Off-campus UMass Amherst users: To download campus access theses, please use the following link to log into our proxy server with your UMass Amherst user name and password.

Non-UMass Amherst users: Please talk to your librarian about requesting this thesis through interlibrary loan.

Theses that have an embargo placed on them will not be available to anyone until the embargo expires.

Access Type

Open Access

Document Type


Degree Program

Electrical & Computer Engineering

Degree Type

Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)

Year Degree Awarded


Month Degree Awarded



True Random Number Generation, Process Variation, Circuit calibration


On-chip True Random Number Generator (TRNG) forms an integral part of a number of cryptographic systems in multi-core processors, communication networks and RFID. TRNG provides random keys, device id and seed for Pseudo Random Number Generators (PRNG). These circuits, harnessing physical random variations like thermal noise or stray electromagnetic waves are ideally expected to generate random bits with very high entropy and zero correlation. But, progression to advance semiconductor manufacturing processes has brought about various challenges in the design of TRNG. Increasing variations in the fabrication process and the sensitivity of transistors to operating conditions like temperature and supply voltage have significant effect on the efficiency of TRNG designed in sub-micron technologies. Poorly designed random number generators also provide an avenue for attackers to break the security of a cryptographic system. Process variation and operating conditions may be used as effective tools of attack against TRNG. This work makes a comprehensive study of the effect of process variation on metastability-based TRNG designed in deep sub-micron technology. Furthermore, the effect of operating temperature and the supply voltage on the performance of TRNG is also analyzed. To mitigate these issues we study entropy extraction mechanisms based both on algorithmic approach and circuit tuning and compare these techniques based on their tolerance to process variation and the energy overhead for correction. We combine the two v approaches to efficiently perform self-calibration, using a hybrid of algorithmic correction and circuit tuning to compensate the effect of variations. The proposed technique provides a fair trade-off between the degree of entropy extraction and the overhead in terms of area and energy, introducing minimal correlation in the output of the TRNG. Besides the study of the effect of process variation and operating conditions on the TRNG, we also propose to study the possible attack models on a TRNG. Finally, we propose a probabilistic approach to design and analysis of TRNG using a stochastic model of the circuit operation and incorporating the random source in thermal noise. All analysis is done for 45nm technology using the NCSU PDK transistor models. The simulation platform is developed using HSPICE and a Perl based automation flow.


First Advisor

Wayne P. Burleson

Included in

Engineering Commons