Researching | Optical neural networks based on perovskite solar cells

Photonics Research, Volume. 13, Issue 2, 382(2025)

Optical neural networks based on perovskite solar cells

Kaicheng Zhang¹, Jonathon Harwell¹, Davide Pierangeli², Claudio Conti³, and Andrea Di Falco^1、*

Author Affiliations

¹School of Physics and Astronomy, University of St Andrews, St Andrews KY16 9SS, Scotland

²Institute for Complex Systems, National Research Council, Rome 00185, Italy

³Department of Physics, Sapienza Università di Roma, Rome 00185, Italy

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[1] D. Zhang, Z. Tan. A review of optical neural networks. Appl. Sci., 12, 5338(2022).

[2] J. Liu, Q. Wu, X. Sui. Research progress in optical neural networks: theory, applications and developments. PhotoniX, 2, 5(2021).

[3] Z. Cheng, C. Ros, W. H. Pernice. On-chip photonic synapse. Sci. Adv., 3, e1700160(2017).

[4] C. Qian, Z. Wang, H. Qian. Dynamic recognition and mirage using neuro-metamaterials. Nat. Commun., 13, 2694(2022).

[5] D. Owen-Newns, J. Robertson, M. Hejda. GHz rate neuromorphic photonic spiking neural network with a single vertical cavity surface-emitting laser (VCSEL). IEEE J. Sel. Top. Quantum Electron., 29, 1500110(2022).

[6] C. D. Schuman, S. R. Kulkarni, M. Parsa. Opportunities for neuromorphic computing algorithms and applications. Nat. Comput. Sci., 2, 10-19(2022).

[7] S. B. Furber, D. R. Lester, L. A. Plana. Overview of the spinnaker system architecture. IEEE Trans. Comput., 62, 2454-2467(2012).

[8] J. Wu, X. Lin, Y. Guo. Analog optical computing for artificial intelligence. Engineering, 10, 133-145(2022).

[9] M. Onen, N. Emond, B. Wang. Nanosecond protonic programmable resistors for analog deep learning. Science, 377, 539-543(2022).

[10] J. Spall, X. Guo, T. D. Barrett. Fully reconfigurable coherent optical vector–matrix multiplication. Opt. Lett., 45, 5752-5755(2020).

[11] L. Bernstein, A. Sludds, C. Panuski. Single-shot optical neural network. Sci. Adv., 9, eadg7904(2023).

[12] X. Lin, Y. Rivenson, N. T. Yardimci. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004-1008(2018).

[13] C. Liu, Q. Ma, Z. J. Luo. A programmable diffractive deep neural network based on a digital-coding metasurface array. Nat. Electron., 5, 113-122(2022).

[14] J. Chang, V. Sitzmann, X. Dun. Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Sci. Rep., 8, 12324(2018).

[15] X. Guo, T. D. Barrett, Z. M. Wang. Backpropagation through nonlinear units for the all-optical training of neural networks. Photon. Res., 9, B71-B80(2021).

[16] T. Wang, M. M. Sohoni, L. G. Wright. Image sensing with multilayer, nonlinear optical neural networks. Nat. Photonics, 18, 408-415(2023).

[17] T. Wang, S.-Y. Ma, L. G. Wright. An optical neural network using less than 1 photon per multiplication. Nat. Commun., 13, 123(2022).

[18] K. Tang, J. Chen, H. Jiang. Optical computing powers graph neural networks. Appl. Opt., 61, 10471-10477(2022).

[19] F. Duport, B. Schneider, A. Smerieri. All-optical reservoir computing. Opt. Express, 20, 22783-22795(2012).

[20] M. Nakajima, K. Tanaka, T. Hashimoto. Scalable reservoir computing on coherent linear photonic processor. Commun. Phys., 4, 20(2021).

[21] D. Pierangeli, G. Marcucci, C. Conti. Photonic extreme learning machine by free-space optical propagation. Photon. Res., 9, 1446-1454(2021).

[22] A. Momeni, R. Fleury. Electromagnetic wave-based extreme deep learning with nonlinear time-floquet entanglement. Nat. Commun., 13, 2651(2022).

[23] M. Rafayelyan, J. Dong, Y. Tan. Large-scale optical reservoir computing for spatiotemporal chaotic systems prediction. Phys. Rev. X, 10, 041037(2020).

[24] D. Pierangeli, C. Conti. Single-shot polarimetry of vector beams by supervised learning. Nat. Commun., 14, 1831(2023).

[25] W. Tress, M. Yavari, K. Domanski. Interpretation and evolution of open-circuit voltage, recombination, ideality factor and subgap defect states during reversible light-soaking and irreversible degradation of perovskite solar cells. Energy Environ. Sci., 11, 151-165(2018).

