Abstract

The interaction between light and matter during laser machining is particularly challenging to model via analytical approaches. Here, we show the application of a statistical approach that constructs a model of the machining process directly from experimental images of the laser machined sample, and hence negating the need for understanding the underlying physical processes. Specifically, we use a neural network to transform a laser spatial intensity profile into an equivalent scanning electron microscope image of the laser-machined target. This approach enables the simulated visualization of the result of laser machining with any laser spatial intensity profile, and hence demonstrates predictive capabilities for laser machining. The trained neural network was found to have encoded functionality that was consistent with the laws of diffraction, hence showing the potential of this approach for discovering physical laws directly from experimental data.

Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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References

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2017 (4)

2016 (2)

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

R. Bruck, K. Vynck, P. Lalanne, B. Mills, D. J. Thomson, G. Z. Mashanovich, G. T. Reed, and O. L. Muskens, “All-optical spatial light modulator for reconfigurable silicon photonic circuits,” Optica 3(4), 396–402 (2016).
[Crossref]

2015 (2)

2014 (2)

L. Gong, Y. Ren, W. Liu, M. Wang, M. Zhong, Z. Wang, and Y. Li, “Generation of cylindrically polarized vector vortex beams with digital micromirror device,” J. Appl. Phys. 116(18), 183105 (2014).
[Crossref]

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

2013 (1)

B. Mills, M. Feinaeugle, C. L. Sones, N. Rizvi, and R. W. Eason, “Sub-micron-scale femtosecond laser ablation using a digital micromirror device,” J. Micromech. Microeng. 23(3), 035005 (2013).
[Crossref]

2011 (1)

2010 (1)

2007 (1)

S. Amoruso, R. Bruzzese, X. Wang, N. N. Nedialkov, and P. A. Atanasov, “Femtosecond laser ablation of nickel in vacuum,” J. Phys. D Appl. Phys. 40(2), 331–340 (2007).
[Crossref]

2006 (1)

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
[Crossref] [PubMed]

2005 (2)

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

2003 (1)

J. K. Chen and J. E. Beraun, “Modelling of ultrashort laser ablation of gold films in vacuum,” J. Opt. A, Pure Appl. Opt. 5(3), 168–173 (2003).
[Crossref]

2002 (2)

H. O. Jeschke, M. E. Garcia, M. Lenzner, J. Bonse, J. Krüger, and W. Kautek, “Laser ablation thresholds of silicon for different pulse durations: theory and experiment,” Appl. Surf. Sci. 197–198, 839–844 (2002).
[Crossref]

E. G. Gamaly, A. V. Rode, B. Luther-Davies, and V. T. Tikhonchuk, “Ablation of solids by femtosecond lasers: Ablation mechanism and ablation thresholds for metals and dielectrics,” Phys. Plasmas 9(3), 949–957 (2002).
[Crossref]

1997 (1)

S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Trans. Neural Netw. 8(1), 98–113 (1997).
[Crossref] [PubMed]

1996 (1)

B. N. Chichkov, C. Momma, S. Nolte, F. V. Alvensleben, and A. Tünnermann, “Femtosecond, picosecond and nanosecond laser ablation of solids,” Appl. Phys., A Mater. Sci. Process. 63(2), 109–115 (1996).
[Crossref]

1995 (1)

P. P. Pronko, S. K. Dutta, J. Squier, J. V. Rudd, D. Du, and G. Mourou, “Machining of sub-micron holes using a femtosecond laser at 800 nm,” Opt. Commun. 114(1–2), 106–110 (1995).
[Crossref]

1991 (1)

D. F. Specht, “A general regression neural network,” IEEE Trans. Neural Netw. 2(6), 568–576 (1991).
[Crossref] [PubMed]

1990 (1)

L. K. Hansen and P. Salamon, “Neural network ensembles,” IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990).
[Crossref]

1989 (1)

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw. 2(5), 359–366 (1989).
[Crossref]

1986 (1)

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature 323(6088), 533–536 (1986).
[Crossref]

Akata, Z.

