De-Correlation and De-Bias Post-Processing Circuits for True Random Number Generator

True random number generators (TRNGs) are commonly used in hardware security for secure authentication, data encryption, etc. The raw random numbers often exhibit defects. The most commonly observed defects are bias and correlations. Post processing techniques have been developed to address them. The von Neumann method addresses bias, but it requires input that is uncorrelated and has an identical distribution. On the other hand, the Markov chain can address correlation but introduce bias. In this work, we research the lightweight combination of two techniques. We verified that MKV2(QL4)/VN2 performs well for both Markov and non-Markov model bitstreams. MKV1(QL8)/VN8W is effective for the Markov model. The randomness is verified by NIST SP 800-22 and 800-90B, and ENT, respectively. Both of these circuits require only 16 bits of memory, which is 12 times smaller than in previous work. MKV1(QL8)/VN8W is implemented using 65-nm CMOS. A prototype chip demonstrates a minimum energy consumption of 0.149 pJ/bit at 0.45V. When applied to a latch-based TRNG, it can double the operation frequency thanks to the enhanced decorrelation. The total energy consumption is reduced by 21%.