Graduation Year

2006

Document Type

Dissertation

Degree

Ph.D.

Degree Granting Department

Electrical Engineering

Major Professor

Sanjukta Bhanja, Ph.D.

Keywords

Single-event-upsets, Soft errors, Dynamic errors, Error modeling, Bayesian networks

Abstract

Reliability is one of the most serious issues confronted by microelectronics industry as feature sizes scale down from deep submicron to sub-100-nanometer and nanometer regime. Due to processing defects and increased noise effects, it is almost impractical to come up with error-free circuits. As we move beyond 22nm, devices will be operating very close to their thermal limit making the gates error-prone and every gate will have a finite propensity of providing erroneous outputs. Additional factors increasing the erroneous behaviors are low operating voltages and extremely high frequencies. These types of errors are not captured by current defect and fault tolerant mechanisms as they might not be present during the testing and reconfiguration. Hence Reliability-centric CAD analysis tool is becoming more essential not only to combat defect and hard faults but also errors that are transient and probabilistic in nature.In this dissertation, we address three broad categories of

errors. First, we focus on random pattern testability of logic circuits with respect to hard or permanent faults. Second, we model the effect of single-event-upset (SEU) at an internal node to primary outputs. We capture the temporal nature of SEUs by adding timing information to our model. Finally, we model the dynamic error in nano-domain computing, where reliable computation has to be achieved with "systemic" unreliable devices, thus making the entire computation process probabilistic rather than deterministic in nature.Our central theoretical scheme relies on Bayesian Belief networks that are compact efficient models representing joint probability distribution in a minimal graphical structure that not only uses conditional independencies to model the underlying probabilistic dependence but also uses them for computational advantage. We used both exact and approximate inference which has let us achieve order of magnitude improvements in both accuracy and speed and have enabled us t

o study larger benchmarks than the state-of-the-art. We are also able to study error sensitivities, explore design space, and characterize the input space with respect to errors and finally, evaluate the effect of redundancy schemes.

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