S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,” https://arXiv:1605.05396 (2016).

Alvensleben, F. V.

B. N. Chichkov, C. Momma, S. Nolte, F. V. Alvensleben, and A. Tünnermann, “Femtosecond, picosecond and nanosecond laser ablation of solids,” Appl. Phys., A Mater. Sci. Process. 63(2), 109–115 (1996).
[Crossref]

Amer, M. S.

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

Amoruso, S.

S. Amoruso, R. Bruzzese, X. Wang, N. N. Nedialkov, and P. A. Atanasov, “Femtosecond laser ablation of nickel in vacuum,” J. Phys. D Appl. Phys. 40(2), 331–340 (2007).
[Crossref]

Anisimov, S. I.

B. Rethfeld, D. S. Ivanov, M. E. Garcia, and S. I. Anisimov, “Modelling ultrafast laser ablation,” J. Phys. D Appl. Phys. 50(19), 193001 (2017).
[Crossref]

Atanasov, P. A.

S. Amoruso, R. Bruzzese, X. Wang, N. N. Nedialkov, and P. A. Atanasov, “Femtosecond laser ablation of nickel in vacuum,” J. Phys. D Appl. Phys. 40(2), 331–340 (2007).
[Crossref]

Back, A. D.

S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Trans. Neural Netw. 8(1), 98–113 (1997).
[Crossref] [PubMed]

Beraun, J. E.

J. K. Chen and J. E. Beraun, “Modelling of ultrashort laser ablation of gold films in vacuum,” J. Opt. A, Pure Appl. Opt. 5(3), 168–173 (2003).
[Crossref]

Bonse, J.

H. O. Jeschke, M. E. Garcia, M. Lenzner, J. Bonse, J. Krüger, and W. Kautek, “Laser ablation thresholds of silicon for different pulse durations: theory and experiment,” Appl. Surf. Sci. 197–198, 839–844 (2002).
[Crossref]

Booth, M. J.

Bruck, R.

Bruzzese, R.

S. Amoruso, R. Bruzzese, X. Wang, N. N. Nedialkov, and P. A. Atanasov, “Femtosecond laser ablation of nickel in vacuum,” J. Phys. D Appl. Phys. 40(2), 331–340 (2007).
[Crossref]

Cai, Z.

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Chen, J. K.

J. K. Chen and J. E. Beraun, “Modelling of ultrashort laser ablation of gold films in vacuum,” J. Opt. A, Pure Appl. Opt. 5(3), 168–173 (2003).
[Crossref]

Chichkov, B. N.

B. N. Chichkov, C. Momma, S. Nolte, F. V. Alvensleben, and A. Tünnermann, “Femtosecond, picosecond and nanosecond laser ablation of solids,” Appl. Phys., A Mater. Sci. Process. 63(2), 109–115 (1996).
[Crossref]

Chilkoti, A.

Chu, J.

L. Yang, D. Qian, C. Xin, Z. Hu, S. Ji, D. Wu, Y. Hu, J. Li, W. Huang, and J. Chu, “Two-photon polymerization of microstructures by a non-diffraction multifoci pattern generated from a superposed Bessel beam,” Opt. Lett. 42(4), 743–746 (2017).
[Crossref] [PubMed]

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Clark, R. L.

Cumming, B. P.

Darrell, T.

D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2536–2544.

Donahue, J.

D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2536–2544.

Dosser, L. R.

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

Du, D.

P. P. Pronko, S. K. Dutta, J. Squier, J. V. Rudd, D. Du, and G. Mourou, “Machining of sub-micron holes using a femtosecond laser at 800 nm,” Opt. Commun. 114(1–2), 106–110 (1995).
[Crossref]

Du, W.

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Dutta, S. K.

