受惠於第五代行動通訊技術(5th generation mobile networks,5G)的快速發展，帶動印刷電路板(Printed Circuit Board, PCB)產品中的軟板(Flexible Print Circuit, FPC)等PCB產品發展。本文之數位微影系統使用數位微反射鏡陣列(Digital Micromirror Device, DMD)取代光罩，並能應用在PCB的相關產品中，本文中提到之數位微影曝光系統並不拘限於PCB中，可將此系統同樣應用於先進封裝製程。因DMD具有可重複利用以及可快速變換曝光圖片之特性，使用DMD進行曝光能夠有效的降低製作光罩成本以及增加曝光自由度以實時進行補償。本文能量規劃演算法為基於生成對抗網路(Generative Adversarial Network，GAN)之改良，稱做DLithoGAN，針對數位微影特色設計其目標函數，以解決曝光的光學誤差及重新規劃能量分布。DLithoGAN在優化300mm×75mm的PCB圖案上，只需要花費12.5小時即可完成優化計算時間。在計算CD Error測試上，DLithoGAN可控制7.4%的CD Error誤差內，此外，DLithoGAN可達60%的改善率。DLithoGAN在針對大範圍印刷電路圖案能兼具計算效率及品質。本論文亦將DLithoGAN優化後的掃描位置圖案使用數位微影系統使用長軸為20μm光點定義線寬10μm之印刷電路板圖案及先進封裝圖案。

Benefiting from the rapid growth of 5th generation mobile networks (5G) in 2020, it will drive the development of Flexible Print Circuit (FPC) and many related products of Printed Circuit Board (PCB) industry. The digital lithography system in this research uses a digital micromirror device (DMD) to replace the mask and is able to apply in PCB related products. The digital lithography exposure system mentioned in this research can apply to not only PCB industry, but also advanced packaging processes. Due to the characteristics of reusability and rapid change of exposure pictures, using DMD for exposure can effectively reduce the cost of making masks and increases the exposure freedom to compensate in real time. The dosage planning algorithm in this study is based on the improvement of Generative Adversarial Network (GAN), called DLithoGAN. Its objective function is designed for digital lithography process, and the system function of digital lithography is added to solve the optical distortion of exposure and improve the dosage map. The DLithoGAN only needs 12.5 hours to optimize the 300mm×75mm pattern size; in the CD Error test, DLithoGAN controls the CD Error within 7.4%; in addition, DLithoGAN attains an improvement rate of 60% in the aerial image quality factors. It is proved that DLithoGAN has both efficiency and quality for larger PCB area size. Finally, DLithoGAN can optimize the scanning position pattern using digital lithography system. The 20μm light spot is used to PCB and advanced semiconductor packaging patterns with the feature width of 10μm.

[1] 每日頭條.(2019). PCB行業市場現狀及發展趨勢分析 [Online]. Available FTP: kknews.cc
[2] TPCA. (2019). HKPCA Show觀察及中國PCB市場概況 [Online]. Available FTP:tpca.org.tw
[3] 邱鈺傑, and 林俊隆. "印刷電路板無光罩數位成像資料處理技術." 電腦與通訊 , pp. 114-122, 2016.
[4] Chien, H. L., and Y. C. Lee. "Three Dimensional Maskless Ultraviolet Exposure System Based on Digital Light Processing." International Journal of Precision Engineering and Manufacturing, pp. 1-9, 2020.
[5] Liu, Yong, and Avideh Zakhor. "Binary and phase shifting mask design for optical lithography." IEEE Transactions on Semiconductor Manufacturing 5.2, pp. 138-152, 1992.
[6] Oh, Yong-Ho, Jai-Cheol Lee, and Sungwoo Lim. "Resolution enhancement through optical proximity correction and stepper parameter optimization for 0.12-um mask pattern." Optical Microlithography XII, vol. 3679, International Society for Optics and Photonics, 1999.
