Abstract

Resolution improvement through signal processing techniques for integrated circuit imaging is becoming more crucial as the rapid decrease in integrated circuit dimensions continues. Although there is a significant effort to push the limits of optical resolution for backside fault analysis through the use of solid immersion lenses, higher order laser beams, and beam apodization, signal processing techniques are required for additional improvement. In this work, we propose a sparse image reconstruction framework which couples overcomplete dictionary-based representation with a physics-based forward model to improve resolution and localization accuracy in high numerical aperture confocal microscopy systems for backside optical integrated circuit analysis. The effectiveness of the framework is demonstrated on experimental data.

© 2015 Optical Society of America

Full Article  |  PDF Article
OSA Recommended Articles
Image fusion via nonlocal sparse K-SVD dictionary learning

Ying Li, Fangyi Li, Bendu Bai, and Qiang Shen
Appl. Opt. 55(7) 1814-1823 (2016)

Dictionaries for image and video-based face recognition [Invited]

Vishal M. Patel, Yi-Chen Chen, Rama Chellappa, and P. Jonathon Phillips
J. Opt. Soc. Am. A 31(5) 1090-1103 (2014)

Denoising infrared maritime imagery using tailored dictionaries via modified K-SVD algorithm

L. N. Smith, C. C. Olson, K. P. Judd, and J. M. Nichols
Appl. Opt. 51(17) 3941-3949 (2012)

References

  • View by:
  • |
  • |
  • |

  1. Y. S. Ng, T. Lundquist, D. Skvortsov, J. Liao, S. Kasapi, and H. Marks, “Laser Voltage Imaging: A New Perspective of Laser Voltage Probing,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2010), pp. 5–13.
  2. S. Kolachina, “Introduction to Laser Voltage Probing (LVP) of Integrated Circuits,” in, Microelectronics Failure Analysis, Desk Reference 6th ed. (ASM International, 2011).
  3. W. K. Chim, Semiconductor Device and Failure Analysis : Using Photon Emission Microscopy (Wiley, 2000)
  4. S. B. Ippolito, B. B. Goldberg, and M. S. Ünlü, “High spatial resolution subsurface microscopy,” Appl. Phys. Lett. 78(26), 4071–4073 (2001).
    [Crossref]
  5. F. H. Köklü, S. B. Ippolito, B. B. Goldberg, and M. S. Ünlü, “Subsurface microscopy of integrated circuits with angular spectrum and polarization control,” Opt. Lett. 34(8), 1261–1263 (2009).
    [Crossref] [PubMed]
  6. A. Yurt, M. D. Grogan, S. Ramachandran, B. B. Goldberg, and M. S. Ünlü, “Effect of vector asymmetry of radially polarized beams in solid immersion microscopy,” Opt. Express 22(6), 7320–7329 (2014).
    [Crossref] [PubMed]
  7. L. Novotny and B. Hecht, Principles of Nano-Optics (Cambridge University, 2006).
    [Crossref]
  8. R. Chen, K. Agarwal, C. J. R. Sheppard, J. C. H. Phang, and X. Chen, “A complete and computationally efficient numerical model of aplanatic solid immersion lens scanning microscope,” Opt. Express 21(12), 14316–14330 (2013).
    [Crossref] [PubMed]
  9. L. Hu, R. Chen, K. Agarwal, C. J. R. Sheppard, J. C. H. Phang, and X. Chen, “Dyadic Green’s function for aplanatic solid immersion lens based sub-surface microscopy,” Opt. Express 19(20), 19280–19295 (2011).
    [Crossref] [PubMed]
  10. P. Török, P.R.T. Munro, and E. E. Kriezis, “High numerical aperture vectorial imaging in coherent optical microscopes,” Opt. Express 16(2), 507–523 (2008).
    [Crossref] [PubMed]
  11. A. Yurt, A. Uyar, T. B. Cilingiroglu, B. B. Goldberg, and M. S. Ünlü, “Evanescent waves in high numerical aperture aplanatic solid immersion microscopy: Effects of forbidden light on subsurface imaging,” Opt. Express 22(7), 7422–7433 (2014).
    [Crossref] [PubMed]
  12. T. B. Cilingiroglu, F. H. Koklu, E. Ramsay, Y. Lu, A. Yurt, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Image Reconstruction Techniques for High Numerical Aperture Integrated Circuit Imaging,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2012), pp. 551–556.
  13. T. B. Cilingiroglu, “A Sparsity-based Framework for Resolution Enhancement in Optical Fault analysis of Integrated Circuits,” Ph.D. thesis, 2015.
  14. T. B. Cilingiroglu, A. Tuysuzoglu, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Dictionary-based Image Enhancement for Integrated Circuit Imaging,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2013), pp. 1869–1873.
  15. T. B. Cilingiroglu, M. Zangeneh, A. Uyar, W. C. Karl, J. Konrad, A. Joshi, B. B. Goldberg, and M. S. Unlu, “Dictionary-based sparse representation for resolution improvement in laser voltage imaging of CMOS integrated circuits,” in Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition(DATE) (EDA Consortium, 2015), pp..597–600
  16. M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15(2), 3736–3745 (2006).
    [Crossref] [PubMed]
  17. M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54(11), 4311–4322 (2006).
    [Crossref]
  18. L. Donoho, M. Elad, and V.N. Temlyakov, “Stable recovery of sparse overcomplete representations in the presence of noise,” IEEE Trans. Inf. Theory 52(1), 6–18 (2006).
    [Crossref]
  19. J.-L. Starck, M. Elad, and D.L. Donoho, “Image decomposition via the combination of sparse representations and a variational approach,” IEEE Trans. Image Process. 14(10), 1570–1582 (2005).
    [Crossref] [PubMed]
  20. David L. Donoho and Iain M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika 81(3), 425–455 (1994)
    [Crossref]
  21. Sadegh Samadi, Mujdat Cetin, and Masnadi-Shirazi Mohammad Ali, “Multiple feature-enhanced synthetic aperture radar imagery,” in Algorithms for synthetic aperture radar imagery (SPIE, 2009).
  22. S. Gazit, A. Szameit, Y. C. Eldar, and M. Segev, “Super-resolution and reconstruction of sparse sub-wavelength images,” Opt. Express 17(26), 23920–23946 (2009).
    [Crossref]
  23. S. J. Holden, S. Uphoff, and A. N. Kapanidis, “DAOSTORM: an algorithm for high-density super-resolution microscopy,” Nature methods 8(4), 279–280 (2011).
    [Crossref] [PubMed]
  24. A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
    [Crossref]
  25. L. Zhu, W. Zhang, D. Elnatan, and B. Huang, “Faster STORM using compressed sensing,” Nature methods 9, 721–723 (2012).
    [Crossref] [PubMed]
  26. S. Yee, “Numerical solution of initial boundary value problems involving Maxwell’s equations in isotropic media,” IEEE Trans. Antennas Propag. 14(3), 302–307 (1966).
    [Crossref]
  27. A. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Computing20(1), 33–61 (2006).
    [Crossref]
  28. S. Mallat and Z. Zhang, “Matching pursuit in a time-frequency dictionary,” IEEE Trans. Signal Process. 41(12), 3397–3415 (1993).
    [Crossref]
  29. Amir Beck and Marc Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci.2(1), 183–202 (2009).
    [Crossref]
  30. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and trends in machine learning 3(1), 1–122 (2010).
    [Crossref]
  31. P. Ciuciu, J. Idier, and J. F. Giovannelli, “Markovian high resolution spectral analysis,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 1999), pp. 1601–1604.
  32. R. Chartrand, “Exact reconstruction of sparse signals via nonconvex minimization,” IEEE Sig. Process. Lett. 14(10), 707–719 (2007).
    [Crossref]
  33. Z. Xu, X. Chang, F. Xu, and H. Zhang, “L1/2 Regularization: A Thresholding Representation Theory and a Fast Solver,” IEEE Trans. Neural Netw. Learn. Syst. 23(7), 1013–1027 (2012).
    [Crossref] [PubMed]
  34. S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “A method for large- scale l1-regularized least squares,” IEEE J. Sel. Topics Signal Process. 1(4), 606–617 (2007).
    [Crossref]
  35. S. Voronin and R. Chartrand, “A New Generalized Thresholding Algorithm for Inverse Problems with Sparsity Constraints,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2013), pp. 1636–1640.

2014 (2)

2013 (1)

2012 (3)

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

L. Zhu, W. Zhang, D. Elnatan, and B. Huang, “Faster STORM using compressed sensing,” Nature methods 9, 721–723 (2012).
[Crossref] [PubMed]

Z. Xu, X. Chang, F. Xu, and H. Zhang, “L1/2 Regularization: A Thresholding Representation Theory and a Fast Solver,” IEEE Trans. Neural Netw. Learn. Syst. 23(7), 1013–1027 (2012).
[Crossref] [PubMed]

2011 (2)

2010 (1)

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and trends in machine learning 3(1), 1–122 (2010).
[Crossref]

2009 (2)

2008 (1)

2007 (2)

R. Chartrand, “Exact reconstruction of sparse signals via nonconvex minimization,” IEEE Sig. Process. Lett. 14(10), 707–719 (2007).
[Crossref]

S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “A method for large- scale l1-regularized least squares,” IEEE J. Sel. Topics Signal Process. 1(4), 606–617 (2007).
[Crossref]

2006 (3)

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15(2), 3736–3745 (2006).
[Crossref] [PubMed]

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54(11), 4311–4322 (2006).
[Crossref]

L. Donoho, M. Elad, and V.N. Temlyakov, “Stable recovery of sparse overcomplete representations in the presence of noise,” IEEE Trans. Inf. Theory 52(1), 6–18 (2006).
[Crossref]

2005 (1)

J.-L. Starck, M. Elad, and D.L. Donoho, “Image decomposition via the combination of sparse representations and a variational approach,” IEEE Trans. Image Process. 14(10), 1570–1582 (2005).
[Crossref] [PubMed]

2001 (1)

S. B. Ippolito, B. B. Goldberg, and M. S. Ünlü, “High spatial resolution subsurface microscopy,” Appl. Phys. Lett. 78(26), 4071–4073 (2001).
[Crossref]

1994 (1)

David L. Donoho and Iain M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika 81(3), 425–455 (1994)
[Crossref]

1993 (1)

S. Mallat and Z. Zhang, “Matching pursuit in a time-frequency dictionary,” IEEE Trans. Signal Process. 41(12), 3397–3415 (1993).
[Crossref]

1966 (1)

S. Yee, “Numerical solution of initial boundary value problems involving Maxwell’s equations in isotropic media,” IEEE Trans. Antennas Propag. 14(3), 302–307 (1966).
[Crossref]

Agarwal, K.

Aharon, M.

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15(2), 3736–3745 (2006).
[Crossref] [PubMed]

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54(11), 4311–4322 (2006).
[Crossref]

Beck, Amir

Amir Beck and Marc Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci.2(1), 183–202 (2009).
[Crossref]

Boyd, S.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and trends in machine learning 3(1), 1–122 (2010).
[Crossref]

S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “A method for large- scale l1-regularized least squares,” IEEE J. Sel. Topics Signal Process. 1(4), 606–617 (2007).
[Crossref]

Bruckstein, A.

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54(11), 4311–4322 (2006).
[Crossref]

Bullkich, E.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

Cetin, Mujdat

Sadegh Samadi, Mujdat Cetin, and Masnadi-Shirazi Mohammad Ali, “Multiple feature-enhanced synthetic aperture radar imagery,” in Algorithms for synthetic aperture radar imagery (SPIE, 2009).

Chang, X.

Z. Xu, X. Chang, F. Xu, and H. Zhang, “L1/2 Regularization: A Thresholding Representation Theory and a Fast Solver,” IEEE Trans. Neural Netw. Learn. Syst. 23(7), 1013–1027 (2012).
[Crossref] [PubMed]

Chartrand, R.

R. Chartrand, “Exact reconstruction of sparse signals via nonconvex minimization,” IEEE Sig. Process. Lett. 14(10), 707–719 (2007).
[Crossref]

S. Voronin and R. Chartrand, “A New Generalized Thresholding Algorithm for Inverse Problems with Sparsity Constraints,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2013), pp. 1636–1640.

Chen, A. S.

A. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Computing20(1), 33–61 (2006).
[Crossref]

Chen, R.

Chen, X.

Chim, W. K.

W. K. Chim, Semiconductor Device and Failure Analysis : Using Photon Emission Microscopy (Wiley, 2000)

Chu, E.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and trends in machine learning 3(1), 1–122 (2010).
[Crossref]

Cilingiroglu, T. B.

A. Yurt, A. Uyar, T. B. Cilingiroglu, B. B. Goldberg, and M. S. Ünlü, “Evanescent waves in high numerical aperture aplanatic solid immersion microscopy: Effects of forbidden light on subsurface imaging,” Opt. Express 22(7), 7422–7433 (2014).
[Crossref] [PubMed]

T. B. Cilingiroglu, F. H. Koklu, E. Ramsay, Y. Lu, A. Yurt, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Image Reconstruction Techniques for High Numerical Aperture Integrated Circuit Imaging,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2012), pp. 551–556.

T. B. Cilingiroglu, “A Sparsity-based Framework for Resolution Enhancement in Optical Fault analysis of Integrated Circuits,” Ph.D. thesis, 2015.

T. B. Cilingiroglu, A. Tuysuzoglu, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Dictionary-based Image Enhancement for Integrated Circuit Imaging,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2013), pp. 1869–1873.

T. B. Cilingiroglu, M. Zangeneh, A. Uyar, W. C. Karl, J. Konrad, A. Joshi, B. B. Goldberg, and M. S. Unlu, “Dictionary-based sparse representation for resolution improvement in laser voltage imaging of CMOS integrated circuits,” in Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition(DATE) (EDA Consortium, 2015), pp..597–600

Ciuciu, P.

P. Ciuciu, J. Idier, and J. F. Giovannelli, “Markovian high resolution spectral analysis,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 1999), pp. 1601–1604.

Cohen, O.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

Cohen-Hyams, T.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

Dana, H.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

Donoho, D. L.

A. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Computing20(1), 33–61 (2006).
[Crossref]

Donoho, D.L.

J.-L. Starck, M. Elad, and D.L. Donoho, “Image decomposition via the combination of sparse representations and a variational approach,” IEEE Trans. Image Process. 14(10), 1570–1582 (2005).
[Crossref] [PubMed]

Donoho, David L.

David L. Donoho and Iain M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika 81(3), 425–455 (1994)
[Crossref]

Donoho, L.

L. Donoho, M. Elad, and V.N. Temlyakov, “Stable recovery of sparse overcomplete representations in the presence of noise,” IEEE Trans. Inf. Theory 52(1), 6–18 (2006).
[Crossref]

Eckstein, J.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and trends in machine learning 3(1), 1–122 (2010).
[Crossref]

Elad, M.

L. Donoho, M. Elad, and V.N. Temlyakov, “Stable recovery of sparse overcomplete representations in the presence of noise,” IEEE Trans. Inf. Theory 52(1), 6–18 (2006).
[Crossref]

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15(2), 3736–3745 (2006).
[Crossref] [PubMed]

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54(11), 4311–4322 (2006).
[Crossref]

J.-L. Starck, M. Elad, and D.L. Donoho, “Image decomposition via the combination of sparse representations and a variational approach,” IEEE Trans. Image Process. 14(10), 1570–1582 (2005).
[Crossref] [PubMed]

Eldar, Y. C.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

S. Gazit, A. Szameit, Y. C. Eldar, and M. Segev, “Super-resolution and reconstruction of sparse sub-wavelength images,” Opt. Express 17(26), 23920–23946 (2009).
[Crossref]

Elnatan, D.

L. Zhu, W. Zhang, D. Elnatan, and B. Huang, “Faster STORM using compressed sensing,” Nature methods 9, 721–723 (2012).
[Crossref] [PubMed]

Gazit, S.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

S. Gazit, A. Szameit, Y. C. Eldar, and M. Segev, “Super-resolution and reconstruction of sparse sub-wavelength images,” Opt. Express 17(26), 23920–23946 (2009).
[Crossref]

Giovannelli, J. F.

P. Ciuciu, J. Idier, and J. F. Giovannelli, “Markovian high resolution spectral analysis,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 1999), pp. 1601–1604.

Goldberg, B. B.

A. Yurt, A. Uyar, T. B. Cilingiroglu, B. B. Goldberg, and M. S. Ünlü, “Evanescent waves in high numerical aperture aplanatic solid immersion microscopy: Effects of forbidden light on subsurface imaging,” Opt. Express 22(7), 7422–7433 (2014).
[Crossref] [PubMed]

A. Yurt, M. D. Grogan, S. Ramachandran, B. B. Goldberg, and M. S. Ünlü, “Effect of vector asymmetry of radially polarized beams in solid immersion microscopy,” Opt. Express 22(6), 7320–7329 (2014).
[Crossref] [PubMed]

F. H. Köklü, S. B. Ippolito, B. B. Goldberg, and M. S. Ünlü, “Subsurface microscopy of integrated circuits with angular spectrum and polarization control,” Opt. Lett. 34(8), 1261–1263 (2009).
[Crossref] [PubMed]

S. B. Ippolito, B. B. Goldberg, and M. S. Ünlü, “High spatial resolution subsurface microscopy,” Appl. Phys. Lett. 78(26), 4071–4073 (2001).
[Crossref]

T. B. Cilingiroglu, A. Tuysuzoglu, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Dictionary-based Image Enhancement for Integrated Circuit Imaging,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2013), pp. 1869–1873.

T. B. Cilingiroglu, F. H. Koklu, E. Ramsay, Y. Lu, A. Yurt, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Image Reconstruction Techniques for High Numerical Aperture Integrated Circuit Imaging,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2012), pp. 551–556.

T. B. Cilingiroglu, M. Zangeneh, A. Uyar, W. C. Karl, J. Konrad, A. Joshi, B. B. Goldberg, and M. S. Unlu, “Dictionary-based sparse representation for resolution improvement in laser voltage imaging of CMOS integrated circuits,” in Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition(DATE) (EDA Consortium, 2015), pp..597–600

Gorinevsky, D.

S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “A method for large- scale l1-regularized least squares,” IEEE J. Sel. Topics Signal Process. 1(4), 606–617 (2007).
[Crossref]

Grogan, M. D.

Hecht, B.

L. Novotny and B. Hecht, Principles of Nano-Optics (Cambridge University, 2006).
[Crossref]

Holden, S. J.

S. J. Holden, S. Uphoff, and A. N. Kapanidis, “DAOSTORM: an algorithm for high-density super-resolution microscopy,” Nature methods 8(4), 279–280 (2011).
[Crossref] [PubMed]

Hu, L.

Huang, B.

L. Zhu, W. Zhang, D. Elnatan, and B. Huang, “Faster STORM using compressed sensing,” Nature methods 9, 721–723 (2012).
[Crossref] [PubMed]

Idier, J.

P. Ciuciu, J. Idier, and J. F. Giovannelli, “Markovian high resolution spectral analysis,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 1999), pp. 1601–1604.

Ippolito, S. B.

Johnstone, Iain M.

David L. Donoho and Iain M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika 81(3), 425–455 (1994)
[Crossref]

Joshi, A.

T. B. Cilingiroglu, M. Zangeneh, A. Uyar, W. C. Karl, J. Konrad, A. Joshi, B. B. Goldberg, and M. S. Unlu, “Dictionary-based sparse representation for resolution improvement in laser voltage imaging of CMOS integrated circuits,” in Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition(DATE) (EDA Consortium, 2015), pp..597–600

Kapanidis, A. N.

S. J. Holden, S. Uphoff, and A. N. Kapanidis, “DAOSTORM: an algorithm for high-density super-resolution microscopy,” Nature methods 8(4), 279–280 (2011).
[Crossref] [PubMed]

Karl, W. C.

T. B. Cilingiroglu, M. Zangeneh, A. Uyar, W. C. Karl, J. Konrad, A. Joshi, B. B. Goldberg, and M. S. Unlu, “Dictionary-based sparse representation for resolution improvement in laser voltage imaging of CMOS integrated circuits,” in Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition(DATE) (EDA Consortium, 2015), pp..597–600

T. B. Cilingiroglu, F. H. Koklu, E. Ramsay, Y. Lu, A. Yurt, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Image Reconstruction Techniques for High Numerical Aperture Integrated Circuit Imaging,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2012), pp. 551–556.

T. B. Cilingiroglu, A. Tuysuzoglu, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Dictionary-based Image Enhancement for Integrated Circuit Imaging,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2013), pp. 1869–1873.

Kasapi, S.

Y. S. Ng, T. Lundquist, D. Skvortsov, J. Liao, S. Kasapi, and H. Marks, “Laser Voltage Imaging: A New Perspective of Laser Voltage Probing,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2010), pp. 5–13.

Kim, S.-J.

S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “A method for large- scale l1-regularized least squares,” IEEE J. Sel. Topics Signal Process. 1(4), 606–617 (2007).
[Crossref]

Kley, E. B.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

Koh, K.

S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “A method for large- scale l1-regularized least squares,” IEEE J. Sel. Topics Signal Process. 1(4), 606–617 (2007).
[Crossref]

Koklu, F. H.

T. B. Cilingiroglu, F. H. Koklu, E. Ramsay, Y. Lu, A. Yurt, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Image Reconstruction Techniques for High Numerical Aperture Integrated Circuit Imaging,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2012), pp. 551–556.

Köklü, F. H.

Kolachina, S.

S. Kolachina, “Introduction to Laser Voltage Probing (LVP) of Integrated Circuits,” in, Microelectronics Failure Analysis, Desk Reference 6th ed. (ASM International, 2011).

Konrad, J.

T. B. Cilingiroglu, A. Tuysuzoglu, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Dictionary-based Image Enhancement for Integrated Circuit Imaging,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2013), pp. 1869–1873.

T. B. Cilingiroglu, F. H. Koklu, E. Ramsay, Y. Lu, A. Yurt, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Image Reconstruction Techniques for High Numerical Aperture Integrated Circuit Imaging,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2012), pp. 551–556.

T. B. Cilingiroglu, M. Zangeneh, A. Uyar, W. C. Karl, J. Konrad, A. Joshi, B. B. Goldberg, and M. S. Unlu, “Dictionary-based sparse representation for resolution improvement in laser voltage imaging of CMOS integrated circuits,” in Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition(DATE) (EDA Consortium, 2015), pp..597–600

Kriezis, E. E.

Liao, J.

Y. S. Ng, T. Lundquist, D. Skvortsov, J. Liao, S. Kasapi, and H. Marks, “Laser Voltage Imaging: A New Perspective of Laser Voltage Probing,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2010), pp. 5–13.

Lu, Y.

T. B. Cilingiroglu, F. H. Koklu, E. Ramsay, Y. Lu, A. Yurt, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Image Reconstruction Techniques for High Numerical Aperture Integrated Circuit Imaging,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2012), pp. 551–556.

Lundquist, T.

Y. S. Ng, T. Lundquist, D. Skvortsov, J. Liao, S. Kasapi, and H. Marks, “Laser Voltage Imaging: A New Perspective of Laser Voltage Probing,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2010), pp. 5–13.

Lustig, M.

S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “A method for large- scale l1-regularized least squares,” IEEE J. Sel. Topics Signal Process. 1(4), 606–617 (2007).
[Crossref]

Mallat, S.

S. Mallat and Z. Zhang, “Matching pursuit in a time-frequency dictionary,” IEEE Trans. Signal Process. 41(12), 3397–3415 (1993).
[Crossref]

Marks, H.

Y. S. Ng, T. Lundquist, D. Skvortsov, J. Liao, S. Kasapi, and H. Marks, “Laser Voltage Imaging: A New Perspective of Laser Voltage Probing,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2010), pp. 5–13.

Mohammad Ali, Masnadi-Shirazi

Sadegh Samadi, Mujdat Cetin, and Masnadi-Shirazi Mohammad Ali, “Multiple feature-enhanced synthetic aperture radar imagery,” in Algorithms for synthetic aperture radar imagery (SPIE, 2009).

Munro, P.R.T.

Ng, Y. S.

Y. S. Ng, T. Lundquist, D. Skvortsov, J. Liao, S. Kasapi, and H. Marks, “Laser Voltage Imaging: A New Perspective of Laser Voltage Probing,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2010), pp. 5–13.

Novotny, L.

L. Novotny and B. Hecht, Principles of Nano-Optics (Cambridge University, 2006).
[Crossref]

Osherovich, E.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

Parikh, N.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and trends in machine learning 3(1), 1–122 (2010).
[Crossref]

Peleato, B.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and trends in machine learning 3(1), 1–122 (2010).
[Crossref]

Phang, J. C. H.

Ramachandran, S.

Ramsay, E.

T. B. Cilingiroglu, F. H. Koklu, E. Ramsay, Y. Lu, A. Yurt, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Image Reconstruction Techniques for High Numerical Aperture Integrated Circuit Imaging,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2012), pp. 551–556.

Samadi, Sadegh

Sadegh Samadi, Mujdat Cetin, and Masnadi-Shirazi Mohammad Ali, “Multiple feature-enhanced synthetic aperture radar imagery,” in Algorithms for synthetic aperture radar imagery (SPIE, 2009).

Saunders, M. A.

A. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Computing20(1), 33–61 (2006).
[Crossref]

Segev, M.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

S. Gazit, A. Szameit, Y. C. Eldar, and M. Segev, “Super-resolution and reconstruction of sparse sub-wavelength images,” Opt. Express 17(26), 23920–23946 (2009).
[Crossref]

Shechtman, Y.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

Sheppard, C. J. R.

Shoham, S.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

Sidorenko, P.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

Skvortsov, D.

Y. S. Ng, T. Lundquist, D. Skvortsov, J. Liao, S. Kasapi, and H. Marks, “Laser Voltage Imaging: A New Perspective of Laser Voltage Probing,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2010), pp. 5–13.

Starck, J.-L.

J.-L. Starck, M. Elad, and D.L. Donoho, “Image decomposition via the combination of sparse representations and a variational approach,” IEEE Trans. Image Process. 14(10), 1570–1582 (2005).
[Crossref] [PubMed]

Steiner, S.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

Szameit, A.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

S. Gazit, A. Szameit, Y. C. Eldar, and M. Segev, “Super-resolution and reconstruction of sparse sub-wavelength images,” Opt. Express 17(26), 23920–23946 (2009).
[Crossref]

Teboulle, Marc

Amir Beck and Marc Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci.2(1), 183–202 (2009).
[Crossref]

Temlyakov, V.N.

L. Donoho, M. Elad, and V.N. Temlyakov, “Stable recovery of sparse overcomplete representations in the presence of noise,” IEEE Trans. Inf. Theory 52(1), 6–18 (2006).
[Crossref]

Török, P.

Tuysuzoglu, A.

T. B. Cilingiroglu, A. Tuysuzoglu, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Dictionary-based Image Enhancement for Integrated Circuit Imaging,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2013), pp. 1869–1873.

Unlu, M. S.

T. B. Cilingiroglu, M. Zangeneh, A. Uyar, W. C. Karl, J. Konrad, A. Joshi, B. B. Goldberg, and M. S. Unlu, “Dictionary-based sparse representation for resolution improvement in laser voltage imaging of CMOS integrated circuits,” in Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition(DATE) (EDA Consortium, 2015), pp..597–600

Ünlü, M. S.

A. Yurt, M. D. Grogan, S. Ramachandran, B. B. Goldberg, and M. S. Ünlü, “Effect of vector asymmetry of radially polarized beams in solid immersion microscopy,” Opt. Express 22(6), 7320–7329 (2014).
[Crossref] [PubMed]

A. Yurt, A. Uyar, T. B. Cilingiroglu, B. B. Goldberg, and M. S. Ünlü, “Evanescent waves in high numerical aperture aplanatic solid immersion microscopy: Effects of forbidden light on subsurface imaging,” Opt. Express 22(7), 7422–7433 (2014).
[Crossref] [PubMed]

F. H. Köklü, S. B. Ippolito, B. B. Goldberg, and M. S. Ünlü, “Subsurface microscopy of integrated circuits with angular spectrum and polarization control,” Opt. Lett. 34(8), 1261–1263 (2009).
[Crossref] [PubMed]

S. B. Ippolito, B. B. Goldberg, and M. S. Ünlü, “High spatial resolution subsurface microscopy,” Appl. Phys. Lett. 78(26), 4071–4073 (2001).
[Crossref]

T. B. Cilingiroglu, F. H. Koklu, E. Ramsay, Y. Lu, A. Yurt, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Image Reconstruction Techniques for High Numerical Aperture Integrated Circuit Imaging,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2012), pp. 551–556.

T. B. Cilingiroglu, A. Tuysuzoglu, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Dictionary-based Image Enhancement for Integrated Circuit Imaging,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2013), pp. 1869–1873.

Uphoff, S.

S. J. Holden, S. Uphoff, and A. N. Kapanidis, “DAOSTORM: an algorithm for high-density super-resolution microscopy,” Nature methods 8(4), 279–280 (2011).
[Crossref] [PubMed]

Uyar, A.

A. Yurt, A. Uyar, T. B. Cilingiroglu, B. B. Goldberg, and M. S. Ünlü, “Evanescent waves in high numerical aperture aplanatic solid immersion microscopy: Effects of forbidden light on subsurface imaging,” Opt. Express 22(7), 7422–7433 (2014).
[Crossref] [PubMed]

T. B. Cilingiroglu, M. Zangeneh, A. Uyar, W. C. Karl, J. Konrad, A. Joshi, B. B. Goldberg, and M. S. Unlu, “Dictionary-based sparse representation for resolution improvement in laser voltage imaging of CMOS integrated circuits,” in Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition(DATE) (EDA Consortium, 2015), pp..597–600

Voronin, S.

S. Voronin and R. Chartrand, “A New Generalized Thresholding Algorithm for Inverse Problems with Sparsity Constraints,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2013), pp. 1636–1640.

Xu, F.

Z. Xu, X. Chang, F. Xu, and H. Zhang, “L1/2 Regularization: A Thresholding Representation Theory and a Fast Solver,” IEEE Trans. Neural Netw. Learn. Syst. 23(7), 1013–1027 (2012).
[Crossref] [PubMed]

Xu, Z.

Z. Xu, X. Chang, F. Xu, and H. Zhang, “L1/2 Regularization: A Thresholding Representation Theory and a Fast Solver,” IEEE Trans. Neural Netw. Learn. Syst. 23(7), 1013–1027 (2012).
[Crossref] [PubMed]

Yavneh, I.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

Yee, S.

S. Yee, “Numerical solution of initial boundary value problems involving Maxwell’s equations in isotropic media,” IEEE Trans. Antennas Propag. 14(3), 302–307 (1966).
[Crossref]

Yurt, A.

Zangeneh, M.

T. B. Cilingiroglu, M. Zangeneh, A. Uyar, W. C. Karl, J. Konrad, A. Joshi, B. B. Goldberg, and M. S. Unlu, “Dictionary-based sparse representation for resolution improvement in laser voltage imaging of CMOS integrated circuits,” in Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition(DATE) (EDA Consortium, 2015), pp..597–600

Zhang, H.

Z. Xu, X. Chang, F. Xu, and H. Zhang, “L1/2 Regularization: A Thresholding Representation Theory and a Fast Solver,” IEEE Trans. Neural Netw. Learn. Syst. 23(7), 1013–1027 (2012).
[Crossref] [PubMed]

Zhang, W.

L. Zhu, W. Zhang, D. Elnatan, and B. Huang, “Faster STORM using compressed sensing,” Nature methods 9, 721–723 (2012).
[Crossref] [PubMed]

Zhang, Z.

S. Mallat and Z. Zhang, “Matching pursuit in a time-frequency dictionary,” IEEE Trans. Signal Process. 41(12), 3397–3415 (1993).
[Crossref]

Zhu, L.

L. Zhu, W. Zhang, D. Elnatan, and B. Huang, “Faster STORM using compressed sensing,” Nature methods 9, 721–723 (2012).
[Crossref] [PubMed]

Zibulevsky, M.

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

Appl. Phys. Lett. (1)

S. B. Ippolito, B. B. Goldberg, and M. S. Ünlü, “High spatial resolution subsurface microscopy,” Appl. Phys. Lett. 78(26), 4071–4073 (2001).
[Crossref]

Biometrika (1)

David L. Donoho and Iain M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika 81(3), 425–455 (1994)
[Crossref]

Foundations and trends in machine learning (1)

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers,” Foundations and trends in machine learning 3(1), 1–122 (2010).
[Crossref]

IEEE J. Sel. Topics Signal Process. (1)

S.-J. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “A method for large- scale l1-regularized least squares,” IEEE J. Sel. Topics Signal Process. 1(4), 606–617 (2007).
[Crossref]

IEEE Sig. Process. Lett. (1)

R. Chartrand, “Exact reconstruction of sparse signals via nonconvex minimization,” IEEE Sig. Process. Lett. 14(10), 707–719 (2007).
[Crossref]

IEEE Trans. Antennas Propag. (1)

S. Yee, “Numerical solution of initial boundary value problems involving Maxwell’s equations in isotropic media,” IEEE Trans. Antennas Propag. 14(3), 302–307 (1966).
[Crossref]

IEEE Trans. Image Process. (2)

J.-L. Starck, M. Elad, and D.L. Donoho, “Image decomposition via the combination of sparse representations and a variational approach,” IEEE Trans. Image Process. 14(10), 1570–1582 (2005).
[Crossref] [PubMed]

M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process. 15(2), 3736–3745 (2006).
[Crossref] [PubMed]

IEEE Trans. Inf. Theory (1)

L. Donoho, M. Elad, and V.N. Temlyakov, “Stable recovery of sparse overcomplete representations in the presence of noise,” IEEE Trans. Inf. Theory 52(1), 6–18 (2006).
[Crossref]

IEEE Trans. Neural Netw. Learn. Syst. (1)

Z. Xu, X. Chang, F. Xu, and H. Zhang, “L1/2 Regularization: A Thresholding Representation Theory and a Fast Solver,” IEEE Trans. Neural Netw. Learn. Syst. 23(7), 1013–1027 (2012).
[Crossref] [PubMed]

IEEE Trans. Signal Process. (2)

S. Mallat and Z. Zhang, “Matching pursuit in a time-frequency dictionary,” IEEE Trans. Signal Process. 41(12), 3397–3415 (1993).
[Crossref]

M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54(11), 4311–4322 (2006).
[Crossref]

Nature Mater. (1)

A. Szameit, Y. Shechtman, E. Osherovich, E. Bullkich, P. Sidorenko, H. Dana, S. Steiner, E. B. Kley, S. Gazit, T. Cohen-Hyams, S. Shoham, M. Zibulevsky, I. Yavneh, Y. C. Eldar, O. Cohen, and M. Segev, “Sparsity-based single-shot subwavelength coherent diffractive imaging,” Nature Mater. 11, 455–459 (2012).
[Crossref]

Nature methods (2)

L. Zhu, W. Zhang, D. Elnatan, and B. Huang, “Faster STORM using compressed sensing,” Nature methods 9, 721–723 (2012).
[Crossref] [PubMed]

S. J. Holden, S. Uphoff, and A. N. Kapanidis, “DAOSTORM: an algorithm for high-density super-resolution microscopy,” Nature methods 8(4), 279–280 (2011).
[Crossref] [PubMed]

Opt. Express (6)

Opt. Lett. (1)

Other (13)

Y. S. Ng, T. Lundquist, D. Skvortsov, J. Liao, S. Kasapi, and H. Marks, “Laser Voltage Imaging: A New Perspective of Laser Voltage Probing,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2010), pp. 5–13.

S. Kolachina, “Introduction to Laser Voltage Probing (LVP) of Integrated Circuits,” in, Microelectronics Failure Analysis, Desk Reference 6th ed. (ASM International, 2011).

W. K. Chim, Semiconductor Device and Failure Analysis : Using Photon Emission Microscopy (Wiley, 2000)

L. Novotny and B. Hecht, Principles of Nano-Optics (Cambridge University, 2006).
[Crossref]

T. B. Cilingiroglu, F. H. Koklu, E. Ramsay, Y. Lu, A. Yurt, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Image Reconstruction Techniques for High Numerical Aperture Integrated Circuit Imaging,” International Symposium for Testing and Failure Analysis (ISTFA), (ASM International, 2012), pp. 551–556.

T. B. Cilingiroglu, “A Sparsity-based Framework for Resolution Enhancement in Optical Fault analysis of Integrated Circuits,” Ph.D. thesis, 2015.

T. B. Cilingiroglu, A. Tuysuzoglu, W. C. Karl, J. Konrad, B. B. Goldberg, and M. S. Ünlü, “Dictionary-based Image Enhancement for Integrated Circuit Imaging,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2013), pp. 1869–1873.

T. B. Cilingiroglu, M. Zangeneh, A. Uyar, W. C. Karl, J. Konrad, A. Joshi, B. B. Goldberg, and M. S. Unlu, “Dictionary-based sparse representation for resolution improvement in laser voltage imaging of CMOS integrated circuits,” in Proceedings of the 2015 Design, Automation & Test in Europe Conference & Exhibition(DATE) (EDA Consortium, 2015), pp..597–600

Sadegh Samadi, Mujdat Cetin, and Masnadi-Shirazi Mohammad Ali, “Multiple feature-enhanced synthetic aperture radar imagery,” in Algorithms for synthetic aperture radar imagery (SPIE, 2009).

Amir Beck and Marc Teboulle, “A fast iterative shrinkage-thresholding algorithm for linear inverse problems,” SIAM J. Imaging Sci.2(1), 183–202 (2009).
[Crossref]

A. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Computing20(1), 33–61 (2006).
[Crossref]

P. Ciuciu, J. Idier, and J. F. Giovannelli, “Markovian high resolution spectral analysis,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 1999), pp. 1601–1604.

S. Voronin and R. Chartrand, “A New Generalized Thresholding Algorithm for Inverse Problems with Sparsity Constraints,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2013), pp. 1636–1640.

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (15)

Fig. 1
Fig. 1

CAD layout example

Fig. 2
Fig. 2

Design example

Fig. 3
Fig. 3

Examples of dictionary elements for the design from Fig. 2: Horizontal and vertical lines of various widths, lengths and locations.

Fig. 4
Fig. 4

Comparison of PSF cross section for different values of α for aluminum objects (a) horizontal cross section, (b) vertical cross section, and for polysilicon objects (c) horizontal cross section, (d) vertical cross section.

Fig. 5
Fig. 5

Comparison of PSF (α = 2.753) cross section and data cross section: (a) experimental aSIL data, (b) data cross section (along the green line shown in panel (a)), (c) PSF cross section (blue for horizontal and red for vertical).

Fig. 6
Fig. 6

Simulated PSFs with linearly polarized input light in the y direction with α = 2.53 (a) for aluminum objects (b) for polysilicon objects.

Fig. 7
Fig. 7

Phantoms for resolution structures used in simulated experiments (a) Phantom 1 (b) Phantom 2

Fig. 8
Fig. 8

Simulated observation images obtained from the two phantoms from Fig. 7 with either x– or y–polarized light and two levels of noise, 10 dB and 20 dB.

Fig. 9
Fig. 9

Sparse image reconstruction results for Phantom 1 (a) non-quadratic regularization for SNR=10dB, (b) dictionary-based l1–regularization and SNR=10dB, (c) dictionary-based l1/2–regularization and SNR=10dB, (d) non-quadratic regularization result for SNR=20dB case, (e) dictionary-based l1–regularization for SNR=20dB case, (f) dictionary-based l1 / 2–regularization for SNR=20dB, and for Phantom 2 (g) non-quadratic regularization result for SNR=10dB, (h) dictionary-based l1–regularization and SNR=10dB, (i) dictionary-based l1/2–regularization and SNR=10dB, (j) non-quadratic regularization result for SNR=20dB case, (k) dictionary-based l1–regularization for SNR=20dB case, (l) dictionary-based l1/2–regularization for SNR=20dB

Fig. 10
Fig. 10

MSE plots (a) Phantom 1 (b) Phantom 2

Fig. 11
Fig. 11

(a) CNN structure design and SEM images for lines resolution target with (b) 282nm (c) 252nm (d) 224nm separation

Fig. 12
Fig. 12

CNN-shaped polysilicon resolution target observation data with (a) x– polarized input light (b) y–polarized input light, and reconstruction results with (c) non-quadratic regularization (d) dictionary-based l1–regularization (e) dictionary-based l1 / 2–regularization.

Fig. 13
Fig. 13

Cross sections from the observation and reconstructions for CNN-shaped polysilicon resolution target (a) horizontal (b) vertical

Fig. 14
Fig. 14

Observation data of resolution target of aluminum lines with 282nm pitch (a) x–polarized input light (b) y–polarized input light, and reconstruction results with (c) non-quadratic regularization (d) dictionary-based l1–regularization (e) dictionary-based l1/2–regularization, observation data of resolution target of aluminum lines with 252nm pitch (f) x–polarized input light (g) y–polarized input light, and reconstruction results with (h) non-quadratic regularization (i) dictionary-based l1–regularization (j) dictionary-based l1/2–regularization, observation data of resolution target of aluminum lines with 224nm pitch (k) x–polarized input light (l) y–polarized input light, and reconstruction results with (m) non-quadratic regularization (n) dictionary-based l1–regularization (o) dictionary-based l1 / 2–regularization.

Fig. 15
Fig. 15

Cross sections from observation data and reconstructions for aluminum lines resolution target for 282nm pitch (a) horizontal (b) vertical, for 252nm pitch (c) horizontal (d) vertical and for for 224nm pitch (e) horizontal (f) vertical

Equations (7)

Equations on this page are rendered with MathJax. Learn more.

g j = H j f ,
f = Φ ω ,
g j = H j Φ ω + w j .
ω ^ = arg min ω J ( ω ) = j = 1 n H j Φ ω g j 2 2 + λ ω p p ,
E d e t = G a S I L ( r , θ a S I L , ϕ ) E s c a t ( r , θ a S I L , ϕ ) + E R e f ( r , θ a S I L , ϕ ) ,
P S F = α G a S I L E s c a t + E R e f 2 E R e f 2 ,
f ^ = arg min f J ( f ) = j = 1 n H j f g j 2 2 + λ 1 D f 1 + λ 2 f 1 ,

Metrics