P. P. Pronko, S. K. Dutta, J. Squier, J. V. Rudd, D. Du, and G. Mourou, “Machining of sub-micron holes using a femtosecond laser at 800 nm,” Opt. Commun. 114(1–2), 106–110 (1995).
[Crossref]

Eason, R. W.

Efros, A. A.

D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2536–2544.

El-Ashry, M. A.

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

Fei-Fei, L.

A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1725–1732.
[Crossref]

Feinaeugle, M.

Gamaly, E. G.

E. G. Gamaly, A. V. Rode, B. Luther-Davies, and V. T. Tikhonchuk, “Ablation of solids by femtosecond lasers: Ablation mechanism and ablation thresholds for metals and dielectrics,” Phys. Plasmas 9(3), 949–957 (2002).
[Crossref]

Garcia, M. E.

B. Rethfeld, D. S. Ivanov, M. E. Garcia, and S. I. Anisimov, “Modelling ultrafast laser ablation,” J. Phys. D Appl. Phys. 50(19), 193001 (2017).
[Crossref]

H. O. Jeschke, M. E. Garcia, M. Lenzner, J. Bonse, J. Krüger, and W. Kautek, “Laser ablation thresholds of silicon for different pulse durations: theory and experiment,” Appl. Surf. Sci. 197–198, 839–844 (2002).
[Crossref]

Giles, C. L.

S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Trans. Neural Netw. 8(1), 98–113 (1997).
[Crossref] [PubMed]

Gong, L.

Y.-X. Ren, R.-D. Lu, and L. Gong, “Tailoring light with a digital micromirror device,” Ann. Phys. 527(7–8), 447–470 (2015).
[Crossref]

L. Gong, Y. Ren, W. Liu, M. Wang, M. Zhong, Z. Wang, and Y. Li, “Generation of cylindrically polarized vector vortex beams with digital micromirror device,” J. Appl. Phys. 116(18), 183105 (2014).
[Crossref]

Göröcs, Z.

Grant-Jacob, J. A.

Gu, M.

Günaydin, H.

Hansen, L. K.

L. K. Hansen and P. Salamon, “Neural network ensembles,” IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990).
[Crossref]

Heath, D. J.

Hill, R. T.

Hinton, G.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

Hinton, G. E.

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
[Crossref] [PubMed]

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature 323(6088), 533–536 (1986).
[Crossref]

Hix, K. E.

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

Hornik, K.

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw. 2(5), 359–366 (1989).
[Crossref]

Hu, Y.

L. Yang, D. Qian, C. Xin, Z. Hu, S. Ji, D. Wu, Y. Hu, J. Li, W. Huang, and J. Chu, “Two-photon polymerization of microstructures by a non-diffraction multifoci pattern generated from a superposed Bessel beam,” Opt. Lett. 42(4), 743–746 (2017).
[Crossref] [PubMed]

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Hu, Z.

Huang, W.

Hucknall, A.

Irwin, B.

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

Ivanov, D. S.

B. Rethfeld, D. S. Ivanov, M. E. Garcia, and S. I. Anisimov, “Modelling ultrafast laser ablation,” J. Phys. D Appl. Phys. 50(19), 193001 (2017).
[Crossref]

Jenness, N. J.

Jesacher, A.

Jeschke, H. O.

H. O. Jeschke, M. E. Garcia, M. Lenzner, J. Bonse, J. Krüger, and W. Kautek, “Laser ablation thresholds of silicon for different pulse durations: theory and experiment,” Appl. Surf. Sci. 197–198, 839–844 (2002).
[Crossref]

Ji, S.

Karpathy, A.

A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1725–1732.
[Crossref]

Kautek, W.

H. O. Jeschke, M. E. Garcia, M. Lenzner, J. Bonse, J. Krüger, and W. Kautek, “Laser ablation thresholds of silicon for different pulse durations: theory and experiment,” Appl. Surf. Sci. 197–198, 839–844 (2002).
[Crossref]

Krahenbuhl, P.

