A Fuzzy-Matching Model With Grid Reduction for Lithography Hotspot Detection

Wen, Wan-Yu; Li, Jincheng; Lin, Sheng-Yuan; Chen, Jingyi; Chang, Shih-Chieh

doi:10.1109/tcad.2014.2351273

Cited by 68 publications

(2 citation statements)

References 21 publications

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“…For comparison, we employ the convolutional neural network ResNet to predict the capacitance matrix. A density-based data representation method [11,29] is used to describe the geometric characteristics of the metal layer and represents the input of ResNet. This method can effectively describe the layout feature [23].…”

Section: Density-based Data Representationmentioning

confidence: 99%

Extraction of Interconnect Parasitic Capacitance Matrix Based on Deep Neural Network

Yan

et al. 2023

Electronics

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Interconnect parasitic capacitance extraction is crucial in analyzing VLSI circuits’ delay and crosstalk. This paper uses the deep neural network (DNN) to predict the parasitic capacitance matrix of a two-dimensional pattern. To save the DNN training time, the neural network’s output includes only coupling capacitances in the matrix, and total capacitances are obtained by summing corresponding predicted coupling capacitances. In this way, we can obtain coupling and total capacitances simultaneously using a single neural network. Moreover, we introduce a mirror flip method to augment the datasets computed by the finite element method (FEM), which doubles the dataset size and reduces data preparation efforts. Then, we compare the prediction accuracy of DNN with another neural network ResNet. The result shows that DNN performs better in this case. Moreover, to verify our method’s efficiency, the total capacitances calculated from the trained DNN are compared with the network (named DNN-2) that takes the total capacitance as an extra output. The results show that the prediction accuracy of the two methods is very close, indicating that our method is reliable and can save the training workload for the total capacitance. Finally, a solving efficiency comparison shows that the average computation time of the trained DNN for one case is not more than 2% of that of FEM.

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Section: Density-based Data Representationmentioning

confidence: 99%

Extraction of Interconnect Parasitic Capacitance Matrix Based on Deep Neural Network

Yan

et al. 2023

Electronics

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“…To address these issues, researchers have used fuzzy techniques to integrate into machine learning called Fuzzy Machine Learning [1] (FML) as a solution, since fuzzy techniques are successful to deal with uncertainties. FML systems fuse machine learning algorithms with fuzzy techniques, such as fuzzy sets [2], fuzzy systems [3], fuzzy clustering [4], fuzzy relations [5], fuzzy measures [6], fuzzy matching [7], fuzzy optimization [8], and so on, to build new models that are more robust to the many and varied types of uncertainty found in real-world problems.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review

Lu,

Ma,

Zhang

2024

IEEE Trans. Fuzzy Syst.

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Machine learning draws its power from various disciplines, including computer science, cognitive science, and statistics. Although machine learning has achieved great advancements in both theory and practice, its methods have some limitations when dealing with complex situations and highly uncertain environments. Insufficient data, imprecise observations, and ambiguous information/relationships can all confound traditional machine learning systems. To address these problems, researchers have integrate machine leaning from different aspects, and fuzzy techniques including fuzzy sets, fuzzy systems, fuzzy logic, fuzzy measures, fuzzy relations, and so on. This paper presents a systematic review of fuzzy machine learning, from theory, approach to application, with the overall objective of providing an overview of recent achievements in the field of fuzzy machine learning. To this end, the concepts and frameworks discussed are divided into five categories: (a) fuzzy classical machine learning; (b) fuzzy transfer learning; (c) fuzzy data stream learning; (d) fuzzy reinforcement learning; and (e) fuzzy recommender systems. The literature presented should provide researchers with a solid understanding of the current progress in fuzzy machine learning research and its applications.

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Machine Learning in Physical Verification, Mask Synthesis, and Physical Design

Lin

Pan

2019

Machine Learning in VLSI Computer-Aided Design

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A Fuzzy-Matching Model With Grid Reduction for Lithography Hotspot Detection

Cited by 68 publications

References 21 publications

Extraction of Interconnect Parasitic Capacitance Matrix Based on Deep Neural Network

Extraction of Interconnect Parasitic Capacitance Matrix Based on Deep Neural Network

Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review

Machine Learning in Physical Verification, Mask Synthesis, and Physical Design

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