The output was random for only a jitter zone of 24 ps with a period difference of 50 ps and 100 ps.

Increasing the output rate by sampling on the rising and falling edge instead of only the rising was not effective. A variety of clever algorithms have been developed which generate sequences of numbers which pass every statistical test used to distinguish random. As the jitter zone gets larger, the proportion of the jitter zone to the difference in periods of the oscillators must increase for the output to remain random. The proportion of the size of the jitter zone compared to the period difference between the two oscillators limits the performance of this technique. The mathematical algorithms, no matter how perfect they can be, are not capable to generate combinations of random numbers. This research examines how the time of the differences in period of the two oscillators, the size of the jitter zone, and whether sampling on the rising and falling edge of the oscillator rather than just the rising edge affects the randomness of the TRNG. An oscillator sampling technique has proved to be an effective TRNG in a Xilinx FPGA. Since random bits do not have to be brought into the processes from an outside source, they cannot be compromised. A fast true random number generator on a field programmable gate array presents digital designers with the ability to have the generator on chip. As society has become more dependent on electronic technology the need for true random number generators has increased due to processes that require encryption in everyday use. Only a true random number generator is secure because the output bits are non-repeating and nonreproducible. There are true random number generators (TRNG) and pseudo random number generators (PRNG). The common entropy sources include thermal. True Random Number Generators, Genetic Algorithms, Auto-Correlation, Entropy. True random number generators (RNGs) are composed of two parts: entropy source and algorithm post-processing. Random number generators are used in many areas of engineering, computer science, most notably in simulations and cryptographic applications. We also include a TestU01 evaluation over the random numbers generated.

True random number generator algorithm