D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2536–2544.

Krizhevsky, A.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

Krüger, J.

H. O. Jeschke, M. E. Garcia, M. Lenzner, J. Bonse, J. Krüger, and W. Kautek, “Laser ablation thresholds of silicon for different pulse durations: theory and experiment,” Appl. Surf. Sci. 197–198, 839–844 (2002).
[Crossref]

Lalanne, P.

Lao, Z.

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Lawrence, S.

S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Trans. Neural Netw. 8(1), 98–113 (1997).
[Crossref] [PubMed]

Lee, H.

S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,” https://arXiv:1605.05396 (2016).

Lenzner, M.

H. O. Jeschke, M. E. Garcia, M. Lenzner, J. Bonse, J. Krüger, and W. Kautek, “Laser ablation thresholds of silicon for different pulse durations: theory and experiment,” Appl. Surf. Sci. 197–198, 839–844 (2002).
[Crossref]

Leung, T.

A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1725–1732.
[Crossref]

Li, J.

L. Yang, D. Qian, C. Xin, Z. Hu, S. Ji, D. Wu, Y. Hu, J. Li, W. Huang, and J. Chu, “Two-photon polymerization of microstructures by a non-diffraction multifoci pattern generated from a superposed Bessel beam,” Opt. Lett. 42(4), 743–746 (2017).
[Crossref] [PubMed]

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Li, Y.

L. Gong, Y. Ren, W. Liu, M. Wang, M. Zhong, Z. Wang, and Y. Li, “Generation of cylindrically polarized vector vortex beams with digital micromirror device,” J. Appl. Phys. 116(18), 183105 (2014).
[Crossref]

Liu, W.

L. Gong, Y. Ren, W. Liu, M. Wang, M. Zhong, Z. Wang, and Y. Li, “Generation of cylindrically polarized vector vortex beams with digital micromirror device,” J. Appl. Phys. 116(18), 183105 (2014).
[Crossref]

Logeswaran, L.

S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,” https://arXiv:1605.05396 (2016).

Lu, R.-D.

Y.-X. Ren, R.-D. Lu, and L. Gong, “Tailoring light with a digital micromirror device,” Ann. Phys. 527(7–8), 447–470 (2015).
[Crossref]

Luther-Davies, B.

E. G. Gamaly, A. V. Rode, B. Luther-Davies, and V. T. Tikhonchuk, “Ablation of solids by femtosecond lasers: Ablation mechanism and ablation thresholds for metals and dielectrics,” Phys. Plasmas 9(3), 949–957 (2002).
[Crossref]

Maguire, J. F.

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

Mashanovich, G. Z.

Mills, B.

Momma, C.

B. N. Chichkov, C. Momma, S. Nolte, F. V. Alvensleben, and A. Tünnermann, “Femtosecond, picosecond and nanosecond laser ablation of solids,” Appl. Phys., A Mater. Sci. Process. 63(2), 109–115 (1996).
[Crossref]

Mourou, G.

P. P. Pronko, S. K. Dutta, J. Squier, J. V. Rudd, D. Du, and G. Mourou, “Machining of sub-micron holes using a femtosecond laser at 800 nm,” Opt. Commun. 114(1–2), 106–110 (1995).
[Crossref]

Muskens, O. L.

Nedialkov, N. N.

S. Amoruso, R. Bruzzese, X. Wang, N. N. Nedialkov, and P. A. Atanasov, “Femtosecond laser ablation of nickel in vacuum,” J. Phys. D Appl. Phys. 40(2), 331–340 (2007).
[Crossref]

Ni, J.

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Nolte, S.

B. N. Chichkov, C. Momma, S. Nolte, F. V. Alvensleben, and A. Tünnermann, “Femtosecond, picosecond and nanosecond laser ablation of solids,” Appl. Phys., A Mater. Sci. Process. 63(2), 109–115 (1996).
[Crossref]

Ozcan, A.

