Issue

Vol. 17 (2025)

Photonic Chip Based on Ultrafast Laser-Induced Reversible Phase Change for Convolutional Neural Network

https://doi.org/10.1007/s40820-025-01693-5
Jiawang Xie
State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, People’s Republic of China
Jianfeng Yan
State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, People’s Republic of China
Haoze Han
State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, People’s Republic of China
Yuzhi Zhao
State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, People’s Republic of China
Ma Luo
State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, People’s Republic of China
Jiaqun Li
State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, People’s Republic of China
Heng Guo
State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, People’s Republic of China
Ming Qiao
State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, People’s Republic of China

Corresponding Author: Jianfeng Yan

yanjianfeng@tsinghua.edu.cn

Nano-Micro Letters, Vol. 17 (2025), Article Number: 179

Abstract

Photonic computing has emerged as a promising technology for the ever-increasing computational demands of machine learning and artificial intelligence. Due to the advantages in computing speed, integrated photonic chips have attracted wide research attention on performing convolutional neural network algorithm. Programmable photonic chips are vital for achieving practical applications of photonic computing. Herein, a programmable photonic chip based on ultrafast laser-induced phase change is fabricated for photonic computing. Through designing the ultrafast laser pulses, the Sb film integrated into photonic waveguides can be reversibly switched between crystalline and amorphous phase, resulting in a large contrast in refractive index and extinction coefficient. As a consequence, the light transmission of waveguides can be switched between write and erase states. To determine the phase change time, the transient laser-induced phase change dynamics of Sb film are revealed at atomic scale, and the time-resolved transient reflectivity is measured. Based on the integrated photonic chip, photonic convolutional neural networks are built to implement machine learning algorithm, and images recognition task is achieved. This work paves a route for fabricating programmable photonic chips by designed ultrafast laser, which will facilitate the application of photonic computing in artificial intelligence.


Highlights:
1 Programmable photonic chips based on ultrafast laser-induced phase change is fabricated for photonic computing.
2 Based on the integrated photonic chip, photonic convolutional neural networks are built to implement machine learning algorithm, and images recognition task is achieved.
3 The transient laser-induced phase change dynamics of Sb film are revealed at atomic scale, and the phase change time is measured.

