Statistical Leakage Analysis Using Gaussian Mixture Model
In the design process of advanced semiconductor devices, statistical leakage analysis has emerged as a major step due to uncertainties in the leakage current caused by the process variations. In this paper, a novel statistical leakage analysis which uses Gaussian mixture model (GMM) as the density f...
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2018-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/8466761/ |
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doaj-4e8f096053b2415a99f37390b0be50f42021-03-29T21:04:35ZengIEEEIEEE Access2169-35362018-01-016519395195010.1109/ACCESS.2018.28705288466761Statistical Leakage Analysis Using Gaussian Mixture ModelHyunjeong Kwon0https://orcid.org/0000-0003-3356-0357Mingyu Woo1Young Hwan Kim2https://orcid.org/0000-0002-5532-610XSeokhyeong Kang3Department of Electrical Engineering, Pohang University of Science and Technology, Pohang, South KoreaDepartment of Computer Engineering, University of California at San Diego, La Jolla, CA, USADepartment of Electrical Engineering, Pohang University of Science and Technology, Pohang, South KoreaDepartment of Electrical Engineering, Pohang University of Science and Technology, Pohang, South KoreaIn the design process of advanced semiconductor devices, statistical leakage analysis has emerged as a major step due to uncertainties in the leakage current caused by the process variations. In this paper, a novel statistical leakage analysis which uses Gaussian mixture model (GMM) as the density function of leakage current is proposed. To estimate the probability density function, our proposed method clusters the rapidly converged leakage data using the GMM. The GMM can represent any distributions, soit is suitable to estimate the leakage distribution, which varies as the technology node or operating condition changes. In addition, our proposed method (SLA-GMM) defines a terminating condition that guarantees the convergence of the leakage data and prevents the underfitting or overfitting in the GMM modeling process. With sequential addition, SLA-GMM significantly reduced the error that can occur during the addition process. In studies with a goodness-of-fit test, SLA-GMM achieved up to 98% and 94% improvements in the Chi-square static and the K-S static compared with the previous method based on an analytic model.https://ieeexplore.ieee.org/document/8466761/Expectation-maximization algorithmGaussian mixture modelmachine learningstatistical leakage analysis |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Hyunjeong Kwon Mingyu Woo Young Hwan Kim Seokhyeong Kang |
| spellingShingle |
Hyunjeong Kwon Mingyu Woo Young Hwan Kim Seokhyeong Kang Statistical Leakage Analysis Using Gaussian Mixture Model IEEE Access Expectation-maximization algorithm Gaussian mixture model machine learning statistical leakage analysis |
| author_facet |
Hyunjeong Kwon Mingyu Woo Young Hwan Kim Seokhyeong Kang |
| author_sort |
Hyunjeong Kwon |
| title |
Statistical Leakage Analysis Using Gaussian Mixture Model |
| title_short |
Statistical Leakage Analysis Using Gaussian Mixture Model |
| title_full |
Statistical Leakage Analysis Using Gaussian Mixture Model |
| title_fullStr |
Statistical Leakage Analysis Using Gaussian Mixture Model |
| title_full_unstemmed |
Statistical Leakage Analysis Using Gaussian Mixture Model |
| title_sort |
statistical leakage analysis using gaussian mixture model |
| publisher |
IEEE |
| series |
IEEE Access |
| issn |
2169-3536 |
| publishDate |
2018-01-01 |
| description |
In the design process of advanced semiconductor devices, statistical leakage analysis has emerged as a major step due to uncertainties in the leakage current caused by the process variations. In this paper, a novel statistical leakage analysis which uses Gaussian mixture model (GMM) as the density function of leakage current is proposed. To estimate the probability density function, our proposed method clusters the rapidly converged leakage data using the GMM. The GMM can represent any distributions, soit is suitable to estimate the leakage distribution, which varies as the technology node or operating condition changes. In addition, our proposed method (SLA-GMM) defines a terminating condition that guarantees the convergence of the leakage data and prevents the underfitting or overfitting in the GMM modeling process. With sequential addition, SLA-GMM significantly reduced the error that can occur during the addition process. In studies with a goodness-of-fit test, SLA-GMM achieved up to 98% and 94% improvements in the Chi-square static and the K-S static compared with the previous method based on an analytic model. |
| topic |
Expectation-maximization algorithm Gaussian mixture model machine learning statistical leakage analysis |
| url |
https://ieeexplore.ieee.org/document/8466761/ |
| work_keys_str_mv |
AT hyunjeongkwon statisticalleakageanalysisusinggaussianmixturemodel AT mingyuwoo statisticalleakageanalysisusinggaussianmixturemodel AT younghwankim statisticalleakageanalysisusinggaussianmixturemodel AT seokhyeongkang statisticalleakageanalysisusinggaussianmixturemodel |
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1724193612135137280 |