Pathak, D.

D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2536–2544.

Pronko, P. P.

P. P. Pronko, S. K. Dutta, J. Squier, J. V. Rudd, D. Du, and G. Mourou, “Machining of sub-micron holes using a femtosecond laser at 800 nm,” Opt. Commun. 114(1–2), 106–110 (1995).
[Crossref]

Qian, D.

Rao, S.

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Reed, G. T.

Reed, S.

S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,” https://arXiv:1605.05396 (2016).

Ren, Y.

L. Gong, Y. Ren, W. Liu, M. Wang, M. Zhong, Z. Wang, and Y. Li, “Generation of cylindrically polarized vector vortex beams with digital micromirror device,” J. Appl. Phys. 116(18), 183105 (2014).
[Crossref]

Ren, Y.-X.

Y.-X. Ren, R.-D. Lu, and L. Gong, “Tailoring light with a digital micromirror device,” Ann. Phys. 527(7–8), 447–470 (2015).
[Crossref]

Rethfeld, B.

B. Rethfeld, D. S. Ivanov, M. E. Garcia, and S. I. Anisimov, “Modelling ultrafast laser ablation,” J. Phys. D Appl. Phys. 50(19), 193001 (2017).
[Crossref]

Rivenson, Y.

Rizvi, N.

B. Mills, M. Feinaeugle, C. L. Sones, N. Rizvi, and R. W. Eason, “Sub-micron-scale femtosecond laser ablation using a digital micromirror device,” J. Micromech. Microeng. 23(3), 035005 (2013).
[Crossref]

Rode, A. V.

E. G. Gamaly, A. V. Rode, B. Luther-Davies, and V. T. Tikhonchuk, “Ablation of solids by femtosecond lasers: Ablation mechanism and ablation thresholds for metals and dielectrics,” Phys. Plasmas 9(3), 949–957 (2002).
[Crossref]

Rudd, J. V.

P. P. Pronko, S. K. Dutta, J. Squier, J. V. Rudd, D. Du, and G. Mourou, “Machining of sub-micron holes using a femtosecond laser at 800 nm,” Opt. Commun. 114(1–2), 106–110 (1995).
[Crossref]

Rumelhart, D. E.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature 323(6088), 533–536 (1986).
[Crossref]

Salakhutdinov, R.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

Salakhutdinov, R. R.

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
[Crossref] [PubMed]

Salamon, P.

L. K. Hansen and P. Salamon, “Neural network ensembles,” IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990).
[Crossref]

Schiele, B.

S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,” https://arXiv:1605.05396 (2016).

Shetty, S.

A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1725–1732.
[Crossref]

Sones, C. L.

B. Mills, M. Feinaeugle, C. L. Sones, N. Rizvi, and R. W. Eason, “Sub-micron-scale femtosecond laser ablation using a digital micromirror device,” J. Micromech. Microeng. 23(3), 035005 (2013).
[Crossref]

Specht, D. F.

D. F. Specht, “A general regression neural network,” IEEE Trans. Neural Netw. 2(6), 568–576 (1991).
[Crossref] [PubMed]

Squier, J.

P. P. Pronko, S. K. Dutta, J. Squier, J. V. Rudd, D. Du, and G. Mourou, “Machining of sub-micron holes using a femtosecond laser at 800 nm,” Opt. Commun. 114(1–2), 106–110 (1995).
[Crossref]

Srivastava, N.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

Stinchcombe, M.

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw. 2(5), 359–366 (1989).
[Crossref]

Sugioka, K.

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Sukthankar, R.

A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1725–1732.
[Crossref]

Sutskever, I.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

Thomson, D. J.

Tikhonchuk, V. T.

E. G. Gamaly, A. V. Rode, B. Luther-Davies, and V. T. Tikhonchuk, “Ablation of solids by femtosecond lasers: Ablation mechanism and ablation thresholds for metals and dielectrics,” Phys. Plasmas 9(3), 949–957 (2002).
[Crossref]

Toderici, G.