[26] F. Ma, Y. Zhao, Z. Qu. Developments of highly efficient perovskite solar cells. Acc. Mater. Res., 4, 716-725(2023).

[27] M. T. Hoang, Y. Yang, N. D. Pham. Ecofriendly solution processing of perovskite solar cells using water. J. Phys. Chem. Lett., 15, 6880-6889(2024).

[28] R. Garai, B. Sharma, M. A. Afroz. High-efficiency semitransparent perovskite solar cells enabled by controlling the crystallization of ultrathin films. ACS Energy Lett., 9, 2936-2943(2024).

[29] N. Cheng, Z. Yu, W. Li. Highly efficient perovskite solar cells employing SnO₂ electron transporting layer derived from a tin oxalate precursor solution. J. Power Sources, 544, 231871(2022).

[30] W. Shockley, H. J. Queisser. Detailed balance limit of efficiency of p-n junction solar cells. J. Appl. Phys., 32, 510-519(1961).

[31] G.-B. Huang, Q.-Y. Zhu, C.-K. Siew. Extreme learning machine: theory and applications. Neurocomputing, 70, 489-519(2006).

[32] C. M. Valensise, I. Grecco, D. Pierangeli. Large-scale photonic natural language processing. Photon. Res., 10, 2846-2853(2022).

[33] L. Liang, T. Ma, Z. Chen. Patterning technologies for metal halide perovskites: a review. Adv. Mater. Technol., 8, 2200419(2023).

[34] Y. H. Lee, J. Y. Park, P. Niu. One-step solution patterning for two dimensional perovskite nanoplate arrays. ACS Nano, 17, 13840-13850(2023).

[35] J. Harwell, J. Burch, A. Fikouras. Patterning multicolor hybrid perovskite films via top-down lithography. ACS Nano, 13, 3823-3829(2019).

[36] Y. Rong, Y. Ming, W. Ji. Toward industrial-scale production of perovskite solar cells: screen printing, slot-die coating, and emerging techniques. J. Phys. Chem. Lett., 9, 2707-2713(2018).

[37] A. Farooq, M. R. Khan, T. Abzieher. Photodegradation of triplecation perovskite solar cells: the role of spectrum and bias conditions. ACS Appl. Energy Mater., 4, 3083-3092(2021).

[38] R. Sharma, A. Sharma, S. Agarwal. Stability and efficiency issues, solutions and advancements in perovskite solar cells: a review. Sol. Energy, 244, 516-535(2022).

[39] L. Duan, D. Walter, N. Chang. Stability challenges for the commercialization of perovskite–silicon tandem solar cells. Nat. Rev. Mater., 8, 261-281(2023).

[40] T. A. Chowdhury, M. A. B. Zafar, M. Sajjad-Ul Islam. Stability of perovskite solar cells: issues and prospects. RSC Adv., 13, 1787-1810(2023).

[41] C. Ballif, F.-J. Haug, M. Boccard. Status and perspectives of crystalline silicon photovoltaics in research and industry. Nat. Rev. Mater., 7, 597-616(2022).

[42] P. Holzhey, M. Prettl, S. Collavini. Toward commercialization with lightweight, flexible perovskite solar cells for residential photovoltaics. Joule, 7, 257-271(2023).

[43] E. Lopez-Fraguas, B. Arredondo, C. Vega-Colado. Visible light communication system using an organic emitter and a perovskite photodetector. Organ. Electron., 73, 292-298(2019).

[44] T. Chen, P. B. Catrysse, A. El Gamal. How small should pixel size be?. Proc. SPIE, 3965, 451-459(2000).

[45] P. L. Reu, W. Sweatt, T. Miller. Camera system resolution and its influence on digital image correlation. Exp. Mech., 55, 9-25(2015).

[46] https://doi.org/10.17630/4da40306-655e-4ad1-982d-87827f97b2cd. https://doi.org/10.17630/4da40306-655e-4ad1-982d-87827f97b2cd

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Kaicheng Zhang, Jonathon Harwell, Davide Pierangeli, Claudio Conti, Andrea Di Falco, "Optical neural networks based on perovskite solar cells," Photonics Res. 13, 382 (2025)

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Paper Information

Category: Optical Devices

Received: Sep. 19, 2024

Accepted: Nov. 22, 2024

Published Online: Jan. 24, 2025

The Author Email: Andrea Di Falco (adf10@st-andrews.ac.uk)

DOI:10.1364/PRJ.542564

CSTR:32188.14.PRJ.542564

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology