A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM 60, 84–90 (2017).

[Crossref]

X. Ma, S. Jiang, J. Wang, B. Wu, Z. Song, and Y. Li, “A fast and manufacture-friendly optical proximity correction based on machine learning,” Microelectron. Eng. 168, 15–26 (2016).

[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).

[Crossref]
[PubMed]

P. Sprechmann, A. M. Bronstein, and G. Sapiro, “Learning efficient sparse and low rank models,” IEEE Trans. Pattern Anal. 37, 1821–1833 (2015).

[Crossref]

X. Wu, S. Liu, W. Lv, and E. Y. Lam, “Robust and efficient inverse mask synthesis with basis function representation,” J. Opt. Soc. Am. A 31, B1–B9 (2014).

[Crossref]

O. A. Hamid, A. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, “Convolutional neural networks for speech recognition,” IEEE/ACM Trans. Audio, Speech, and Language Process. 22, 1533–1545 (2014).

[Crossref]

K. Luo, Z. Shi, X. Yan, and Z. Geng, “SVM based layout retargeting for fast and regularized inverse lithography,” J. Zhejiang Uni-Sci C (Comput & Electron) 15, 390–400 (2014).

[Crossref]

X. Ma, B. Wu, Z. Song, S. Jiang, and Y. Li, “Fast pixel-based optical proximity correction based on nonparametric kernel regression,” J. Micro/Nanolith. MEMS MOEMS 13, 043007 (2014).

[Crossref]

W. Lv, S. Liu, Q. Xia, X. Wu, Y. Shen, and E. Y. Lam, “Level-set-based inverse lithography for mask synthesis using the conjugate gradient and an optimal time step,” J. Vac. Sci. Technol. B 31, 041605 (2013).

[Crossref]

R. Luo, “Optical proximity correction using a multilayer perceptron neural network,” J. Opt. 15, 075708 (2013).

[Crossref]

X. Ma, Z. Song, Y. Li, and G. R. Arce, “Block-based mask optimization for optical lithography,” Appl. Opt. 52, 3351–3363 (2013).

[Crossref]
[PubMed]

X. Ma and G. R. Arce, “Pixel-based OPC optimization based on conjugate gradients,” Opt. Express 19, 2165–2180 (2011).

[Crossref]
[PubMed]

Y. Shen, N. Jia, N. Wong, and E. Y. Lam, “Robust level-set-based inverse lithography,” Opt. Express 19, 5511–5521 (2011).

[Crossref]
[PubMed]

X. Ma, G. R. Arce, and Y. Li, “Optimal 3D phase-shifting masks in partially coherent illumination,” Appl. Opt. 50, 5567–5576 (2011).

[Crossref]
[PubMed]

A. Poonawala, B. Painter, and C. Kerchner, “Model-based assist feature placement for 32nm and 22nm technology nodes using inverse mask technology,” Proc. SPIE 7488, 748814 (2009).

[Crossref]

Y. Shen, N. Wong, and E. Y. Lam, “Level-set-based inverse lithography for photomask synthesis,” Opt. Express 17, 23690–23701 (2009).

[Crossref]

Y. Granik, “Fast pixel-based mask optimization for inverse lithography,” J. Microlith. Microfab. Microsyst. 5, 043002 (2006).

A. Poonawala and P. Milanfar, “OPC and PSM design using inverse lithography: a non-linear optimization approach,” Proc. SPIE 6154, 61543H (2006).

[Crossref]

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

[Crossref]
[PubMed]

N. B. Cobb and Y. Granik, “Dense OPC for 65nm and below,” Proc. SPIE 5992, 599259 (2005).

[Crossref]

P. M. Martin, C. J. Progler, G. Xiao, R. Gray, L. Pang, and Y. Liu, “Manufacturability study of masks created by inverse lithography technology (ILT),” Proc. SPIE 5992, 599235 (2005).

[Crossref]

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

[Crossref]

Y. Liu and A. Zakhor, “Binary and phase shifting mask design for optical lithography,” IEEE Trans. on Semicond. Manuf. 5, 138–152 (1992).

[Crossref]

G. E. Moore, “Cramming more components onto integrated circuits,” Electronics 38, 114ff (1965).

H. H. Hopkins, “On the diffraction theory of optical images,” Proc. R”. Soc. Lond. A 217, 408–432 (1953).

[Crossref]

H. H. Hopkins, “The concept of partial coherence in optics,” Proc. R. Soc. Lond. A 208, 263–277 (1951).

[Crossref]

E. Alpaydin, Introduction to Machine Learning, 2nd ed. (China Machine Press, 2014).

X. Ma, Z. Song, Y. Li, and G. R. Arce, “Block-based mask optimization for optical lithography,” Appl. Opt. 52, 3351–3363 (2013).

[Crossref]
[PubMed]

X. Ma, G. R. Arce, and Y. Li, “Optimal 3D phase-shifting masks in partially coherent illumination,” Appl. Opt. 50, 5567–5576 (2011).

[Crossref]
[PubMed]

X. Ma and G. R. Arce, “Pixel-based OPC optimization based on conjugate gradients,” Opt. Express 19, 2165–2180 (2011).

[Crossref]
[PubMed]

X. Ma and G. R. Arce, “Binary mask optimization for inverse lithography with partially coherent illumination,” J. Opt. Soc. Am. A 25, 2960–2970 (2008).

[Crossref]

X. Ma and G. R. Arce, “Generalized inverse lithography methods for phase-shifting mask design,” Opt. Express 15, 15066–15079 (2007).

[Crossref]
[PubMed]

X. Ma and G. R. Arce, Computational Lithography, Wiley Series in Pure and Applied Optics, 1st ed. (John Wiley and Sons, 2010).

[Crossref]

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

[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).

[Crossref]
[PubMed]

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, Adaptive Computation and Machine Learning Series (The MIT Press, 2016).

Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series,” in the handbook of brain theory and neural networks, 255–258 (The MIT Press, 1998).

P. Sprechmann, A. M. Bronstein, and G. Sapiro, “Learning efficient sparse and low rank models,” IEEE Trans. Pattern Anal. 37, 1821–1833 (2015).

[Crossref]

N. B. Cobb and Y. Granik, “Dense OPC for 65nm and below,” Proc. SPIE 5992, 599259 (2005).

[Crossref]

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, Adaptive Computation and Machine Learning Series (The MIT Press, 2016).

O. A. Hamid, A. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, “Convolutional neural networks for speech recognition,” IEEE/ACM Trans. Audio, Speech, and Language Process. 22, 1533–1545 (2014).

[Crossref]

B. Xin, Y. Wang, W. Gao, and D. Wipf, “Maximal sparsity with deep networks?,” ar”Xiv:1605.01636 (2016).

K. Luo, Z. Shi, X. Yan, and Z. Geng, “SVM based layout retargeting for fast and regularized inverse lithography,” J. Zhejiang Uni-Sci C (Comput & Electron) 15, 390–400 (2014).

[Crossref]

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

[Crossref]

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, Adaptive Computation and Machine Learning Series (The MIT Press, 2016).

Y. Granik, “Fast pixel-based mask optimization for inverse lithography,” J. Microlith. Microfab. Microsyst. 5, 043002 (2006).

N. B. Cobb and Y. Granik, “Dense OPC for 65nm and below,” Proc. SPIE 5992, 599259 (2005).

[Crossref]

P. M. Martin, C. J. Progler, G. Xiao, R. Gray, L. Pang, and Y. Liu, “Manufacturability study of masks created by inverse lithography technology (ILT),” Proc. SPIE 5992, 599235 (2005).

[Crossref]

K. Gregor and Y. LeCun, “Learning fast approximations of sparse coding,” Proc. 27th International Conference on Machine Learning, 399–406 (2010).

O. A. Hamid, A. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, “Convolutional neural networks for speech recognition,” IEEE/ACM Trans. Audio, Speech, and Language Process. 22, 1533–1545 (2014).

[Crossref]

J. Hershey, J. Roux, and F. Weninger, “Deep unfolding: model-based inspiration of novel deep architectures,” ar”Xiv:1409.2574 (2014).

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).

[Crossref]
[PubMed]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM 60, 84–90 (2017).

[Crossref]

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

[Crossref]
[PubMed]

H. H. Hopkins, “On the diffraction theory of optical images,” Proc. R”. Soc. Lond. A 217, 408–432 (1953).

[Crossref]

H. H. Hopkins, “The concept of partial coherence in optics,” Proc. R. Soc. Lond. A 208, 263–277 (1951).

[Crossref]

Z. Wang, Q. Ling, and T. S. Huang, “Learning deep l0 encoders,” Proc. Thirtieth AAAI Conference on Artificial Intelligence, 2194–2200 (2016).

O. A. Hamid, A. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, “Convolutional neural networks for speech recognition,” IEEE/ACM Trans. Audio, Speech, and Language Process. 22, 1533–1545 (2014).

[Crossref]

X. Ma, S. Jiang, J. Wang, B. Wu, Z. Song, and Y. Li, “A fast and manufacture-friendly optical proximity correction based on machine learning,” Microelectron. Eng. 168, 15–26 (2016).

[Crossref]

X. Ma, B. Wu, Z. Song, S. Jiang, and Y. Li, “Fast pixel-based optical proximity correction based on nonparametric kernel regression,” J. Micro/Nanolith. MEMS MOEMS 13, 043007 (2014).

[Crossref]

A. Poonawala, B. Painter, and C. Kerchner, “Model-based assist feature placement for 32nm and 22nm technology nodes using inverse mask technology,” Proc. SPIE 7488, 748814 (2009).

[Crossref]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM 60, 84–90 (2017).

[Crossref]

X. Wu, S. Liu, W. Lv, and E. Y. Lam, “Robust and efficient inverse mask synthesis with basis function representation,” J. Opt. Soc. Am. A 31, B1–B9 (2014).

[Crossref]

W. Lv, S. Liu, Q. Xia, X. Wu, Y. Shen, and E. Y. Lam, “Level-set-based inverse lithography for mask synthesis using the conjugate gradient and an optimal time step,” J. Vac. Sci. Technol. B 31, 041605 (2013).

[Crossref]

Y. Shen, N. Jia, N. Wong, and E. Y. Lam, “Robust level-set-based inverse lithography,” Opt. Express 19, 5511–5521 (2011).

[Crossref]
[PubMed]

N. Jia and E. Y. Lam, “Machine learning for inverse lithography: using stochastic gradient descent for robust photomask synthesis,” J. Opt. 12, 045601 (2010).

[Crossref]

Y. Shen, N. Wong, and E. Y. Lam, “Level-set-based inverse lithography for photomask synthesis,” Opt. Express 17, 23690–23701 (2009).

[Crossref]

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

[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).

[Crossref]
[PubMed]

Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series,” in the handbook of brain theory and neural networks, 255–258 (The MIT Press, 1998).

K. Gregor and Y. LeCun, “Learning fast approximations of sparse coding,” Proc. 27th International Conference on Machine Learning, 399–406 (2010).

X. Ma, S. Jiang, J. Wang, B. Wu, Z. Song, and Y. Li, “A fast and manufacture-friendly optical proximity correction based on machine learning,” Microelectron. Eng. 168, 15–26 (2016).

[Crossref]

X. Ma, B. Wu, Z. Song, S. Jiang, and Y. Li, “Fast pixel-based optical proximity correction based on nonparametric kernel regression,” J. Micro/Nanolith. MEMS MOEMS 13, 043007 (2014).

[Crossref]

X. Ma, Z. Song, Y. Li, and G. R. Arce, “Block-based mask optimization for optical lithography,” Appl. Opt. 52, 3351–3363 (2013).

[Crossref]
[PubMed]

X. Ma, Y. Li, and L. Dong, “Mask optimization approaches in optical lithography based on a vector imaging model,” J. Opt. Soc. Am. A 29, 1300–1312 (2012).

[Crossref]

X. Ma, G. R. Arce, and Y. Li, “Optimal 3D phase-shifting masks in partially coherent illumination,” Appl. Opt. 50, 5567–5576 (2011).

[Crossref]
[PubMed]

Z. Wang, Q. Ling, and T. S. Huang, “Learning deep l0 encoders,” Proc. Thirtieth AAAI Conference on Artificial Intelligence, 2194–2200 (2016).

X. Wu, S. Liu, W. Lv, and E. Y. Lam, “Robust and efficient inverse mask synthesis with basis function representation,” J. Opt. Soc. Am. A 31, B1–B9 (2014).

[Crossref]

W. Lv, S. Liu, Q. Xia, X. Wu, Y. Shen, and E. Y. Lam, “Level-set-based inverse lithography for mask synthesis using the conjugate gradient and an optimal time step,” J. Vac. Sci. Technol. B 31, 041605 (2013).

[Crossref]

P. M. Martin, C. J. Progler, G. Xiao, R. Gray, L. Pang, and Y. Liu, “Manufacturability study of masks created by inverse lithography technology (ILT),” Proc. SPIE 5992, 599235 (2005).

[Crossref]

Y. Liu and A. Zakhor, “Binary and phase shifting mask design for optical lithography,” IEEE Trans. on Semicond. Manuf. 5, 138–152 (1992).

[Crossref]

K. Luo, Z. Shi, X. Yan, and Z. Geng, “SVM based layout retargeting for fast and regularized inverse lithography,” J. Zhejiang Uni-Sci C (Comput & Electron) 15, 390–400 (2014).

[Crossref]

R. Luo, “Optical proximity correction using a multilayer perceptron neural network,” J. Opt. 15, 075708 (2013).

[Crossref]

X. Wu, S. Liu, W. Lv, and E. Y. Lam, “Robust and efficient inverse mask synthesis with basis function representation,” J. Opt. Soc. Am. A 31, B1–B9 (2014).

[Crossref]

W. Lv, S. Liu, Q. Xia, X. Wu, Y. Shen, and E. Y. Lam, “Level-set-based inverse lithography for mask synthesis using the conjugate gradient and an optimal time step,” J. Vac. Sci. Technol. B 31, 041605 (2013).

[Crossref]

X. Ma, S. Jiang, J. Wang, B. Wu, Z. Song, and Y. Li, “A fast and manufacture-friendly optical proximity correction based on machine learning,” Microelectron. Eng. 168, 15–26 (2016).

[Crossref]

X. Ma, B. Wu, Z. Song, S. Jiang, and Y. Li, “Fast pixel-based optical proximity correction based on nonparametric kernel regression,” J. Micro/Nanolith. MEMS MOEMS 13, 043007 (2014).

[Crossref]

X. Ma, Z. Song, Y. Li, and G. R. Arce, “Block-based mask optimization for optical lithography,” Appl. Opt. 52, 3351–3363 (2013).

[Crossref]
[PubMed]

X. Ma, Y. Li, and L. Dong, “Mask optimization approaches in optical lithography based on a vector imaging model,” J. Opt. Soc. Am. A 29, 1300–1312 (2012).

[Crossref]

X. Ma, G. R. Arce, and Y. Li, “Optimal 3D phase-shifting masks in partially coherent illumination,” Appl. Opt. 50, 5567–5576 (2011).

[Crossref]
[PubMed]

X. Ma and G. R. Arce, “Pixel-based OPC optimization based on conjugate gradients,” Opt. Express 19, 2165–2180 (2011).

[Crossref]
[PubMed]

X. Ma and G. R. Arce, “Binary mask optimization for inverse lithography with partially coherent illumination,” J. Opt. Soc. Am. A 25, 2960–2970 (2008).

[Crossref]

X. Ma and G. R. Arce, “Generalized inverse lithography methods for phase-shifting mask design,” Opt. Express 15, 15066–15079 (2007).

[Crossref]
[PubMed]

X. Ma and G. R. Arce, Computational Lithography, Wiley Series in Pure and Applied Optics, 1st ed. (John Wiley and Sons, 2010).

[Crossref]

P. M. Martin, C. J. Progler, G. Xiao, R. Gray, L. Pang, and Y. Liu, “Manufacturability study of masks created by inverse lithography technology (ILT),” Proc. SPIE 5992, 599235 (2005).

[Crossref]

A. Poonawala and P. Milanfar, “OPC and PSM design using inverse lithography: a non-linear optimization approach,” Proc. SPIE 6154, 61543H (2006).

[Crossref]

O. A. Hamid, A. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, “Convolutional neural networks for speech recognition,” IEEE/ACM Trans. Audio, Speech, and Language Process. 22, 1533–1545 (2014).

[Crossref]

G. E. Moore, “Cramming more components onto integrated circuits,” Electronics 38, 114ff (1965).

A. Poonawala, B. Painter, and C. Kerchner, “Model-based assist feature placement for 32nm and 22nm technology nodes using inverse mask technology,” Proc. SPIE 7488, 748814 (2009).

[Crossref]

P. M. Martin, C. J. Progler, G. Xiao, R. Gray, L. Pang, and Y. Liu, “Manufacturability study of masks created by inverse lithography technology (ILT),” Proc. SPIE 5992, 599235 (2005).

[Crossref]

O. A. Hamid, A. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, “Convolutional neural networks for speech recognition,” IEEE/ACM Trans. Audio, Speech, and Language Process. 22, 1533–1545 (2014).

[Crossref]

A. Poonawala, B. Painter, and C. Kerchner, “Model-based assist feature placement for 32nm and 22nm technology nodes using inverse mask technology,” Proc. SPIE 7488, 748814 (2009).

[Crossref]

A. Poonawala and P. Milanfar, “OPC and PSM design using inverse lithography: a non-linear optimization approach,” Proc. SPIE 6154, 61543H (2006).

[Crossref]

P. M. Martin, C. J. Progler, G. Xiao, R. Gray, L. Pang, and Y. Liu, “Manufacturability study of masks created by inverse lithography technology (ILT),” Proc. SPIE 5992, 599235 (2005).

[Crossref]

J. Hershey, J. Roux, and F. Weninger, “Deep unfolding: model-based inspiration of novel deep architectures,” ar”Xiv:1409.2574 (2014).

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

[Crossref]
[PubMed]

P. Sprechmann, A. M. Bronstein, and G. Sapiro, “Learning efficient sparse and low rank models,” IEEE Trans. Pattern Anal. 37, 1821–1833 (2015).

[Crossref]

W. Lv, S. Liu, Q. Xia, X. Wu, Y. Shen, and E. Y. Lam, “Level-set-based inverse lithography for mask synthesis using the conjugate gradient and an optimal time step,” J. Vac. Sci. Technol. B 31, 041605 (2013).

[Crossref]

Y. Shen, N. Jia, N. Wong, and E. Y. Lam, “Robust level-set-based inverse lithography,” Opt. Express 19, 5511–5521 (2011).

[Crossref]
[PubMed]

Y. Shen, N. Wong, and E. Y. Lam, “Level-set-based inverse lithography for photomask synthesis,” Opt. Express 17, 23690–23701 (2009).

[Crossref]

K. Luo, Z. Shi, X. Yan, and Z. Geng, “SVM based layout retargeting for fast and regularized inverse lithography,” J. Zhejiang Uni-Sci C (Comput & Electron) 15, 390–400 (2014).

[Crossref]

X. Ma, S. Jiang, J. Wang, B. Wu, Z. Song, and Y. Li, “A fast and manufacture-friendly optical proximity correction based on machine learning,” Microelectron. Eng. 168, 15–26 (2016).

[Crossref]

X. Ma, B. Wu, Z. Song, S. Jiang, and Y. Li, “Fast pixel-based optical proximity correction based on nonparametric kernel regression,” J. Micro/Nanolith. MEMS MOEMS 13, 043007 (2014).

[Crossref]

X. Ma, Z. Song, Y. Li, and G. R. Arce, “Block-based mask optimization for optical lithography,” Appl. Opt. 52, 3351–3363 (2013).

[Crossref]
[PubMed]

P. Sprechmann, A. M. Bronstein, and G. Sapiro, “Learning efficient sparse and low rank models,” IEEE Trans. Pattern Anal. 37, 1821–1833 (2015).

[Crossref]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM 60, 84–90 (2017).

[Crossref]

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

[Crossref]

X. Ma, S. Jiang, J. Wang, B. Wu, Z. Song, and Y. Li, “A fast and manufacture-friendly optical proximity correction based on machine learning,” Microelectron. Eng. 168, 15–26 (2016).

[Crossref]

B. Xin, Y. Wang, W. Gao, and D. Wipf, “Maximal sparsity with deep networks?,” ar”Xiv:1605.01636 (2016).

Z. Wang, Q. Ling, and T. S. Huang, “Learning deep l0 encoders,” Proc. Thirtieth AAAI Conference on Artificial Intelligence, 2194–2200 (2016).

J. Hershey, J. Roux, and F. Weninger, “Deep unfolding: model-based inspiration of novel deep architectures,” ar”Xiv:1409.2574 (2014).

B. Xin, Y. Wang, W. Gao, and D. Wipf, “Maximal sparsity with deep networks?,” ar”Xiv:1605.01636 (2016).

A. K. Wong, Resolution Enhancement Techniques in Optical Lithography(SPIE, 2001).

[Crossref]

Y. Shen, N. Jia, N. Wong, and E. Y. Lam, “Robust level-set-based inverse lithography,” Opt. Express 19, 5511–5521 (2011).

[Crossref]
[PubMed]

Y. Shen, N. Wong, and E. Y. Lam, “Level-set-based inverse lithography for photomask synthesis,” Opt. Express 17, 23690–23701 (2009).

[Crossref]

X. Ma, S. Jiang, J. Wang, B. Wu, Z. Song, and Y. Li, “A fast and manufacture-friendly optical proximity correction based on machine learning,” Microelectron. Eng. 168, 15–26 (2016).

[Crossref]

X. Ma, B. Wu, Z. Song, S. Jiang, and Y. Li, “Fast pixel-based optical proximity correction based on nonparametric kernel regression,” J. Micro/Nanolith. MEMS MOEMS 13, 043007 (2014).

[Crossref]

X. Wu, S. Liu, W. Lv, and E. Y. Lam, “Robust and efficient inverse mask synthesis with basis function representation,” J. Opt. Soc. Am. A 31, B1–B9 (2014).

[Crossref]

W. Lv, S. Liu, Q. Xia, X. Wu, Y. Shen, and E. Y. Lam, “Level-set-based inverse lithography for mask synthesis using the conjugate gradient and an optimal time step,” J. Vac. Sci. Technol. B 31, 041605 (2013).

[Crossref]

W. Lv, S. Liu, Q. Xia, X. Wu, Y. Shen, and E. Y. Lam, “Level-set-based inverse lithography for mask synthesis using the conjugate gradient and an optimal time step,” J. Vac. Sci. Technol. B 31, 041605 (2013).

[Crossref]

P. M. Martin, C. J. Progler, G. Xiao, R. Gray, L. Pang, and Y. Liu, “Manufacturability study of masks created by inverse lithography technology (ILT),” Proc. SPIE 5992, 599235 (2005).

[Crossref]

B. Xin, Y. Wang, W. Gao, and D. Wipf, “Maximal sparsity with deep networks?,” ar”Xiv:1605.01636 (2016).

K. Luo, Z. Shi, X. Yan, and Z. Geng, “SVM based layout retargeting for fast and regularized inverse lithography,” J. Zhejiang Uni-Sci C (Comput & Electron) 15, 390–400 (2014).

[Crossref]

O. A. Hamid, A. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, “Convolutional neural networks for speech recognition,” IEEE/ACM Trans. Audio, Speech, and Language Process. 22, 1533–1545 (2014).

[Crossref]

Y. Liu and A. Zakhor, “Binary and phase shifting mask design for optical lithography,” IEEE Trans. on Semicond. Manuf. 5, 138–152 (1992).

[Crossref]

X. Ma, Z. Song, Y. Li, and G. R. Arce, “Block-based mask optimization for optical lithography,” Appl. Opt. 52, 3351–3363 (2013).

[Crossref]
[PubMed]

X. Ma, G. R. Arce, and Y. Li, “Optimal 3D phase-shifting masks in partially coherent illumination,” Appl. Opt. 50, 5567–5576 (2011).

[Crossref]
[PubMed]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM 60, 84–90 (2017).

[Crossref]

G. E. Moore, “Cramming more components onto integrated circuits,” Electronics 38, 114ff (1965).

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

[Crossref]

Y. Liu and A. Zakhor, “Binary and phase shifting mask design for optical lithography,” IEEE Trans. on Semicond. Manuf. 5, 138–152 (1992).

[Crossref]

P. Sprechmann, A. M. Bronstein, and G. Sapiro, “Learning efficient sparse and low rank models,” IEEE Trans. Pattern Anal. 37, 1821–1833 (2015).

[Crossref]

O. A. Hamid, A. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, “Convolutional neural networks for speech recognition,” IEEE/ACM Trans. Audio, Speech, and Language Process. 22, 1533–1545 (2014).

[Crossref]

X. Ma, B. Wu, Z. Song, S. Jiang, and Y. Li, “Fast pixel-based optical proximity correction based on nonparametric kernel regression,” J. Micro/Nanolith. MEMS MOEMS 13, 043007 (2014).

[Crossref]

Y. Granik, “Fast pixel-based mask optimization for inverse lithography,” J. Microlith. Microfab. Microsyst. 5, 043002 (2006).

N. Jia and E. Y. Lam, “Machine learning for inverse lithography: using stochastic gradient descent for robust photomask synthesis,” J. Opt. 12, 045601 (2010).

[Crossref]

R. Luo, “Optical proximity correction using a multilayer perceptron neural network,” J. Opt. 15, 075708 (2013).

[Crossref]

X. Ma, Y. Li, and L. Dong, “Mask optimization approaches in optical lithography based on a vector imaging model,” J. Opt. Soc. Am. A 29, 1300–1312 (2012).

[Crossref]

X. Wu, S. Liu, W. Lv, and E. Y. Lam, “Robust and efficient inverse mask synthesis with basis function representation,” J. Opt. Soc. Am. A 31, B1–B9 (2014).

[Crossref]

X. Ma and G. R. Arce, “Binary mask optimization for inverse lithography with partially coherent illumination,” J. Opt. Soc. Am. A 25, 2960–2970 (2008).

[Crossref]

W. Lv, S. Liu, Q. Xia, X. Wu, Y. Shen, and E. Y. Lam, “Level-set-based inverse lithography for mask synthesis using the conjugate gradient and an optimal time step,” J. Vac. Sci. Technol. B 31, 041605 (2013).

[Crossref]

K. Luo, Z. Shi, X. Yan, and Z. Geng, “SVM based layout retargeting for fast and regularized inverse lithography,” J. Zhejiang Uni-Sci C (Comput & Electron) 15, 390–400 (2014).

[Crossref]

X. Ma, S. Jiang, J. Wang, B. Wu, Z. Song, and Y. Li, “A fast and manufacture-friendly optical proximity correction based on machine learning,” Microelectron. Eng. 168, 15–26 (2016).

[Crossref]

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521, 436–444 (2015).

[Crossref]
[PubMed]

X. Ma and G. R. Arce, “Pixel-based OPC optimization based on conjugate gradients,” Opt. Express 19, 2165–2180 (2011).

[Crossref]
[PubMed]

Y. Shen, N. Wong, and E. Y. Lam, “Level-set-based inverse lithography for photomask synthesis,” Opt. Express 17, 23690–23701 (2009).

[Crossref]

J. Yu and P. Yu, “Impacts of cost functions on inverse lithography patterning,” Opt. Express 18, 23331–23342 (2010).

[Crossref]
[PubMed]

Y. Shen, N. Jia, N. Wong, and E. Y. Lam, “Robust level-set-based inverse lithography,” Opt. Express 19, 5511–5521 (2011).

[Crossref]
[PubMed]

X. Ma and G. R. Arce, “Generalized inverse lithography methods for phase-shifting mask design,” Opt. Express 15, 15066–15079 (2007).

[Crossref]
[PubMed]

H. H. Hopkins, “The concept of partial coherence in optics,” Proc. R. Soc. Lond. A 208, 263–277 (1951).

[Crossref]

H. H. Hopkins, “On the diffraction theory of optical images,” Proc. R”. Soc. Lond. A 217, 408–432 (1953).

[Crossref]

A. Poonawala, B. Painter, and C. Kerchner, “Model-based assist feature placement for 32nm and 22nm technology nodes using inverse mask technology,” Proc. SPIE 7488, 748814 (2009).

[Crossref]

N. B. Cobb and Y. Granik, “Dense OPC for 65nm and below,” Proc. SPIE 5992, 599259 (2005).

[Crossref]

P. M. Martin, C. J. Progler, G. Xiao, R. Gray, L. Pang, and Y. Liu, “Manufacturability study of masks created by inverse lithography technology (ILT),” Proc. SPIE 5992, 599235 (2005).

[Crossref]

A. Poonawala and P. Milanfar, “OPC and PSM design using inverse lithography: a non-linear optimization approach,” Proc. SPIE 6154, 61543H (2006).

[Crossref]

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

[Crossref]
[PubMed]

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, Adaptive Computation and Machine Learning Series (The MIT Press, 2016).

Y. LeCun and Y. Bengio, “Convolutional networks for images, speech, and time series,” in the handbook of brain theory and neural networks, 255–258 (The MIT Press, 1998).

Z. Wang, Q. Ling, and T. S. Huang, “Learning deep l0 encoders,” Proc. Thirtieth AAAI Conference on Artificial Intelligence, 2194–2200 (2016).

K. Gregor and Y. LeCun, “Learning fast approximations of sparse coding,” Proc. 27th International Conference on Machine Learning, 399–406 (2010).

J. Hershey, J. Roux, and F. Weninger, “Deep unfolding: model-based inspiration of novel deep architectures,” ar”Xiv:1409.2574 (2014).

B. Xin, Y. Wang, W. Gao, and D. Wipf, “Maximal sparsity with deep networks?,” ar”Xiv:1605.01636 (2016).

E. Alpaydin, Introduction to Machine Learning, 2nd ed. (China Machine Press, 2014).

A. K. Wong, Resolution Enhancement Techniques in Optical Lithography(SPIE, 2001).

[Crossref]

X. Ma and G. R. Arce, Computational Lithography, Wiley Series in Pure and Applied Optics, 1st ed. (John Wiley and Sons, 2010).

[Crossref]