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dc.contributor.advisorSanyal, Arindam
dc.contributor.authorVENKATESH, ABILASH
dc.contributor.author0000-0003-2852-2938
dc.date.accessioned2019-07-30T15:11:47Z
dc.date.available2019-07-30T15:11:47Z
dc.date.issued2019
dc.date.submitted2019-05-16 23:56:08
dc.identifier.urihttp://hdl.handle.net/10477/80004
dc.descriptionM.Eng.
dc.description.abstractThis thesis presents a novel architecture of subthreshold voltage divider based strong physical unclonable function (PUF). The PUF derives its uniqueness from random mismatch in threshold voltage in an inverter with gate and drain shorted and biased in subthreshold region. The nonlinear current-voltage relationship in subthreshold region also makes the proposed PUF resistant to machine learning (ML) based attacks. Prediction accuracy of PUF response with logistic regression, support vector machine (SVM) and random forest (RF) is close to 51%. A prototype PUF fabricated in 65nm consumes only 0.3pJ/bit, and achieves the best combination of energy efficiency and resistance to ML attacks. The measured inter and intra hamming distance (HD) for the PUF are 0.5026 and 0.0466 respectively.
dc.formatapplication/pdf
dc.language.isoen
dc.publisherState University of New York at Buffalo
dc.rightsUsers of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.
dc.subjectElectrical engineering
dc.title0.3pJ/bit Machine Learning Resistant Strong PUF Using Subthreshold Voltage Divider Array
dc.typeThesis
dc.rights.holderCopyright retained by author.


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