[1] 陈翔, 杨音. 集成电路全产业链标准数据统计分析[J]. 中国标准化, 2022(6): 33-37. doi: 10.3969/j.issn.1002-5944.2022.06.007CHENX, YANGY. Statistical analysis of the standards data in the whole IC industry chain[J]. China Standardization, 2022(6): 33-37.(in Chinese). doi: 10.3969/j.issn.1002-5944.2022.06.007
[2] 潘桂忠. 亚微米CMOS芯片与制程剖面结构[J]. 集成电路应用, 2019, 36(3):30-34. doi: 10.19339/j.issn.1674-2583.2019.03.008PANG ZH. Submicron CMOS chips and process profile structure[J]. Applications of IC, 2019, 36(3):30-34.(in Chinese). doi: 10.19339/j.issn.1674-2583.2019.03.008
[3] 张思琪, 周思翰, 杨卓俊, 等. 基于数字微镜器件的无掩膜光刻技术进展[J]. 光学 精密工程, 2022, 30(1):12-30. doi: 10.37188/OPE.20223001.0012ZHANGS Q, ZHOUS H, YANGZH J, et al. Research progress of maskless lithography based on digital micromirror devices[J]. Opt. Precision Eng., 2022, 30(1):12-30.(in Chinese). doi: 10.37188/OPE.20223001.0012
[4] M TANG, Z C CHEN, Z Q HUANG et al. Maskless multiple-beam laser lithography for large-area nanostructure/microstructure fabrication. Applied Optics, 50, 6536-6542(2011).
[5] Y LECUN, L BOTTOU, Y BENGIO et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278-2324(1998).
[6] 卢宏涛, 张秦川. 深度卷积神经网络在计算机视觉中的应用研究综述[J]. 数据采集与处理, 2016, 31(1):1-17. doi: 10.16337/j.1004-9037.2016.01.001LUH T, ZHANGQ CH. Applications of deep convolutional neural network in computer vision[J]. Journal of Data Acquisition & Processing, 2016, 31(1):1-17.(in Chinese). doi: 10.16337/j.1004-9037.2016.01.001
[7] 李旭冬, 叶茂, 李涛. 基于卷积神经网络的目标检测研究综述[J]. 计算机应用研究, 2017, 34(10): 2881-2886, 2891. doi: 10.3969/j.issn.1001-3695.2017.10.001LIX D, YEM, LIT. Review of object detection based on convolutional neural networks[J]. Application Research of Computers, 2017, 34(10): 2881-2886, 2891.(in Chinese). doi: 10.3969/j.issn.1001-3695.2017.10.001
[8] J H YOON, S L INGALE, J S KIM et al. Effects of dietary supplementation of synthetic antimicrobial peptide-A3 and P5 on growth performance, apparent total tract digestibility of nutrients, fecal and intestinal microflora and intestinal morphology in weanling pigs. Livestock Science, 159, 53-60(2014).
[9] 杨荣坚, 王芳, 秦浩. 基于双目图像的行人检测与定位系统研究[J]. 计算机应用研究, 2018, 35(5): 1591-1595, 1600. doi: 10.3969/j.issn.1001-3695.2018.05.068YANGR J, WANGF, QINH. Research of pedestrian detection and location system based on stereo images[J]. Application Research of Computers, 2018, 35(5): 1591-1595, 1600.(in Chinese). doi: 10.3969/j.issn.1001-3695.2018.05.068
[10] H LUO, M HE, B HUI et al. Pedestrian detection algorithm based on dual-model fused fully convolutional networks(Invited). Infrared and Laser Engineering, 47, 203001(2018).