A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, “Large-scale video classification with convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2014), pp. 1725–1732.
[Crossref]

Tsoi, A. C.

S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Trans. Neural Netw. 8(1), 98–113 (1997).
[Crossref] [PubMed]

Tünnermann, A.

B. N. Chichkov, C. Momma, S. Nolte, F. V. Alvensleben, and A. Tünnermann, “Femtosecond, picosecond and nanosecond laser ablation of solids,” Appl. Phys., A Mater. Sci. Process. 63(2), 109–115 (1996).
[Crossref]

Vynck, K.

Wang, H.

Wang, M.

L. Gong, Y. Ren, W. Liu, M. Wang, M. Zhong, Z. Wang, and Y. Li, “Generation of cylindrically polarized vector vortex beams with digital micromirror device,” J. Appl. Phys. 116(18), 183105 (2014).
[Crossref]

Wang, X.

S. Amoruso, R. Bruzzese, X. Wang, N. N. Nedialkov, and P. A. Atanasov, “Femtosecond laser ablation of nickel in vacuum,” J. Phys. D Appl. Phys. 40(2), 331–340 (2007).
[Crossref]

Wang, Z.

L. Gong, Y. Ren, W. Liu, M. Wang, M. Zhong, Z. Wang, and Y. Li, “Generation of cylindrically polarized vector vortex beams with digital micromirror device,” J. Appl. Phys. 116(18), 183105 (2014).
[Crossref]

White, H.

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw. 2(5), 359–366 (1989).
[Crossref]

Williams, R. J.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature 323(6088), 533–536 (1986).
[Crossref]

Wilson, T.

Wu, D.

L. Yang, D. Qian, C. Xin, Z. Hu, S. Ji, D. Wu, Y. Hu, J. Li, W. Huang, and J. Chu, “Two-photon polymerization of microstructures by a non-diffraction multifoci pattern generated from a superposed Bessel beam,” Opt. Lett. 42(4), 743–746 (2017).
[Crossref] [PubMed]

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Wu, P.

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Xin, C.

Xu, B.

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Yan, X.

S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,” https://arXiv:1605.05396 (2016).

Yang, L.

Zhang, C.

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Zhang, Y.

Zhao, G.

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Zhong, M.

L. Gong, Y. Ren, W. Liu, M. Wang, M. Zhong, Z. Wang, and Y. Li, “Generation of cylindrically polarized vector vortex beams with digital micromirror device,” J. Appl. Phys. 116(18), 183105 (2014).
[Crossref]

Ann. Phys. (1)

Y.-X. Ren, R.-D. Lu, and L. Gong, “Tailoring light with a digital micromirror device,” Ann. Phys. 527(7–8), 447–470 (2015).
[Crossref]

Appl. Opt. (2)

Appl. Phys., A Mater. Sci. Process. (1)

B. N. Chichkov, C. Momma, S. Nolte, F. V. Alvensleben, and A. Tünnermann, “Femtosecond, picosecond and nanosecond laser ablation of solids,” Appl. Phys., A Mater. Sci. Process. 63(2), 109–115 (1996).
[Crossref]

Appl. Surf. Sci. (3)

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

M. S. Amer, M. A. El-Ashry, L. R. Dosser, K. E. Hix, J. F. Maguire, and B. Irwin, “Femtosecond versus nanosecond laser machining: comparison of induced stresses and structural changes in silicon wafers,” Appl. Surf. Sci. 242(1–2), 162–167 (2005).
[Crossref]

H. O. Jeschke, M. E. Garcia, M. Lenzner, J. Bonse, J. Krüger, and W. Kautek, “Laser ablation thresholds of silicon for different pulse durations: theory and experiment,” Appl. Surf. Sci. 197–198, 839–844 (2002).
[Crossref]

IEEE Trans. Neural Netw. (2)

