Article
Engineering, Electrical & Electronic
Xu Ma, Yihua Pan, Shengen Zhang, Javier Garcia-Frias, Gonzalo R. Arce
Summary: Optical lithography is essential for fabricating nano-scale semiconductor devices by replicating integrated circuit layouts onto silicon wafers. Source and mask optimization methods are used to improve resolution and image fidelity, with the introduction of an informational lithography approach revealing the information transmission mechanism in lithography systems. Through optimal information transfer, a lower bound for lithography pattern error is derived, leading to a new SMO algorithm based on information theory for enhanced image fidelity.
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
(2021)
Article
Optics
Austin Peng, Stephen D. Hsu, Rafael C. Howell, Qinglin Li
Summary: In the field of computational lithography, the performance of an optimized imaging solution is usually evaluated with a full-chip check to ensure defect-free printing. The optimization process itself needs to be driven by the same defect detection mechanism, but the large data size of chip layout poses challenges that require a gradient-based optimization scheme.
Article
Optics
Junbo Liu, Ji Zhou, Haifeng Sun, Chuan Jin, Jian Wang, Song Hu
Summary: Source mask optimization (SMO) is a method for improving image quality in high-node lithography, and algorithm optimization plays a critical role. A hybrid GA-APSO algorithm was proposed to globally optimize pixelated source and mask distributions in lithographic imaging, combining genetic algorithm (GA) and adaptive particle swarm optimization (APSO). The GA-APSO algorithm improved computational efficiency by balancing global and local search using adaptive strategies. Experimental results showed that GA-APSO outperformed GA and APSO in terms of pattern error reduction and time cost. The stability of GA-APSO results was also demonstrated. The study highlights the superior efficiency, accuracy, and repeatability of GA-APSO for source and mask optimization.
Article
Optics
Yiyu Sun, Yanqiu Li, Guanghui Liao, Miao Yuan, Pengzhi Wei, Yaning Li, Lulu Zou, Lihui Liu
Summary: Current research on source and mask optimization focuses on advanced inverse optimization algorithms to speed up the SMO procedures. However, little attention is given to innovations in forward imaging models, which can have a greater impact on computational efficiency. By establishing a sampling-based imaging model with an innovative inverse point spread function, a more efficient framework for fast inverse lithography is provided, accelerating the SMO procedure by a factor of 3.
Article
Optics
Zinan Zhang, Sikun Li, Xiangzhao Wang, Wei Cheng, Yuejing Qi
Summary: This paper introduces an EUV lithography SMO method based on the thick mask model and SL-PSO algorithm, which effectively improves imaging quality and reduces pattern errors.
Article
Optics
Fei Peng, Yiduo Xu, Yi Song, Chengqun Gui, Yan Zhao
Summary: A source and mask optimization method based on defocus generative and adversarial method (DGASMO) is proposed in this paper to improve the depth of focus (DOF) in photolithography. By adjusting the parameters of the source, mask, and defocus, the size of DOF can be controlled, optimizing the robustness of the lithography process and process window.
Article
Optics
Guodong Chen, Sikun Li, Xiangzhao Wang
Summary: Optical proximity correction (OPC) is an important enhancement technique in optical lithography to improve image fidelity and process robustness, especially for advanced technology nodes. The proposed efficient OPC method based on virtual edge and mask pixelation with two-phase sampling effectively addresses imaging distortions and demonstrates superior modification efficiency.
Article
Computer Science, Hardware & Architecture
Guojin Chen, Wanli Chen, Qi Sun, Yuzhe Ma, Haoyu Yang, Bei Yu
Summary: This article introduces a high-performance and scalable deep-learning-enabled optical proximity correction (OPC) system for full-chip scale. The system includes lithography modeling and mask pattern generation, and proposes a novel layout splitting algorithm for full-chip OPC. Graph-based computation and parallelism techniques are used to accelerate computations. Extensive experiments show that the system outperforms state-of-the-art solutions in both academia and industry.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Junbo Liu, Ji Zhou, Dajie Yu, Haifeng Sun, Song Hu, Jian Wang
Summary: In this study, a source optimization method based on a hybrid genetic algorithm is proposed to achieve an acceptable source shape in the imaging process for optical lithography. The method utilizes particle swarm optimization and the tabu list method from the tabu search algorithm to overcome the problems of local optima and small search scope, enhancing the optimization performance. Different feature patterns are employed as the input of the optimization model. Simulation results show that the proposed method exhibits dominant optimization performance for source optimization in optical lithography.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Hardware & Architecture
Wei Zhong, Shuxiang Hu, Yuzhe Ma, Haoyu Yang, Xiuyuan Ma, Bei Yu
Summary: This article proposes a prediction method based on convolutional neural networks and integrates it into the layout decomposition and mask optimization process. The method improves the efficiency of the optimization process and provides more accurate predictions.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2022)
Article
Optics
Jiewen Tian, Xin Ye, Wei Fang
Summary: The text discusses a freeform reflector designed for a space-based remote sensor calibration system, aiming to produce desired patterns and improve efficiency. By proposing a design for an integral sphere source, the reflector achieves independent irradiance distribution and large-scale illumination with high uniformity and efficiency.