[11] 芮挺, 费建超, 周遊, 等. 基于深度卷积神经网络的行人检测[J]. 计算机工程与应用, 2016, 52(13):162-166. doi: 10.3778/j.issn.1002-8331.1502-0122RUIT, FEIJ CH, ZHOUY, et al. Pedestrian detection based on deep convolutional neural network[J]. Computer Engineering and Applications, 2016, 52(13):162-166.(in Chinese). doi: 10.3778/j.issn.1002-8331.1502-0122
[12] 张新钰, 高洪波, 赵建辉, 等. 基于深度学习的自动驾驶技术综述[J]. 清华大学学报(自然科学版), 2018, 58(4): 438-444. doi: 10.16511/j.cnki.qhdxxb.2018.21.010ZHANGX Y, GAOH B, ZHAOJ H, et al. Overview of autopilot technology based on deep learning[J]. Journal of Tsinghua University (Science and Technology), 2018, 58(4): 438-444.(in Chinese). doi: 10.16511/j.cnki.qhdxxb.2018.21.010
[13] 杨聚圃, 杜佳林, 李凡星, 等. 基于深度学习的数字光刻自动检焦方法[J]. 光子学报, 2022, 51(6): 0611002. doi: 10.3788/gzxb20225106.0611002YANGJ P, DUJ L, LIF X, et al. Deep learning based method for automatic focus detection in digital lithography[J]. Acta Photonica Sinica, 2022, 51(6): 0611002.(in Chinese). doi: 10.3788/gzxb20225106.0611002
[14] 郭求是. 基于深度学习的光刻热点检测技术研究[D]. 杭州: 浙江大学, 2019.GUOQ SH. Research on Lithography Hot Spot Detection Technology based on Deep Learning[D]. Hangzhou: Zhejiang University, 2019. (in Chinese)
[15] Y T YU, G H LIN, I H R JIANG et al. Machine-learning-based hotspot detection using topological classification and critical feature extraction, 1-6(29).
[16] K M HE, X Y ZHANG, S Q REN et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1916(2015).
[18] S Q REN, K M HE, R GIRSHICK et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).
[19] R GIRSHICK, J DONAHUE, T DARRELL et al. Rich feature hierarchies for accurate object detection and semantic segmentation, 23, 2014(2014).
[20] W LIU, D ANGUELOV, D ERHAN et al. SSD:
[21] J REDMON, S DIVVALA, R GIRSHICK et al. You only look once: unified, real-time object detection, 27, 779-788(2016).
[22] J REDMON, A FARHADI. YOLO9000: better, faster, stronger, 21, 6517-6525(2017).
[24] S IOFFE, C SZEGEDY. Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR(2015).
[25] C Y WANG, H Y MARK LIAO, Y H WU et al. CSPNet: a new backbone that can enhance learning capability of CNN, 14, 2020(2020).
[26] K M HE, X Y ZHANG, S Q REN et al. Deep residual learning for image recognition, 27, 770-778(2016).
[27] 李敏学. 基于注意力机制的图像显著区域提取算法分析与比较[D]. 北京: 北京交通大学, 2011.LIM X. Analysis and Comparison of Salient Region Extraction Algorithms Based on Attention Mechanism[D]. Beijing: Beijing Jiaotong University, 2011. (in Chinese)
[28] 焦军峰, 靳国旺, 熊新, 等. 旋转矩形框与CBAM改进RetinaNet的SAR图像近岸舰船检测[J]. 测绘科学技术学报, 2020, 37(6): 603-609.JIAOJ F, JING W, XIONGX, et al. SAR images nearshore ship detection based on RetinaNet algorithm with rotated rectangular box[J]. Journal of Geomatics Science and Technology, 2020, 37(6): 603-609.(in Chinese)
[29] 张世权, 朱斌, 顾霞. 不同衬底材料对光刻胶剖面的影响[J]. 电子与封装, 2013, 13(8): 37-39. doi: 10.3969/j.issn.1681-1070.2013.08.011ZHANGSH Q, ZHUB, GUX. Research of photo resists cross section on different substrate material[J]. Electronics and Packaging, 2013, 13(8): 37-39.(in Chinese). doi: 10.3969/j.issn.1681-1070.2013.08.011
[30] 韦亚一. 超大规模集成电路先进光刻理论与应用[M]. 北京: 科学出版社, 2016.WEIY Y. Theory and Application of Advanced Lithography for VLSI[M]. Beijing: Science Press, 2016.(in Chinese)