Algorithms and Methodology for Post-Manufacture Adaptation to Process Variations and Induced Noise in Deeply Scaled CMOS Technologies
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In the last two decades, VLSI technology scaling has spurred a rapid growth in the semiconductor industry. With CMOS device dimensions falling below 100 nm, achieving higher performance and packing more complex functionalities into digital integrated circuits have become easier. However, the scaling trend poses new challenges to design and process engineers. First, larger process parameter variations in the current technologies cause larger spread in the delay and power distribution of circuits and result in the parametric yield loss. In addition, ensuring the reliability of deep sub-micron (DSM) technologies under soft/transient errors is a significant challenge. These errors occur because of the combined effects of the atmospheric radiations and the significantly reduced noise margins of scaled technologies. This thesis focuses on addressing the issues related to the process variations and reliability in deeply scaled CMOS technologies. The objective of this research has been to develop circuit-level techniques to address process variations, transient errors, and the reliability concern. The proposed techniques can be divided into two parts. The first part addresses the process variation concern and proposes techniques to reduce the variation effects on power and performance distribution. The second part deals with the transient errors and techniques to reduce the effect of transient errors with minimum hardware or computational overhead.