[7] Chen-Hua Yu, and Der-Chyang Yeh. "Fan-out package structure and methods for forming the same." U.S. Patent no. 8,803,306. 12 Aug., 2014.
[8] Guo, Moran, et al. "Efficient source mask optimization method for reduction of computational lithography cycles and enhancement of process-window predictability." Journal of Micro/Nanolithography, MEMS, and MOEMS, vol. 14, no. 4, pp.043507, 2015.
[9] Mao, Y. C., Chang, C. C., Hung, J. P., & Lin, J. C. . "Package with a fan-out structure and method of forming the same." U.S. Patent, no. 8,785,299. 22 Jul., 2014.
[10] Capodieci, Luigi. "From Optical Proximity Correction to Lithography-Driven Physical Design (1996-2006): 10 years of Resolution Enhancement Technology and the roadmap enablers for the next decade." Optical Microlithography XIX, vol. 6154, International Society for Optics and Photonics., 2006.
[11] Poonawala, Amyn, and Peyman Milanfar. "Mask design for optical microlithography—an inverse imaging problem." IEEE Transactions on Image Processing, vol.16, no.3, pp.774-788, 2007.
[12] Goodfellow, Ian, et al. "Generative adversarial nets." Advances in neural information processing systems., 2014.
[13] T. Brunner, et al. "Characterization and mitigation of overlay error on silicon wafers with nonuniform stress." Optical Microlithography XXVII, vol. 9052, International Society for Optics and Photonics., 2014.
[14] Omid T. Ghalehbeygi, et al. "Gradient-based optimization for efficient exposure planning in maskless lithography." Journal of Micro/Nanolithography, MEMS, and MOEMS, vol. 16, pp.033507, 2017.
[15] Mirza, Mehdi, and Simon Osindero. "Conditional generative adversarial nets." arXiv preprint arXiv:1411.1784, 2014.
[16] Radford, Alec, Luke Metz, and Soumith Chintala. "Unsupervised representation learning with deep convolutional generative adversarial networks." arXiv preprint arXiv:1511.06434, 2015.
[17] Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition., 2015.
[18] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems., 2012.
[19] Noh, Hyeonwoo, Seunghoon Hong, and Bohyung Han. "Learning deconvolution network for semantic segmentation." Proceedings of the IEEE international conference on computer vision., 2015.
[20] Ledig, Christian, et al. "Photo-realistic single image super-resolution using a generative adversarial network." Proceedings of the IEEE conference on computer vision and pattern recognition., 2017.
[21] Ehsani, Kiana, Roozbeh Mottaghi, and Ali Farhadi. "Segan: Segmenting and generating the invisible." Proceedings of the IEEE conference on computer vision and pattern recognition., 2018.
[22] Isola, Phillip, et al. "Image-to-image translation with conditional adversarial networks." Proceedings of the IEEE conference on computer vision and pattern recognition., 2017.
[23] Zhu, Jun-Yan, et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." Proceedings of the IEEE international conference on computer vision., 2017.
[24] Kim, Taeksoo, et al. "Learning to discover cross-domain relations with generative adversarial networks." Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017.
[25] Ye, Wei, et al. "Lithogan: End-to-end lithography modeling with generative adversarial networks." 2019 56th ACM/IEEE Design Automation Conference (DAC). IEEE, 2019.
[26] Yang, Haoyu, et al. "GAN-OPC: Mask optimization with lithography-guided generative adversarial nets." Proceedings of the 55th Annual Design Automation Conference., 2018.
[27] Alawieh, Mohamed Baker, et al. "Gan-sraf: Sub-resolution assist feature generation using conditional generative adversarial networks." Proceedings of the 56th Annual Design Automation Conference 2019., 2019.
[28] Yang, Haoyu, et al. "Imbalance aware lithography hotspot detection: a deep learning approach." Journal of Micro/Nanolithography, MEMS, and MOEMS, vol. 16, no. 3, pp.033504, 2017.
