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Physical unclonable functions (PUFs) are circuits that produce outputs determined by random physical variations from fabrication. The PUF studied in this paper utilizes the variation sensitivity of static random access memory (SRAM) data retention voltage (DRV), the minimum voltage at which each cell can retain state. Prior work shows that DRV can uniquely identify circuit instances with 28% greater success than SRAM power-up states that are used in PUFs [1]. However, DRV is highly sensitive to temperature, and until now this makes it unreliable and unsuitable for use in a PUF. In this paper, we enable DRV PUFs by proposing a DRV-based hash function that is insensitive to temperature. The new hash function, denoted DRV-based hashing (DH), is reliable across temperatures because it utilizes the temperature-insensitive ordering of DRVs across cells, instead of using the DRVs in absolute terms. To evaluate the security and performance of the DRV PUF, we use DRV measurements from commercially available SRAM chips, and use data from a novel DRV prediction algorithm. The prediction algorithm uses machine learning for fast and accurate simulation-free estimation of any cell’s DRV, and the prediction error in comparison to circuit simulation has a standard deviation of 0.35 mV. We demonstrate the DRV PUF using two applications—secret key generation and identification. In secret key generation, we introduce a new circuit-level reliability knob as an alternative to error correcting codes. In the identification application, our approach is compared to prior work and shown to result in a smaller false-positive identification rate for any desired true-positive identification rate. [ABSTRACT FROM PUBLISHER]