Backend dielectric reliability simulator for microprocessor system

Chen, Chang-Chih; Ahmed, Fahad; Kim, Dae Hyun; Lim, Sung Kyu; Milor, Linda

doi:10.1016/j.microrel.2012.07.002

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…2.1shows the geometries that are present in the actual layout, aside from parallel line. Their study confirms that the irregular geometries affect the failure times of the dielectric[83,84].…”

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confidence: 53%

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Investigation of interconnect layout on CU/Low-K TDDB reliability

Ong¹

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confidence: 53%

“…A study was done on the layout geometry of the actual circuit interconnect designs. Other than long parallel lines, they also contain head-to-head, head-to-line shapes [83,84]. A scanning electron microscope (SEM)…”

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confidence: 99%

Investigation of interconnect layout on CU/Low-K TDDB reliability

Ong¹

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“…Moreover, the loss function of Adaboost will be modified to consider the classification costs. Moreover, we will also investigate the application of the proposed algorithm to information security [41,43,53,42,41,52,18], bioinformatics [40,24,57], medial imaging [56,55,54], computer vision [38,23,22,36,37,19,17,46,44,45,15], reinforcement learning [27,28], cloud computing [50,51] and microprocessor reliability modeling [8,6,7,5,59,61,60].…”

Section: Discussionmentioning

confidence: 99%

Online classifier adaptation for cost-sensitive learning

Zhang

García

2015

Neural Comput & Applic

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In this paper, we propose the problem of online cost-sensitive classifier adaptation and the first algorithm to solve it. We assume we have a base classifier for a cost-sensitive classification problem, but it is trained with respect to a cost setting different to the desired one. Moreover, we also have some training data samples streaming to the algorithm one by one. The problem is to adapt the given base classifier to the desired cost setting using the steaming training samples online. To solve this problem, we propose to learn a new classifier by adding an adaptation function to the base classifier, and update the adaptation function parameter according to the streaming data samples. Given a input data sample and the cost of misclassifying it, we update the adaptation function parameter by minimizing cost weighted hinge loss and respecting previous learned parameter simultaneously. The proposed algorithm is compared to both online and off-line cost-sensitive algorithms on two cost-sensitive classification problems, and the experiments show that it not only outperforms them one classification performances, but also requires significantly less running time.

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“…The activity and state profiles are needed to determine the thermal profiles and electrical stress of each feature in a system [1]- [8]. Taking into account the detailed thermal and electrical stress profiles, a methodology was developed to accurately assess state-of-art microprocessor reliability due to different wearout mechanisms.…”

Section: Discussionmentioning

confidence: 99%

System-level modeling and reliability analysis of microprocessor systems

Chen

Milor

2013

5th IEEE International Workshop on Advances in Sensors and Interfaces IWASI

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I am particularly thankful to my parents, Lo-Wen Chen and Shu-Yuan Tang, who have always been on my side, for their love and encouragement throughout my life. I also thank my grandfather, grandmothers, brother, Hsuan-Chih Chen, and sister, Jou-Chou Chen, for their love and support. Last but not least, I would like to thank all the professors, teachers, families, and friends who guided me to become the person that I am today.v

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Backend dielectric reliability simulator for microprocessor system

Cited by 8 publications

References 18 publications

Investigation of interconnect layout on CU/Low-K TDDB reliability

Investigation of interconnect layout on CU/Low-K TDDB reliability

Online classifier adaptation for cost-sensitive learning

System-level modeling and reliability analysis of microprocessor systems

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