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Tools and techniques for evaluating reliability of defect-tolerant nano architectures

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2 Author(s)
D. Bhaduri ; Bradley Dept. of Electr. & Comput. Eng., Virginia Tech., Blacksburg, VA, USA ; S. Shukla

Nano-computing in the form of quantum, molecular and other computing models is proliferating as we scale down to nano-meter fabrication technologies. However, it is expected that nano-scale devices and interconnections will introduce unprecedented level of defects in the substrates and architectural designs need to accommodate the uncertainty inherent at such scales. This consideration motivates the search for new architectural paradigms based on redundancy based defect-tolerant designs. However, redundancy is not always a solution to the reliability problem, and often too much or too little redundancy may cause lack of reliability. The key challenge is in determining the granularity at which defect tolerance is designed, and the level of redundancy to achieve optimal reliability. Also, redundancy has been applied at different levels of granularity, such as gate level, logic block level, logic function level, unit level etc. Analytical probabilistic models to evaluate these levels are error prone and cumbersome, and do not scale well for complex network of gates. We develop different tools and techniques that can evaluate the reliability measures of combinational logic blocks, and can be used to analyze trade-offs between reliability and redundancy for different architectural configurations. In particular, we report two tools, one of which is a Matlab based tool called Nanolab and the other is a probabilistic model checking based tool named Nanoprism. We also illustrate the effectiveness of our reliability analysis tools by pointing out certain anomalies which are counter-intuitive but can be easily discovered by these tools, thereby providing better insight into defect-tolerant design decisions. We foresee that these tools will help furthering research and pedagogical interests in this area, expedite the reliability analysis process and enhance the accuracy of establishing reliability-redundancy trade-off points.

Published in:

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on  (Volume:4 )

Date of Conference:

25-29 July 2004