Keywords

Photonic chip Ultrafast laser Phase change Convolutional neural network
Xie, Jiawang, Jianfeng Yan, Haoze Han, Yuzhi Zhao, Ma Luo, Jiaqun Li, Heng Guo, and Ming Qiao. 2025. “Photonic Chip Based on Ultrafast Laser-Induced Reversible Phase Change for Convolutional Neural Network”. Nano-Micro Letters 17 (March):179. https://doi.org/10.1007/s40820-025-01693-5.
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
References
  1. D.R. Solli, B. Jalali, Analog optical computing. Nat. Photonics 9, 704–706 (2015). https://doi.org/10.1038/nphoton.2015.208
  2. B.J. Shastri, A.N. Tait, T. Ferreira de Lima, W.H.P. Pernice, H. Bhaskaran et al., Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics 15, 102–114 (2021). https://doi.org/10.1038/s41566-020-00754-y
  3. J. Xie, Y. Zhao, D. Zhu, J. Yan, J. Li et al., A machine learning-combined flexible sensor for tactile detection and voice recognition. ACS Appl. Mater. Interfaces 15, 12551–12559 (2023). https://doi.org/10.1021/acsami.2c22287
  4. T. Fu, Y. Zang, Y. Huang, Z. Du, H. Huang et al., Photonic machine learning with on-chip diffractive optics. Nat. Commun. 14, 70 (2023). https://doi.org/10.1038/s41467-022-35772-7
  5. T. Li, N. Yang, Y. Xiao, Y. Liu, X. Pan et al., Virus detection light diffraction fingerprints for biological applications. Sci. Adv. 10, eadl3466 (2024). https://doi.org/10.1126/sciadv.adl3466
  6. N. Yang, W. Song, Y. Xiao, M. Xia, L. Xiao et al., Minimum minutes machine-learning microfluidic microbe monitoring method (M7). ACS Nano 18, 4862–4870 (2024). https://doi.org/10.1021/acsnano.3c09733
  7. Y. Dong, W. An, Z. Wang, D. Zhang, An artificial intelligence-assisted flexible and wearable mechanoluminescent strain sensor system. Nano-Micro Lett. 17, 62 (2024). https://doi.org/10.1007/s40820-024-01572-5
  8. R. Barends, J. Kelly, A. Megrant, A. Veitia, D. Sank et al., Superconducting quantum circuits at the surface code threshold for fault tolerance. Nature 508, 500–503 (2014). https://doi.org/10.1038/nature13171
  9. D. Bluvstein, S.J. Evered, A.A. Geim, S.H. Li, H. Zhou et al., Logical quantum processor based on reconfigurable atom arrays. Nature 626, 58–65 (2024). https://doi.org/10.1038/s41586-023-06927-3
  10. Y. van de Burgt, A. Melianas, S.T. Keene, G. Malliaras, A. Salleo, Organic electronics for neuromorphic computing. Nat. Electron. 1, 386–397 (2018). https://doi.org/10.1038/s41928-018-0103-3
  11. D. Marković, A. Mizrahi, D. Querlioz, J. Grollier, Physics for neuromorphic computing. Nat. Rev. Phys. 2, 499–510 (2020). https://doi.org/10.1038/s42254-020-0208-2
  12. K. Roy, A. Jaiswal, P. Panda, Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 607–617 (2019). https://doi.org/10.1038/s41586-019-1677-2
  13. X. Chen, T. Wang, J. Shi, W. Lv, Y. Han et al., A novel artificial neuron-like gas sensor constructed from CuS quantum dots/Bi2S3 nanosheets. Nano-Micro Lett. 14, 8 (2021). https://doi.org/10.1007/s40820-021-00740-1
  14. H. Zhou, J. Dong, J. Cheng, W. Dong, C. Huang et al., Photonic matrix multiplication lights up photonic accelerator and beyond. Light Sci. Appl. 11, 30 (2022). https://doi.org/10.1038/s41377-022-00717-8
  15. Y. Shen, N.C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones et al., Deep learning with coherent nanophotonic circuits. Nat. Photonics 11, 441–446 (2017). https://doi.org/10.1038/nphoton.2017.93
  16. Z. Ying, C. Feng, Z. Zhao, S. Dhar, H. Dalir et al., Electronic-photonic arithmetic logic unit for high-speed computing. Nat. Commun. 11, 2154 (2020). https://doi.org/10.1038/s41467-020-16057-3
  17. T. Dai, A. Ma, J. Mao, Y. Ao, X. Jia et al., A programmable topological photonic chip. Nat. Mater. 23, 928–936 (2024). https://doi.org/10.1038/s41563-024-01904-1
  18. J. Hu, H. Qian, S. Han, P. Zhang, Y. Lu, Light-activated virtual sensor array with machine learning for non-invasive diagnosis of coronary heart disease. Nano-Micro Lett. 16, 274 (2024). https://doi.org/10.1007/s40820-024-01481-7
  19. C. Ríos, N. Youngblood, Z. Cheng, M. Le Gallo, W.H.P. Pernice et al., In-memory computing on a photonic platform. Sci. Adv. 5, eaau5759 (2019). https://doi.org/10.1126/sciadv.aau5759