Article
Computer Science, Hardware & Architecture
Ziyang Yu, Guojin Chen, Yuzhe Ma, Bei Yu
Summary: As the feature size of advanced integrated circuits continues to shrink, resolution enhancement techniques (RETs) are employed to enhance the printability in the lithography process. Optical proximity correction (OPC) is a widely used RET that compensates for the mask to generate a more precise wafer image. In this article, a level-set-based OPC approach is proposed with high mask optimization quality and fast convergence. The algorithm utilizes a new process window-aware cost function to suppress the disturbance of condition fluctuations and incorporates a momentum-based evolution technique for significant improvement. Additionally, a self-adaptive conjugate gradient method and graphics processing unit (GPU) acceleration are introduced to further enhance stability and reduce execution time. Experimental results on ICCAD 2013 benchmarks demonstrate that the proposed algorithm outperforms previous OPC algorithms in terms of solution quality and runtime overhead.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2023)
Article
Optics
Zinan Zhang, Sikun Li, Xiangzhao Wang, Wei Cheng
Summary: In this paper, a fast SMO method for EUV based on dual edge evolution and partial sampling strategies is proposed to improve the optimization efficiency and speed of the heuristic algorithm. The method shows superiority over previous methods, especially for large complex patterns, by optimizing source and mask features using dimensionality reduction strategies.
Article
Multidisciplinary Sciences
Sanmun Kim, Chanhyung Park, Shinho Kim, Haejun Chung, Min Seok Jang
Summary: This work reports on the influence of design parameters on the optical efficiency of metasurface-based color splitters, as well as the possibility of fabricating them in legacy fabrication facilities with low structure resolutions.
Article
Engineering, Electrical & Electronic
Pierre Chevalier, Patrick Quemere, Sebastien Berard-Bergery, Jean-Baptist Henry, Charlotte Beylier, Jerome Vaillant
Summary: Grayscale mask creation has traditionally relied on simplified models, but a new rigorous lithographic model has been developed to optimize the size and position of dots on a mask. Experimental results confirm the algorithm's ability to achieve microstructures with dimensions ranging between 1 to 3 micrometers.
JOURNAL OF MICROELECTROMECHANICAL SYSTEMS
(2021)
Article
Optics
Yanmin Zhu, Hau Kwan Abby Lo, Chok Hang Yeung, Edmund Y. Lam
Summary: This study proposes a zero-shot learning method that combines holographic images with semantic attributes for the identification of microplastics, achieving promising results in various environmental samples.
Article
Optics
Zhou Ge, Haoyu Wei, Feng Xu, Yizhao Gao, Zhiqin Chu, Hayden K. -H. So, Edmund Y. Lam
Summary: This study presents a new approach to achieve fast autofocusing in microscopic imaging using neuromorphic event sensing technology. The proposed method can detect sparse brightness changes asynchronously and respond quickly to specimen movement, enabling autofocusing in just tens of milliseconds, which is thousands of times faster than current technologies. Experimental results demonstrate a substantial performance improvement and capability for biopsy specimen inspections.
OPTICS AND LASERS IN ENGINEERING
(2023)
Article
Optics
Xinming Guo, Yixuan Li, Jiaming Qian, Yuxuan Che, Chao Zuo, Qian Chen, Edmund Y. Lam, Huai Wang, Shijie Feng
Summary: Temporal phase unwrapping is an important method for recovering discontinuous surfaces or spatially isolated objects in fringe projection profilometry. Existing algorithms can be classified into three types, but all require additional fringe patterns of different frequencies to retrieve the absolute phase. Image noise limits the efficiency and speed of phase unwrapping. This work shows for the first time that a generalized framework using deep learning can effectively mitigate noise and enhance phase unwrapping reliability, without increasing the number of auxiliary patterns. The proposed method has great potential for developing powerful and reliable phase retrieval techniques.
Article
Optics
L. Song, Edmund Y. Lam
Summary: In this paper, a learning-based recursive dual alternating direction method of multipliers (RD-ADMM) is proposed for phase retrieval. The method solves the phase retrieval problem by solving the primal and dual problems separately. By designing a dual structure, the information embedded in the dual problem is utilized to help with solving the phase retrieval problem, and it is shown that the same operator can be used for regularization in both the primal and dual problems. Experiments demonstrate that our method is effective and robust, providing higher-quality results than other commonly-used phase retrieval methods for this setup.
