lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philos. Mag. J. Sci. 2, 559–572. https://doi.org/10.1080/14786440109462720 (1901).

    Article 

    Google Scholar 

  • Hunter, R. F. H. aiirmap. github https://github.com/rfhhunter/aiirmap (2024).

  • Wes McKinney. Data Structures for Statistical Computing in Python. In Stéfan van der Walt & Jarrod Millman (eds.) Proceedings of the 9th Python in Science Conference, 56 – 61, https://doi.org/10.25080/Majora-92bf1922-00a (2010).

  • The pandas development team. pandas-dev/pandas: Pandas, version 2.2.2, https://doi.org/10.5281/zenodo.3509134 (Apr. 2024).

  • Helmers, H. et al. 68.9% efficient GaAs-based photonic power conversion enabled by photon recycling and optical resonance.. physica status solidi (RRL) – Rapid Res. Lett. 15, 2100113. https://doi.org/10.1002/pssr.202100113 (2021).

    Article 
    CAS 

    Google Scholar 

  • Ayesha, S., Hanif, M. K. & Talib, R. Overview and comparative study of dimensionality reduction techniques for high dimensional data. Inf. Fusion 59, 44–58. https://doi.org/10.1016/j.inffus.2020.01.005 (2020).

    Article 

    Google Scholar 

  • How, W. B., Wang, B., Chu, W., Tkatchenko, A. & Prezhdo, O. V. Significance of the chemical environment of an element in nonadiabatic molecular dynamics: Feature selection and dimensionality reduction with machine learning. J. Phys. Chem. Lett. 12, 12026–12032. https://doi.org/10.1021/acs.jpclett.1c03469 (2021).

    Article 
    CAS 
    PubMed 

    Google Scholar 



  • Source link