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Silicon Physical Unclonable Functions (PUFs) are security primitives relying on intrinsic randomness of IC manufacturing. Strong PUFs have a very large input-output space which is essential for secure authentication. Several proposed strong PUFs use timing races to produce a rich set of responses. However, these PUFs are vulnerable to machine-learning attacks due to linear separability of the output function resulting from the additive nature of timing delay along timing paths. The authors introduce a novel strong silicon PUF based on the exponential current-voltage behavior in subthreshold region of FET operation which injects strong nonlinearity into the response of the PUF.
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