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dc.contributor.authorHuang, Tzu-Hsuanen_US
dc.contributor.authorHung, Wei-Tseen_US
dc.contributor.authorYang, Hao-Yuen_US
dc.contributor.authorChang, Wen-Hsiangen_US
dc.contributor.authorChen, Ying-Yenen_US
dc.contributor.authorKuo, Chun-Yien_US
dc.contributor.authorLee, Jih-Nungen_US
dc.contributor.authorChao, Mango C. -T.en_US
dc.date.accessioned2018-08-21T05:56:48Z-
dc.date.available2018-08-21T05:56:48Z-
dc.date.issued2017-01-01en_US
dc.identifier.issn2153-6961en_US
dc.identifier.urihttp://hdl.handle.net/11536/146662-
dc.description.abstractThis paper presents a statistical model-fitting framework to efficiently decompose the impact of device Vt variation and power-network IR drop from the measured ring-oscillator frequencies without adding any extra circuitry to the original ring oscillators. The framework applies Gaussian process regression as its core model-fitting technique and stepwise regression as a preprocess to select significant predictor features. The experiments conducted based on the SPICE simulation of an industrial 28nm technology demonstrate that our framework can simultaneously predict the NMOS Vt, PMOS Vt and static IR drop of the ring oscillators based on their frequencies measured at different external supply voltages. The final resulting R squares of the predicted features are all more than 99.93%.en_US
dc.language.isoen_USen_US
dc.titlePredicting Vt Variation and Static IR Drop of Ring Oscillators Using Model-Fitting Techniquesen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2017 22ND ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE (ASP-DAC)en_US
dc.citation.spage426en_US
dc.citation.epage431en_US
dc.contributor.department電子工程學系及電子研究所zh_TW
dc.contributor.departmentDepartment of Electronics Engineering and Institute of Electronicsen_US
dc.identifier.wosnumberWOS:000403609600086en_US
Appears in Collections:Conferences Paper