As we move deep into nanometer regime of CMOS VLSI (45nm node and below), the device noise margin gets sharply eroded because of continuous lowering of device threshold voltage together with ever increasing rate of signal transitions driven by the consistent demand for higher performance. Sharp erosion of device noise margin vastly increases the likelihood of intermittent failures (also known as parametric failures) during device operation as opposed to permanent failures caused by physical defects introduced during manufacturing process. The major sources of intermittent failures are capacitive crosstalk between neighbor interconnects, abnormal drop in power supply voltage (also known as droop), localized thermal gradient, and soft errors caused by impact of high energy particles on semiconductor surface. In nanometer technology, these intermittent failures largely outnumber the permanent failures caused by physical defects. Therefore, it is of paramount importance to come up with efficient test generation and test application methods to accurately detect and characterize these classes of failures. Soft error rate (SER) is an important design metric used in semiconductor industry and represented by number of such errors encountered per Billion hours of device operation, known as Failure-In-Time (FIT) rate. Soft errors are rare events. Traditional techniques for SER characterization involve testing multiple devices in parallel, or testing the device while keeping it in a high energy neutron bombardment chamber to artificially accelerate the occurrence of single events. Motivated by the fact that measurement of SER incurs high time and cost overhead, in this thesis, we propose a two step approach: hii a new filtering technique based on amplitude of the noise pulse, which significantly reduces the set of soft error susceptible nodes to be considered for a given design; followed by hiii an Integer Linear Program (ILP)-based pattern generation technique that accelerates the SER characterization process by 1-2 orders of magnitude compared to the current state-of-the-art. During test application, it is important to distinguish between an intermittent failure and a permanent failure. Motivated by the fact that most of the intermittent failures are temporally sparse in nature, we present a novel design-for-testability (DFT) architecture which facilitates application of the same test vector twice in a row. The underlying assumption here is that a soft fail will not manifest its effect in two consecutive test cycles whereas the error caused by a physical defect will produce an identically corrupt output signature in both test cycles. Therefore, comparing the output signature for two consecutive applications of the same test vector will accurately distinguish between a soft fail and a hard fail. We show application of this DFT technique in measuring soft error rate as well as other circuit marginality related parametric failures, such as thermal hot-spot induced delay failures. A major contribution of this thesis lies on investigating the effect of multiple sources of noise acting together in exacerbating the noise effect even further. The existing literature on signal integrity verification and test falls short of taking the combined noise effects into account. We particularly focus on capacitive crosstalk on long signal nets. A typical long net is capacitively coupled with multiple aggressors and also tend to have multiple fanout gates. Gate leakage current that originates in fanout receivers, flows backward and terminates in the driver causing a shift in driver output voltage. This effect becomes more prominent as gate oxide is scaled more aggressively. In this thesis, we first present a dynamic simulation-based study to establish the significance of the problem, followed by proposing an automatic test pattern generation (ATPG) solution which uses 0-1 Integer Linear Program (ILP) to maximize the cumulative voltage noise at a given victim net due to crosstalk and gate leakage loading in conjunction with propagating the fault effect to an observation point. Pattern pairs generated by this technique are useful for both manufacturing test application as well as signal integrity verification for nanometer designs. This research opens up a new direction for studying nanometer noise effects and motivates us to extend the study to other noise sources in tandem including voltage drop and temperature effects.