  20. D. Pérez-López, A. Gutierrez, D. Sánchez, A. López-Hernández, M. Gutierrez et al., General-purpose programmable photonic processor for advanced radiofrequency applications. Nat. Commun. 15, 1563 (2024). https://doi.org/10.1038/s41467-024-45888-7
  21. B. Wang, Y. Li, M. Zhou, Y. Han, M. Zhang et al., Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence. Nat. Commun. 14, 1341 (2023). https://doi.org/10.1038/s41467-023-36017-x
  22. Z. Cheng, C. Ríos, N. Youngblood, C. David Wright, W.H.P. Pernice et al., Device-level photonic memories and logic applications using phase-change materials. Adv. Mater. 30, 1802435(2018). https://doi.org/10.1002/adma.201802435
  23. Z. Fang, R. Chen, J. Zheng, A.I. Khan, K.M. Neilson et al., Ultra-low-energy programmable non-volatile silicon photonics based on phase-change materials with graphene heaters. Nat. Nanotechnol. 17, 842–848 (2022). https://doi.org/10.1038/s41565-022-01153-w
  24. Y. Zhang, J.B. Chou, J. Li, H. Li, Q. Du et al., Broadband transparent optical phase change materials for high-performance nonvolatile photonics. Nat. Commun. 10, 4279 (2019). https://doi.org/10.1038/s41467-019-12196-4
  25. P. Dong, Y.-K. Chen, G.-H. Duan, D.T. Neilson, Silicon photonic devices and integrated circuits. Nanophotonics 3, 215–228 (2014). https://doi.org/10.1515/nanoph-2013-0023
  26. W. Bogaerts, D. Pérez, J. Capmany, D.A.B. Miller, J. Poon et al., Programmable photonic circuits. Nature 586, 207–216 (2020). https://doi.org/10.1038/s41586-020-2764-0
  27. X. Wang, P. Xie, B. Chen, X. Zhang, Chip-based high-dimensional optical neural network. Nano-Micro Lett. 14, 221 (2022). https://doi.org/10.1007/s40820-022-00957-8
  28. J. Feldmann, N. Youngblood, C.D. Wright, H. Bhaskaran, W.P. Pernice, All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208–214 (2019). https://doi.org/10.1038/s41586-019-1157-8
  29. C. Wu, H. Yu, S. Lee, R. Peng, I. Takeuchi et al., Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network. Nat. Commun. 12, 96 (2021). https://doi.org/10.1038/s41467-020-20365-z
  30. H.H. Zhu, J. Zou, H. Zhang, Y.Z. Shi, S.B. Luo et al., Space-efficient optical computing with an integrated chip diffractive neural network. Nat. Commun. 13, 1044 (2022). https://doi.org/10.1038/s41467-022-28702-0
  31. L.S. Madsen, F. Laudenbach, M.F. Askarani, F. Rortais, T. Vincent et al., Quantum computational advantage with a programmable photonic processor. Nature 606, 75–81 (2022). https://doi.org/10.1038/s41586-022-04725-x
  32. D. Van Thourhout, J. Roels, Optomechanical device actuation through the optical gradient force. Nat. Photonics 4, 211–217 (2010). https://doi.org/10.1038/nphoton.2010.72
  33. M.R. Watts, J. Sun, C. DeRose, D.C. Trotter, R.W. Young et al., Adiabatic thermo-optic Mach-Zehnder switch. Opt. Lett. 38, 733–735 (2013). https://doi.org/10.1364/OL.38.000733
  34. R. Chen, Z. Fang, J.E. Fröch, P. Xu, J. Zheng et al., Broadband nonvolatile electrically controlled programmable units in silicon photonics. ACS Photonics 9, 2142–2150 (2022). https://doi.org/10.1021/acsphotonics.2c00452
  35. C. Qiu, W. Gao, R. Soref, J.T. Robinson, Q. Xu, Reconfigurable electro-optical directed-logic circuit using carrier-depletion micro-ring resonators. Opt. Lett. 39, 6767–6770 (2014). https://doi.org/10.1364/OL.39.006767
  36. R. Chen, Z. Fang, C. Perez, F. Miller, K. Kumari et al., Non-volatile electrically programmable integrated photonics with a 5-bit operation. Nat. Commun. 14, 3465 (2023). https://doi.org/10.1038/s41467-023-39180-3
  37. W. Zhang, Fabrication and integration of photonic devices for phase-change memory and neuromorphic computing. Int. J. Extrem. Manuf. 6, 022001 (2024). https://doi.org/10.1088/2631-7990/ad1575
  38. S. Yu, W. Liu, S.-J. Tao, Z.-P. Li, Y.-T. Wang et al., A von-Neumann-like photonic processor and its application in studying quantum signature of chaos. Light Sci. Appl. 13, 74 (2024). https://doi.org/10.1038/s41377-024-01413-5
  39. J. Zheng, Z. Fang, C. Wu, S. Zhu, P. Xu et al., Nonvolatile electrically reconfigurable integrated photonic switch enabled by a silicon PIN diode heater. Adv. Mater. 32, e2001218 (2020). https://doi.org/10.1002/adma.202001218