[29] Shin, Moojoon, and Jee-Hyong Lee. "Accurate lithography hotspot detection using deep convolutional neural networks." Journal of Micro/Nanolithography, MEMS, and MOEMS, vol. 15, no .4, pp.043507, 2016.
[30] Sim, Woojoo, et al. "Automatic correction of lithography hotspots with a deep generative model." Optical Microlithography XXXII. Vol. 10961. International Society for Optics and Photonics, 2019.
[31] Lin, Hsien-I., and Pedro Menendez. "Image Denoising of Printed Circuit Boards using Conditional Generative Adversarial Network." 2019 IEEE 10th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT). IEEE, 2019.
[32] Barbucha, R., et al. "Laser direct imaging of the printed electrical circuits on PCB." Lasers and Applications, vol. 5958, International Society for Optics and Photonics, 2005.
[33] Chan, Kin Foong, et al. "High-resolution maskless lithography," Journal of Micro/Nanolithography, MEMS, and MOEMS, vol. 2, no. 4, pp.331-340, 2003.
[34] Chan, Kin Foong, et al. "High-resolution maskless lithography by the integration of micro-optics and point array technique." MOEMS Display and Imaging Systems. vol. 4985. International Society for Optics and Photonics, 2003.
[35] Hasan, Md Nazmul, et al. "Maskless beam pen lithography based on integrated microlens array and spatial-filter array." Optical Engineering, vol. 56, no. 11, pp. 115104, 2017.
[36] Karras, Tero, Samuli Laine, and Timo Aila. "A style-based generator architecture for generative adversarial networks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019.
[37] Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.
[38] Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." arXiv preprint arXiv:1502.03167, 2015.
[39] Agarap, Abien Fred. "Deep learning using rectified linear units (relu)," arXiv preprint arXiv:1803.08375, 2018.
[40] Yu, Fisher, and Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions," arXiv preprint arXiv:1511.07122, 2015.
[41] Dumoulin, Vincent, and Francesco Visin. "A guide to convolution arithmetic for deep learning," arXiv preprint arXiv:1603.07285, 2016.
[42] Ulyanov, Dmitry, Andrea Vedaldi, and Victor Lempitsky. "Instance normalization: The missing ingredient for fast stylization," arXiv preprint arXiv:1607.08022, 2016.
[43] Johnson, Justin, Alexandre Alahi, and Li Fei-Fei. "Perceptual losses for real-time style transfer and super-resolution," European conference on computer vision. Springer, Cham, 2016.
[44] Wu, Yuxin, and Kaiming He. "Group normalization," Proceedings of the European Conference on Computer Vision (ECCV), 2018.
[45] Kingma, Diederik P., and Jimmy Ba. "Adam: A method for stochastic optimization." arXiv preprint arXiv:1412.6980, 2014.
[46] Sutskever, Ilya, et al. "On the importance of initialization and momentum in deep learning." International conference on machine learning, 2013.
[47] Fred Glover, and Manuel Laguna. "Tabu search." Handbook of combinatorial optimization. 2093-2229, 1998.
[48] Fred Glover. "Tabu search—part I." ORSA Journal on computing, vol. 1, pp. 190-206, 1989.
[49] Fred Glover, "Tabu search—part II." ORSA Journal on computing, vol. 2, pp. 4-32, 1990.
[50] Fred Glover. "Tabu search: A tutorial." Interfaces, vol. 20, pp. 74-94, 1990.
[51] Roberto Battiti, and Giampietro Tecchiolli. "The reactive tabu search." ORSA journal on computing, vol. 6, pp. 126-140, 1994.
[52] Fred Glover, and Eric Taillard. "A user's guide to tabu search." Annals of operations research, vol. 41, pp. 1-28, 1993.
[53] Alain Hertz, and Dominique de Werra. "Using tabu search techniques for graph coloring." Computing , vol. 39, pp. 345-351, 1987.