This paper presents a novel approach to estimate delay differences of each stage in a standard MUX-based physical unclonable function (PUF). Test data collected from PUFs fabricated using 32 nm process are used to train a linear model. The delay differences of the stages directly correspond to the model parameters. These parameters are trained by using a least mean square (LMS) adaptive algorithm. The accuracy of the response using the proposed model is around 97.5% and 99.5% for two different PUFs. Second, the PUF is also modeled by a perceptron. The perceptron has almost 100% classification accuracy. A comparison shows that the perceptron model parameters are scaled versions of the model derived by the LMS algorithm. Thus, the delay differences can be estimated from the perceptron model where the scaling factor is computed by comparing the models of the LMS algorithm and the perceptron. Because the delay differences are challenge independent, these parameters can be stored on the server. This will enable the server to issue random challenges whose responses need not be stored. An analysis of the proposed model confirms that the delay differences of all stages of the PUFs on the same chip belong to the same Gaussian probability density function.
|Original language||English (US)|
|Title of host publication||Proceedings of the 2016 Design, Automation and Test in Europe Conference and Exhibition, DATE 2016|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|State||Published - Apr 25 2016|
|Event||19th Design, Automation and Test in Europe Conference and Exhibition, DATE 2016 - Dresden, Germany|
Duration: Mar 14 2016 → Mar 18 2016
|Name||Proceedings of the 2016 Design, Automation and Test in Europe Conference and Exhibition, DATE 2016|
|Other||19th Design, Automation and Test in Europe Conference and Exhibition, DATE 2016|
|Period||3/14/16 → 3/18/16|
Bibliographical noteFunding Information:
This research has been supported by the National Science Foundation under grant number CNS-1441639 and the semiconductor research corporation under contract number 2014-TS-2560.