Unbalanced-Tests to the Improvement of Yield and Quality

An integrated-circuit testing model (DITM) is used to describe various factors that affect test yield during a test process. We used a probability distribution model to evaluate test yield and quality and introduced a threshold test and a guardband test. As a result of the development speed of the semiconductor manufacturing industry in the future being unpredictable, we use electrical properties of existing products and the current manufacturing technology to estimate future product-distribution trends. In the development of very-large-scale integration (VLSI) testing, the progress of testing technology is very slow. To improve product testing yield and quality, we change the test method and propose an unbalanced-test method, leading to improvements in test results. The calculation using our proposed model and data estimated by the product published by the IEEE International Roadmap for Devices and Systems (IRDS, 2017) proves that the proposed unbalanced-test method can greatly improve test yield and quality and achieve the goal of high-quality, near-zero-defect products.

A Theoretical Modeling Analysis of Adapted Composite CNT Bundle for High-Speed VLSI Interconnect

The aroused quest to reduce the delay at interconnect level is the main urge of this paper to come across a configuration of Carbon Nanotube (CNT) bundle namely squarely packed bundle of composite CNTs. The approach, demonstrated in this paper, adapts the composite bundle to adopt for high speed Very Large Scale Integration (VLSI) interconnect with technology sizing down. To reduce the delay of the proposed configuration of composite CNT bundle, the behavioral change of Resistance (R), Inductance (L) and Capacitance (C) has been observed with respect to both width of the bundle and diameter of the CNTs in the bundle. Consequently, the performance of the modified bundle configuration is compared with previously developed configuration namely squarely packed bundle of dimorphic MWCNTs in terms of propagation delay and crosstalk delay at local, semiglobal and global level interconnect. The proposed bundle configuration is ultimately enacted as better one for 32nmand 16nmtechnology node and suitable for 7nmas well.

Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration

Skeletal muscle repair is driven by the coordinated self-renewal and fusion of myogenic stem and progenitor cells. Single-cell gene expression analyses of myogenesis have been hampered by the poor sampling of rare and transient cell states that are critical for muscle repair, and do not inform the spatial context that is important for myogenic differentiation. Here, we demonstrate how large-scale integration of single-cell and spatial transcriptomic data can overcome these limitations. We created a single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 88 publicly available single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting dataset includes more than 365,000 cells and spans a wide range of ages, injury, and repair conditions. Together, these data enabled identification of the predominant cell types in skeletal muscle, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro-adipogenic progenitors defined by functional roles, and many distinct immune populations. The representation of different experimental conditions and the depth of transcriptome coverage enabled robust profiling of sparsely expressed genes. We built a densely sampled transcriptomic model of myogenesis, from stem cell quiescence to myofiber maturation, and identified rare, transitional states of progenitor commitment and fusion that are poorly represented in individual datasets. We performed spatial RNA sequencing of mouse muscle at three time points after injury and used the integrated dataset as a reference to achieve a high-resolution, local deconvolution of cell subtypes. We also used the integrated dataset to explore ligand-receptor co-expression patterns and identify dynamic cell-cell interactions in muscle injury response. We provide a public web tool to enable interactive exploration and visualization of the data. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

Energy efficient high-performance approximate adders for imprecision-tolerant signal processing applications

The trade-off between Delay and Power consumption has become a major concern as process technology reached less than 10 nm proximity in the modern Very Large-Scale Integration (VLSI) technology. This trade-off can be compensated with accuracy and is vanquished by the development of Approximate Computing (AC). In this paper, six diverse Approximate Adders (AAs) have been proposed based on logic complexity reduction at the transistor level. Simulation results reveal that the Proposed AAs has a significant amount of Power and Delay savings, lesser Power-Delay Product (PDP). The Proposed AAs:PA1, PA3, PA5, PA3 exhibits 12.85 %, 41.59%, 72.05 %, 1.91% lesser power than the Existing AAs EAA1, EAA5, EAA6, EAA9 respectively. The Proposed AAs: PA2, PA3 incorporates 37.5 %, 54.5%, of lesser number of transistors compared to Existing AAs: EAA5, EAA9 whereas PA4, PA5 incorporates 40 % of reduction in the number of transistors compared to Existing AAs: EAA6, EAA8. These results are promising for high performance and energy efficient systems for error-resilient applications such as multimedia and signal processing where a slightly degraded output quality is acceptable, which could lead to significant power reduction.