D. F. Specht, “A general regression neural network,” IEEE Trans. Neural Netw. 2(6), 568–576 (1991).
[Crossref] [PubMed]

S. Lawrence, C. L. Giles, A. C. Tsoi, and A. D. Back, “Face recognition: A convolutional neural-network approach,” IEEE Trans. Neural Netw. 8(1), 98–113 (1997).
[Crossref] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

L. K. Hansen and P. Salamon, “Neural network ensembles,” IEEE Trans. Pattern Anal. Mach. Intell. 12(10), 993–1001 (1990).
[Crossref]

J. Appl. Phys. (1)

L. Gong, Y. Ren, W. Liu, M. Wang, M. Zhong, Z. Wang, and Y. Li, “Generation of cylindrically polarized vector vortex beams with digital micromirror device,” J. Appl. Phys. 116(18), 183105 (2014).
[Crossref]

J. Mach. Learn. Res. (1)

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” J. Mach. Learn. Res. 15(1), 1929–1958 (2014).

J. Micromech. Microeng. (1)

B. Mills, M. Feinaeugle, C. L. Sones, N. Rizvi, and R. W. Eason, “Sub-micron-scale femtosecond laser ablation using a digital micromirror device,” J. Micromech. Microeng. 23(3), 035005 (2013).
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J. Opt. A, Pure Appl. Opt. (1)

J. K. Chen and J. E. Beraun, “Modelling of ultrashort laser ablation of gold films in vacuum,” J. Opt. A, Pure Appl. Opt. 5(3), 168–173 (2003).
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J. Phys. D Appl. Phys. (2)

S. Amoruso, R. Bruzzese, X. Wang, N. N. Nedialkov, and P. A. Atanasov, “Femtosecond laser ablation of nickel in vacuum,” J. Phys. D Appl. Phys. 40(2), 331–340 (2007).
[Crossref]

B. Rethfeld, D. S. Ivanov, M. E. Garcia, and S. I. Anisimov, “Modelling ultrafast laser ablation,” J. Phys. D Appl. Phys. 50(19), 193001 (2017).
[Crossref]

Nature (1)

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature 323(6088), 533–536 (1986).
[Crossref]

Neural Netw. (1)

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw. 2(5), 359–366 (1989).
[Crossref]

Opt. Commun. (1)

P. P. Pronko, S. K. Dutta, J. Squier, J. V. Rudd, D. Du, and G. Mourou, “Machining of sub-micron holes using a femtosecond laser at 800 nm,” Opt. Commun. 114(1–2), 106–110 (1995).
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Opt. Express (2)

Opt. Lett. (1)

Optica (2)

Phys. Plasmas (1)

E. G. Gamaly, A. V. Rode, B. Luther-Davies, and V. T. Tikhonchuk, “Ablation of solids by femtosecond lasers: Ablation mechanism and ablation thresholds for metals and dielectrics,” Phys. Plasmas 9(3), 949–957 (2002).
[Crossref]

Sci. Rep. (1)

C. Zhang, Y. Hu, W. Du, P. Wu, S. Rao, Z. Cai, Z. Lao, B. Xu, J. Ni, J. Li, G. Zhao, D. Wu, J. Chu, and K. Sugioka, “Optimized holographic femtosecond laser patterning method towards rapid integration of high-quality functional devices in microchannels,” Sci. Rep. 6(1), 33281 (2016).
[Crossref] [PubMed]

Science (1)

G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science 313(5786), 504–507 (2006).
[Crossref] [PubMed]

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D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros, “Context encoders: Feature learning by inpainting,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 2536–2544.

