In this paper, we present artificial neural network (ANN) models to predict hard and soft-responses of three configurations of arbiter based physical unclonable functions (PUFs): standard, feed-forward (FF) and modified feed-forward (MFF). The models are trained using data extracted from 32-stage arbiter PUF circuits fabricated using IBM 32 nm HKMG process. The contributions of this paper are two-fold. First, we evaluate the unpredictability of the PUFs by predicting hard responses using ANNs and comparing these with ground truth. Second, ANNs are trained to predict soft-responses and a probability based thresholding scheme is used to define stability. The obtained soft-response models are used to identify unstable responses.
|Original language||English (US)|
|Title of host publication||2017 IEEE 60th International Midwest Symposium on Circuits and Systems, MWSCAS 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|State||Published - Sep 27 2017|
|Event||60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017 - Boston, United States|
Duration: Aug 6 2017 → Aug 9 2017
|Name||Midwest Symposium on Circuits and Systems|
|Other||60th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2017|
|Period||8/6/17 → 8/9/17|
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.
© 2017 IEEE.