  40. M. Delaney, I. Zeimpekis, H. Du, X. Yan, M. Banakar et al., Nonvolatile programmable silicon photonics using an ultralow-loss Sb2Se3 phase change material. Sci. Adv. 7, eabg3500 (2021). https://doi.org/10.1126/sciadv.abg3500
  41. H. Zhou, S. Li, K.-W. Ang, Y.-W. Zhang, Recent advances in in-memory computing: exploring memristor and memtransistor arrays with 2D materials. Nano-Micro Lett. 16, 121 (2024). https://doi.org/10.1007/s40820-024-01335-2
  42. X. Chen, Y. Xue, Y. Sun, J. Shen, S. Song et al., Neuromorphic photonic memory devices using ultrafast, non-volatile phase-change materials. Adv. Mater. 35, 2203909 (2023). https://doi.org/10.1002/adma.202203909
  43. S. Kim, G.W. Burr, W. Kim, S.-W. Nam, Phase-change memory cycling endurance. MRS Bull. 44, 710–714 (2019). https://doi.org/10.1557/mrs.2019.205
  44. Y. Qu, Q. Li, L. Cai, M. Pan, P. Ghosh et al., Thermal camouflage based on the phase-changing material GST. Light. Sci. Appl. 7, 26 (2018). https://doi.org/10.1038/s41377-018-0038-5
  45. T. Cao, R. Wang, R.E. Simpson, G. Li, Photonic Ge-Sb-Te phase change metamaterials and their applications. Prog. Quantum Electron. 74, 100299 (2020). https://doi.org/10.1016/j.pquantelec.2020.100299
  46. M. Delaney, I. Zeimpekis, D. Lawson, D.W. Hewak, O.L. Muskens, A new family of ultralow loss reversible phase-change materials for photonic integrated circuits: Sb2S3 and Sb2Se3. Adv. Funct. Mater. 30, 2002447 (2020). https://doi.org/10.1002/adfm.202002447
  47. H. Guo, J. Yan, L. Jiang, S. Deng, X. Lin et al., Femtosecond laser Bessel beam fabrication of a supercapacitor with a nanoscale electrode gap for high specific volumetric capacitance. ACS Appl. Mater. Interfaces 14, 39220–39229 (2022). https://doi.org/10.1021/acsami.2c10037
  48. M. Qiao, H. Wang, H. Lu, S. Li, J. Yan et al., Micro/nano processing of natural silk fibers with near-field enhanced ultrafast laser. Sci. China Mater. 63, 1300–1309 (2020). https://doi.org/10.1007/s40843-020-1351-3
  49. D. Zhu, J. Xie, J. Yan, G. He, M. Qiao, Ultrafast laser plasmonic fabrication of nanocrystals by molecule modulation for photoresponse multifunctional structures. Adv. Mater. 35, 2211983 (2023). https://doi.org/10.1002/adma.202211983
  50. J. Xie, M. Qiao, D. Zhu, J. Yan, S. Deng et al., Laser induced coffee-ring structure through solid-liquid transition for color printing. Small 19, e2205696 (2023). https://doi.org/10.1002/smll.202205696
  51. J. Zhang, D. Zhu, J. Yan, C.-A. Wang, Strong metal-support interactions induced by an ultrafast laser. Nat. Commun. 12, 6665 (2021). https://doi.org/10.1038/s41467-021-27000-5
  52. M. Salinga, B. Kersting, I. Ronneberger, V.P. Jonnalagadda, X.T. Vu et al., Monatomic phase change memory. Nat. Mater. 17, 681–685 (2018). https://doi.org/10.1038/s41563-018-0110-9
  53. Z. Cheng, T. Milne, P. Salter, J.S. Kim, S. Humphrey et al., Antimony thin films demonstrate programmable optical nonlinearity. Sci. Adv. 7, eabd7097 (2021). https://doi.org/10.1126/sciadv.abd7097
  54. S. Aggarwal, T. Milne, N. Farmakidis, J. Feldmann, X. Li et al., Antimony as a programmable element in integrated nanophotonics. Nano Lett. 22, 3532–3538 (2022). https://doi.org/10.1021/acs.nanolett.1c04286
  55. D.S. Ivanov, L.V. Zhigilei, Combined atomistic-continuum modeling of short-pulse laser melting and disintegration of metal films. Phys. Rev. B 68, 064114 (2003). https://doi.org/10.1103/physrevb.68.064114
  56. J. Xie, J. Yan, D. Zhu, G. He, Atomic-level insight into the formation of subsurface dislocation layer and its effect on mechanical properties during ultrafast laser micro/nano fabrication. Adv. Funct. Mater. 32, 2270088 (2022). https://doi.org/10.1002/adfm.202270088
  57. D.K. Belashchenko, Modeling liquid antimony by means of molecular dynamics. Russ. J. Phys. Chem. 93, 1093–1105 (2019). https://doi.org/10.1134/s0036024419060062
  58. J. Ji, X. Song, J. Liu, Z. Yan, C. Huo et al., Two-dimensional antimonene single crystals grown by van der Waals epitaxy. Nat. Commun. 7, 13352 (2016). https://doi.org/10.1038/ncomms13352
Read More
Author biographies are not available.
Download this PDF file
PDF SUPPLEMENTARY FILE
Statistic
Read Counter : 715 Download : 536