Generation and realization of true random numbers based on physical unclonable functions

The need for random numbers and random number generators is increasing day by day. Random numbers are compiled, especially in computer science and such as computer compilers and cryptographic systems. Random numbers can be generated by using some numerical operations to an initial value, often called a kernel. We can also generate random numbers in many ways, such as using a specific algorithm, a mathematical formula, predetermined tables, or using natural physical events that do not have a deterministic character. The random number generation process can be reproducible when the same channel is used. Therefore, the output of the random number generator may not be truly random. Random number sequences are used in many fields since they consist of numbers that are statistically self-reliant of each other and have no correlation. Random number generators are also used in computer simulations, numerical analysis applications, statistical analysis, applications using the Monte Carlo method, and especially in encryption. For example, the reliability of cryptographic algorithms depends on the numbers generated by random number generators. If these numbers are statistically random, that is, if the recent cannot be determined by looking at the previous outputs, then the generator has excellent statistical properties. In other words, proper encryption requires the right Random Number Generator (RNG). It is possible to divide the RNGs into True-RNGs and Pseudo-RNGs. Depending on the purpose of the application, one of these two structures is preferred. While the actual TRNGs are based on the measurement of natural processes such as noise, the so-called TRNGs use deterministic methods such as numerical algorithms. While true RNGs are used in applications where security is essential before mentioned as encryption, the performance of so-called RNGs is sufficient to be used in computer simulations. In this thesis, the Generation and Realization of True Random Numbers Based on Physical Unclonable Functions are presented. Both PUF structures were performed in FPGA and the numbers produced were tested with the NIST test suite. Successful results were obtained from the tests. Chapter 2 of this work shows the definition of random numbers and their classification, current usage areas, and the reviews about random numbers while Chapter 3 shows PUF architecture. In chapter 4 the National Institute of Standards and Technology (NIST-Test), while in chapter 5 PUF architecture and their implementation in the FPGA environment is discussed and a short evaluation in the conclusion part is given