P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” https://arXiv:1611.07004 (2017).
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Figures (6)

Fig. 1
Fig. 1 Procedure for creating an image transfer function that can turn a laser spatial intensity into an equivalent generated SEM image. A DMD was used for shaping the spatial intensity profile of single laser pulses that were then imaged onto the sample for laser machining, where the white and black pixels on the DMD patterns correspond to laser light and no laser light, respectively. Each DMD pattern was paired with the associated experimentally-measured SEM image, in order to form the training data set. Single DMD patterns were paired with either the associated experimental or generated SEM image and used as the input to the discriminator. The discriminator was trained to detect whether the input was an experimental or generated SEM image, and the generator was trained to convince the discriminator to judge generated SEM images as experimental. The overriding goal was the creation of the image transfer function.
Fig. 2
Fig. 2 Examples of DMD patterns and associated experimentally-measured SEM images from the training data set. Showing nine of the 172 data set pairs, corresponding to training data set numbers of 15, 24, 45, 74, 85, 104, 121, 144, and 161 for (a)-(i), respectively, where the decrease in image brightness was caused by the gradual degradation in the SEM titanium filament during the 5 hours of data capture. The DMD patterns were produced in order to provide a wide distribution of training data. The cGAN was trained on the training data set in order to create an image transfer function that could generate an equivalent SEM image from any input DMD pattern. In order to generate a realistic SEM image, the image transfer function needed to be consistent with the laws of diffraction, laser machining, and SEM imaging.
Fig. 3
Fig. 3 Demonstration of the effectiveness of the image transfer function for different training iterations. Showing DMD patterns corresponding to the letters (a) B and (b) X, with associated generated SEM images for image transfer functions for 1, 2, 5 and 400 cGAN training iterations. For comparison, the associated experimentally-measured SEM images are also shown. The DMD patterns and experimentally-measured SEM images were not part of the training data set, and hence this result shows the effectiveness of the image transfer function on unseen data. The generated SEM images provide a qualitative portrayal of the cGAN training convergence, showing that features, such as shadows and uneven edges, became obvious at different numbers of iterations. No further improvements were realised for iteration numbers greater than 400.
Fig. 4
Fig. 4 Demonstration of the effectiveness of the image transfer function for DMD patterns corresponding to periodic designs, for (a) above and (b) close to the resolution limit of the experimental setup, showing the DMD pattern, and the associated experimental and generated SEM images. These DMD patterns were not in the training data set, and hence this result provides experimental verification of the accuracy of the neural network for feature sizes close to the resolution limit of the experiment.
Fig. 5
Fig. 5 SEM images generated using the image transfer function from the trained cGAN, for DMD patterns consisting of lines, gaps and ring structures. Showing generated SEM images for projected line widths of (a) 250 nm, (b) 500 nm, (c) 1 µm, (d) 2 µm, (e) 3 µm and (f) 5 µm. For DMD patterns with a single vertical line, (c)-(f), show laser-machined structures with widths approximately proportional to the projected line width, while (a)-(b), indicate that the widths of the laser-machined structures do not decrease below a minimum size. For the DMD patterns with gaps and ring structures, (a)-(c), show the inability to resolve two adjacent laser-machined structures. These images indicate that the image transfer function from the trained cGAN contains encoding that appears consistent with the laws of diffraction. There are no associated experimental SEM images for these DMD patterns, hence demonstrating the predictive capabilities of this approach.
Fig. 6
Fig. 6 Reverse-engineering the image transfer function to evaluate the encoding of diffraction. Showing (a) a generated SEM image corresponding to a projected line width of 2 µm, (b) the same image converted to a high-contrast colour-map and averaged over the central 500 rows of pixels in the Y-axis to produce an averaged cross-section, and (c)-(d) the concatenation of averaged cross sections for projected line widths from 128 nm to 12 µm. In (c) the edges of the generated laser-machined structures (the purple and dark blue data regions) closely match the associated projected line width (white dotted lines) for values > 1 µm. However, as emphasized in (d) this relationship is not observed for projected line widths < 1 µm, where instead the widths of the laser-machined structures are observed to be always greater than the diffraction limit of the experimental setup (952 nm [37], white vertical lines).

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