ICCAD-2012 CAD contest in fuzzy pattern matching for physical verification and benchmark suite

Torres, J. Andres

doi:10.1145/2429384.2429457

Cited by 90 publications

(33 citation statements)

References 2 publications

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“…As shown in Table 4, it has normalized time is around 25-60 GPU h/mm 2 . Though our method is faster than optical verification method whose runtime is over 100 CPU h/mm 2 , it does not meet the of ICCAD2012 contest requirement (1-10 CPU h/mm 2 ) [8]. But, we can see the normalized runtime is proportional to the candidate region ratio (CR ratio).…”

Section: Detection Timementioning

confidence: 84%

“…We use Theano library [12] to train and test the CNN model. The proposed method was evaluated using industrial benchmarks titled IC-CAD2012 CAD dataset [8] described in Table 1. These 32 nm, 28 nm datasets has around 500-5000 clips whose size is around 5 µm square.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…This model is calibrated with given hotspot and non-hotspot im- But former machine learning method was accompanied by complex frameworks including complex layout encoding (feature extraction) and multi classifier usage due to its lowperformance model usage like SVM and ANN. In spite of its unknown hotspot detection ability and its sophisticated framework usage, it revealed high false positive in ICCAD2012 benchmark dataset [8].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…In spite of its unknown hotspot detection ability and its sophisticated framework usage, it revealed high false positive in ICCAD2012 benchmark dataset [8].…”

mentioning

confidence: 99%

See 3 more Smart Citations

CNN Based Lithography Hotspot Detection

Shin

Lee

2016

IJFIS

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The lithography hotspot detection process is crucial for semiconductor design development process. But, the lithography hotspot detection using optical simulation method takes much time and it slowdown the layout design development cycle. Though the geometry based approach is introduced as an alternative, it still revealed low detection performance and sophisticated framework. To solve this problem, we introduce a deep convolutional neural network based hotspot detection method. Our method made better results in ICCCAD 2012 dataset. To reach this score, we used lots of technical effort to improve the result in addition to just utilizing the nature of convolutional neural network. Inspection region reduction, data augmentation, DBSCAN clustering helped our work more stable and faster.

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Section: Detection Timementioning

confidence: 84%

Section: Experiments Resultsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

“…In spite of its unknown hotspot detection ability and its sophisticated framework usage, it revealed high false positive in ICCAD2012 benchmark dataset [8].…”

mentioning

confidence: 99%

See 2 more Smart Citations

CNN Based Lithography Hotspot Detection

Shin

Lee

2016

IJFIS

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“…Furthermore, to achieve such accuracy, these hybrid models use multiple classifiers to check numerous potential problematic locations in the full layout. As mentioned in [9], such hybrid models may perform as much as 10-100 times slower than pattern-matching approaches.…”

Section: Introductionmentioning

confidence: 99%

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

Wen

Lin

et al. 2014

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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In advanced IC manufacturing, as the gap increases between lithography optical wavelength and feature size, it becomes challenging to detect problematic layout patterns called lithography hotspot. In this paper, we propose a novel fuzzy matching model which extracts appropriate feature vectors of hotspot and nonhotspot patterns. Our model can dynamically tune appropriate fuzzy regions around known hotspots. Based on this paper, we develop a fast algorithm for lithography hotspot detection with high accuracy of detection and low probability of false-alarm counts. In addition, since higher dimensional size of feature vectors can produce better accuracy but requires longer run time, this paper proposes a grid reduction technique to significantly reduce the CPU run time with very minor impact on the advantages of higher dimensional space. Our results are very encouraging, with average 94.5% accuracy and low false-alarm counts on a set of test benchmarks.

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Machine Learning for Mask Synthesis and Verification

Yang

Lin

2022

Machine Learning Applications in Electronic Design Automation

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ICCAD-2012 CAD contest in fuzzy pattern matching for physical verification and benchmark suite

Cited by 90 publications

References 2 publications

CNN Based Lithography Hotspot Detection

CNN Based Lithography Hotspot Detection

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

Machine Learning for Mask Synthesis and Verification

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