Article
Optics
Yunping Zhang, Stanley H. H. Chan, Edmund Y. Lam
Summary: Digital holography (DH) is a powerful imaging modality that captures object wavefront information, but in low-light situations, the quality of holograms suffers. We developed a snapshot DH system that operates at an ultra-low photon level by using a quanta image sensor and computational reconstruction. Our method expands DH to the photon-starved regime and enables more advanced holography applications.
Article
Biology
Ziqi Zhang, Kelvin C. M. Lee, Dickson M. D. Siu, Michelle C. K. Lo, Queenie T. K. Lai, Edmund Y. Lam, Kevin K. Tsia
Summary: This article introduces an image-based approach that can quantify multiple biophysical fractal-related properties of single cells at subcellular resolution. This technique has high-throughput single-cell imaging performance and offers sufficient statistical power for cell classification, drug response assays, and cell-cycle progression tracking.
COMMUNICATIONS BIOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Shansi Zhang, Nan Meng, Edmund Y. Lam
Summary: This paper proposes an efficient Low-light Restoration Transformer (LRT) for LF images under low-light conditions. The method utilizes multiple heads to perform intermediate tasks within a single network, achieving progressive restoration from small scale to full scale. Experimental results show that the proposed method achieves state-of-the-art performance on low-light LF restoration with high efficiency.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Pei Zhang, Zhou Ge, Li Song, Edmund Y. Lam
Summary: Bio-inspired neuromorphic cameras asynchronously record visual information of dynamic scenes by discrete events. Due to the high sampling rate, they are capable of fast motion capture without causing image blur, overcoming the drawbacks of frame-based cameras that produce blurry recordings of dynamic objects. However, highly sensitive neuromorphic cameras are also susceptible to interference, and can generate a lot of noise in response. Such noisy event data can dramatically degrade the event-based observations and analysis.
IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
(2023)
Correction
Optics
Boyi Huang, Jia Li, Bowen Yao, Zhigang Yang, Edmund Y. Lam, Jia Zhang, Wei Yan, Junle Qu
Article
Optics
Boyi Huang, Jia Li, Bowen Yao, Zhigang Yang, Edmund Y. Lam, Jia Zhang, Wei Yan, Junle Qu
Summary: Super-resolution optical imaging is crucial to studying cellular processes. This study presents a deep-learning-based super-resolution technique for confocal microscopy. The proposed algorithm, using a two-channel attention network, can handle changes in pixel size and imaging setup, and demonstrate live-cell super-resolution imaging of microtubules.
Article
Computer Science, Artificial Intelligence
Pei Zhang, Chutian Wang, Edmund Y. Lam
Summary: This paper proposes a new graph representation for event data and couples it with a Graph Transformer for accurate neuromorphic classification. Results show that this approach performs well in challenging realistic situations with limited computational resources and a small number of events.
Proceedings Paper
Geosciences, Multidisciplinary
Peiyan Guan, Edmund Y. Lam
Summary: This paper proposes a method called spatial-spectral contrastive learning (SSCL) to learn representations of hyperspectral images (HSI) suitable for classification in an unsupervised manner. By defining a contrastive prediction task, the learned representations can extract useful contents from the domain-invariant information.
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Chang Liu, Xiaoyan Qian, Binxiao Huang, Xiaojuan Qi, Edmund Lam, Siew-Chong Tan, Ngai Wong
Summary: In this study, an end-to-end multimodal transformer autolabeler is proposed to generate precise 3D box annotations using LiDAR scans and images. By densifying sparse point clouds and adopting a multi-task design, the autolabeler achieves improved label quality and accuracy for 3D object detection.
COMPUTER VISION, ECCV 2022, PT XXXVIII
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Shansi Zhang, Edmund Y. Lam
Summary: This study proposes a method to recover high-quality LF images from low-light detection. The LF images are decomposed into reflectance and illumination using a decomposition network. Two enhancement networks are then used to denoise the reflectance and enhance the illumination. A parallel dual attention mechanism and a discriminator are also employed to improve the realism and encode important information of the images.
2022 26TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Chang Liu, Xiaoyan Qian, Xiaojuan Qi, Edmund Y. Lam, Siew-Chong Tan, Ngai Wong
Summary: This study proposes a novel autolabeler called MAP-Gen, which can generate high-quality 3D labels from weak 2D boxes. By leveraging dense image information to tackle the sparsity issue of 3D point clouds, the label quality is improved. Object detection networks weakly supervised by 2D boxes using MAP-Gen can achieve similar performance to those fully supervised by 3D annotations.
2022 26TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
(2022)
