Estimating the expected latency to failure due to manufacturing defects

Manufacturers of digital circuits test their products to find defective parts so they are not sold to customers. Despite extensive testing, some of their products that are defective pass the testing process. To combat this problem, manufacturers have developed a metric called defective part level. This metric measures the percentage of parts that passed the testing that are actually defective. While this is useful for the manufacturer, the customer would like to know how long it will take for a manufacturing defect to affect circuit operation. In order for a defect to be detected during circuit operation, it must be excited and observed at the same time. This research shows the correlation between defect detection during automatic test pattern generation (ATPG) testing and normal operation for both combinational and sequential circuits. This information is then used to formulate a mathematical model to predict the expected latency to failure due to manufacturing defects

Modeling defective part level due to static and dynamic defects based upon site observation and excitation balance

Manufacture testing of digital integrated circuits is essential for high quality. However, exhaustive testing is impractical, and only a small subset of all possible test patterns (or test pattern pairs) may be applied. Thus, it is crucial to choose a subset that detects a high percentage of the defective parts and produces a low defective part level. Historically, test pattern generation has often been seen as a deterministic endeavor. Test sets are generated to deterministically ensure that a large percentage of the targeted faults are detected. However, many real defects do not behave like these faults, and a test set that detects them all may still miss many defects. Unfortunately, modeling all possible defects as faults is impractical. Thus, it is important to fortuitously detect unmodeled defects using high quality test sets. To maximize fortuitous detection, we do not assume a high correlation between faults and actual defects. Instead, we look at the common requirements for all defect detection. We deterministically maximize the observations of the leastobserved sites while randomly exciting the defects that may be present. The resulting decrease in defective part level is estimated using the MPGD model. This dissertation describes the MPGD defective part level model and shows how it can be used to predict defective part levels resulting from static defect detection. Unlike many other predictors, its predictions are a function of site observations, not fault coverage, and thus it is generally more accurate at high fault coverages. Furthermore, its components model the physical realities of site observation and defect excitation, and thus it can be used to give insight into better test generation strategies. Next, we investigate the effect of additional constraints on the fortuitous detection of defects-specifically, as we focus on detecting dynamic defects instead of static ones. We show that the quality of the randomness of excitation becomes increasingly important as defect complexity increases. We introduce a new metric, called excitation balance, to estimate the quality of the excitation, and we show how excitation balance relates to the constant τ in the MPGD model

Quadratic backward propagation of variance for nonlinear statistical circuit modeling

Accurate statistical modeling and simulation are keys to ensure that integrated circuits (ICs) meet specifications over the stochastic variations inherent in IC manufacturing technologies. Backward propagation of variance (BPV) is a general technique for statistical modeling of semiconductor devices. However, the BPV approach assumes that statistical fluctuations are not large, so that variations in device electrical performances can be modeled as linear functions of process parameters. With technology scaling, device performance variability over manufacturing variations becomes nonlinear. In this paper we extend the BPV technique to take into account these nonlinearities. We present the theory behind the technique, and apply it to specific examples. We also investigate the effectiveness of several possible solution algorithms

Investigating aprataxin function: roles in DNA single strand break repair and functional cellular effects

Aprataxin protects nuclear and mitochondrial DNA against genotoxic stress, and loss-of-function mutations in the APTX gene cause the autosomal recessive cerebellar ataxia, Ataxia Oculomotor Apraxia 1 (AOA1) in humans. In an effort to extend current understanding of aprataxin function, this thesis examines the roles of aprataxin, especially in response to oxidative damage. Firstly, involvement of aprataxin during the gap-filling as well as the end-processing steps of single strand break repair were demonstrated using an in vitro single strand break repair assay using synthetic DNA substrates, cell-free lysates and/or recombinant proteins. Next, loss-of-function studies were conducted in Aptx-/- mouse embryonic fibroblasts (MEFs) and tissues from adult mice harbouring a toxic gain-of-function mutant form of superoxide dismutase1 (SOD1G93A). Expression of the mutant SOD1G93A enhanced sensitivity to oxidative damage in aprataxin-deleted cells and revealed an accelerated senescence and attenuated somatic growth phenotype. Together these findings suggest that aprataxin function is involved in optimal repair of single strand breaks and is therefore critical in maintaining cell function in situations of elevated oxidative stress

Genetic obesity:Disorders and diagnostics

Obesity is a common disease with serious consequences for the health and well-being of patients. In a small proportion of people with excessive weight, a change in genetic material is the main cause of the obesity. In this thesis, the results of genetic testing for these rare obesity disorders are described. Because of the high prevalence of obesity, it is currently impossible to perform genetic diagnostics in all people with obesity. An improved insight in the clinical phenotype of patients with a genetic obesity disorder is therefore needed to determine which patients should undergo genetic testing. Moreover, the impact of diagnosing these disorders is described in this thesis. Increased knowledge about the underlying mechanisms offers great opportunities for the development of novel drug therapy for obesity

The role of thymus dependent and bone marrow derived lymphocytes in acquired tolerance

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The Genetics of Primary Immunodeficiency in Children

Studies of children with recurrent infection demonstrate that primary immunodeficiency (PID) has a significant genetic component. In PID, over 300 genes of high penetrance inherited mostly in autosomal recessive manner have already been identified. However, many children, including those with early onset immunodeficiency have not received a genetic diagnosis, despite use of targeted sequencing methods. I performed bioinformatic analysis in whole genome sequencing data in patients with immunodeficiency. These analyses were initially in a small cohort of affected children at Great Ormond Street in whom a genetic diagnosis was not known. I devised and utilised bioinformatic programs to identify novel genetic variants in this cohort. I evaluated the performance of whole genome sequence analyses with targeted gene panel analyses, which is the most utilised method of genetic diagnosis. To expand my analysis, I looked at a larger cohort of young people and adults with immunodeficiency as part of the large national collaborative project NIHR Bioresource Rare Diseases BRIDGE-PID project. I quantified the burden of rare coding variation in a case cohort compared to controls and used rare variant association analysis to identify potential novel candidate genes in primary immunodeficiency. The final chapter focuses on 2 novel genetic variants found in the cohort and our initial functional testing to verify genetic diagnosis. The work presented in this thesis demonstrates novel genetic causes of immunodeficiency and their functional implications. The results of my work have improved understanding of the genetic architecture of primary immunodeficiencies and has clinical utility in the diagnosis and subsequent treatment of immunodeficiency

ICR ANNUAL REPORT 2020 (Volume 27)[All Pages]

This Annual Report covers from 1 January to 31 December 202