Graduation Year


Document Type




Degree Granting Department

Electrical Engineering

Major Professor

Sanjukta Bhanja, Ph.D.

Committee Member

Nagarajan Ranganathan, Ph.D.

Committee Member

Syed M. Alam, Ph.D.

Committee Member

Wilfrido A. Moreno, Ph.D.

Committee Member

Paris H. Wiley, Ph.D.


Reliability, Worst-case input, Sequential circuits, Redundancy models


Technology scaling to the nanometer levels has paved the way to realize multi-dimensional applications in a single product by increasing the density of the electronic devices on integrated chips. This has naturally attracted a wide variety of industries like medicine, communication, automobile, defense and even house-hold appliance, to use high speed multi-functional computing machines. Apart from the advantages of these nano-domain computing devices, their usage in safety-centric applications like implantable biomedical chips and automobile safety has immensely increased the need for comprehensive error analysis to enhance their reliability. Moreover, these nano-electronic devices have increased propensity to transient errors due to extremely small device dimensions and low switching energy. The nature of these transient errors is more probabilistic than deterministic, and so requires probabilistic models for estimation and analysis. In this dissertation, we present comprehensive analytic studies of error behavior in nano-level digital logic circuits using probabilistic reliability models. It comprises the design of exact probabilistic error models, to compute the maximum error over all possible input space in a circuit-specific manner; to study the behavior of transient errors in sequential circuits; and to achieve error mitigation through redundancy techniques. The model to compute maximum error, also provides the worst-case input vector, which has the highest probability to generate an erroneous output, for any given logic circuit. The model for sequential logic that can measure the expected output error probability, given a probabilistic input space, can account for both spatial dependencies and temporal correlations across the logic, using a time evolving causal network. For comprehensive error reduction in logic circuits, temporal, spatial and hybrid redundancy models, are implemented. The temporal redundancy model uses the triple temporal redundancy technique that applies redundancy in the input space, spatial redundancy model uses the cascaded triple modular redundancy technique that applies redundancy in the intermediate signal space and the hybrid redundancy techniques encapsulates both temporal and spatial redundancy schemes. All the above studies are performed on standard benchmark circuits from ISCAS and MCNC suites and the subsequent experimental results are obtained. These results clearly encompasses the various aspects of error behavior in nano VLSI circuits and also shows the efficiency and versatility of the probabilistic error models.