I. Introduction
Full-entropy True Random Number Generators (TRNG) serve as the bedrock of secure platforms, providing high-entropy keys for data encryption, secure wireless communications and media content protection [1]. While a variety of non-deterministic physical phenomena like phase jitter [2], telegraph noise [3], and oxide soft-breakdown [4] are excellent sources of randomness, the technology-invariance and frequency-independence of thermal noise have made it a popular choice in recent TRNG implementations [5]–[9]. Although raw bitstreams produced by these sources display high levels of entropy and may pass NIST randomness tests, they may still contain nonuniformities and serial-correlations due to random process variations, intermittent high-frequency supply/coupling noise and feedback control loop artifacts that render them sub-optimal for direct use as full-entropy cryptographic key generators in high-volume manufacturing environments. Conventional TRNGs overcome such nonidealities by using keyed functions like HMAC, CMAC or CBC-MAC to post-process the output of a single nonuniform entropy source [10]. While block-ciphers like AES-128 in CBC-MAC mode are ideal extractors well suited for desktop and server applications, the large area (>30K gates) and high energy consumption render them unsuitable for use in ultra-low energy internet-of-things (IoT) and wearable platforms [11]. These battery-constrained systems require energy-efficient low-area solutions for generating cryptographic-quality keys.