Atomistic to circuit-level modeling of doped SWCNT for on-chip interconnects

In this article, we present a hierarchical model for doped single-wall carbon nanotube (SWCNT) for on-chip interconnect application. We study the realistic CVD grown SWCNT with defects and contacts, which induce important resistance values and worsens SWCNT on-chip interconnect performance. We investigate the fundamental physical mechanism of doping in SWCNT with the purpose of improving its electrical conductivity as well as combining mitigating the effects of defects and large contact resistance. The atomistic model provides insights on statistical variations of the number of conducting channels of doped SWCNT and SWCNT resistance variation with a various number of vacancy defects configurations. Based on atomistic simulations, we develop circuit-level models to simulate SWCNT interconnects and understand the impact of doping, defects, and contacts. Simulation results show an 80% resistance reduction by doping. Additionally, we observe that doping can mitigate the effects of defects and limited impact on contact resistance

Investigation of Pt-salt-doped-standalone-multiwall carbon nanotubes for on-chip interconnect applications

In this paper, we investigate, by combining electrical measurements with an atomistic-to-circuit modeling approach, the conductance of doped standalone multiwall carbon nanotubes (CNTs) as a viable candidate for the next generation of back-end-of-line interconnects. Ab initio simulations predict a doping-related shift of the Fermi level, which reduces shell chirality variability and improves electrical resistivity up to 90% by converting semiconducting shells to metallic. Electrical measurements of Pt-salt-doped CNTs provide up to 50% of resistance reduction, which is a milestone result for future CNT interconnect technology. Moreover, we find that defects and contacts introduce additional resistance, which limits the efficiency of doping, and are the primary cause for the mismatch between theoretical predictions and experimental measurements on doped CNTs

Transcriptome-wide association study reveals novel susceptibility genes for coronary atherosclerosis

BackgroundGenetic risk factors substantially contributed to the development of coronary atherosclerosis. Genome-wide association study (GWAS) has identified many risk loci for coronary atherosclerosis, but the translation of these loci into therapeutic targets is limited for their location in non-coding regions. Here, we aimed to screen the potential coronary atherosclerosis pathogenic genes expressed though TWAS (transcriptome wide association study) and explore the underlying mechanism association.MethodsFour TWAS approaches (PrediXcan, JTI, UTMOST, and FUSION) were used to screen genes associated with coronary atherosclerosis. Enrichment analysis of TWAS-identified genes was applied through the Metascape website. The summary-data-based Mendelian randomization (SMR) analysis was conducted to provide the evidence of causal relationship between the candidate genes and coronary atherosclerosis. At last, the cell type-specific expression of the intersection genes was examined by using human coronary artery single-cell RNA-seq, interrogating the immune microenvironment of human coronary atherosclerotic plaque at different stages of maturity.ResultsWe identified 19 genes by at least three approaches and 1 gene (NBEAL1) by four approaches. Enrichment analysis enriching the genes identified at least by two TWAS approaches, suggesting that these genes were markedly enriched in asthma and leukocyte mediated immunity reaction. Further, the summary-data-based Mendelian randomization (SMR) analysis provided the evidence of causal relationship between NBEAL1 gene and coronary atherosclerosis, confirming the protecting effects of NBEAL1 gene and coronary atherosclerosis. At last, the single cell cluster analysis demonstrated that NBEAL1 gene has differential expressions in macrophages, plasma cells and endothelial cells.ConclusionOur study identified the novel genes associated with coronary atherosclerosis and suggested the potential biological function for these genes, providing insightful guidance for further biological investigation and therapeutic approaches development in atherosclerosis-related diseases

Variability study of MWCNT local interconnects considering defects and contact resistances - Part II: impact of charge transfer doping

In this paper, the impact of charge transfer doping on the variability of multiwalled carbon nanotube (MWCNT) local interconnects is studied by experiments and simulations. We calculate the number of conducting channels of both metallic and semiconducting carbon nanotubes as a function of Fermi level shift due to doping based on the calculation of transmission coefficients. By using the MWCNT compact model proposed in Part I of this paper, we study the charge transfer doping of MWCNTs employing Fermi level shift to reduce the performance variability due to changes in diameter, chirality, defects, and contact resistance. Simulation results show that charge transfer doping can significantly improve MWCNT interconnect performance and variability by increasing the number of conducting channels of shells and degenerating semiconducting shells to metallic shells. As a case study on an MWCNT of 11 nm outer diameter, when the Fermi level shifts to 0.1 eV, up to ~80% of performance and standard deviation improvements are observed. Furthermore, a good match between experimental data and simulation results is observed, demonstrating the effectiveness of doping, the validity of the MWCNT compact model and proposed simulation methodology

A manually annotated Actinidia chinensis var. chinensis (kiwifruit) genome highlights the challenges associated with draft genomes and gene prediction in plants

Most published genome sequences are drafts, and most are dominated by computational gene prediction. Draft genomes typically incorporate considerable sequence data that are not assigned to chromosomes, and predicted genes without quality confidence measures. The current Actinidia chinensis (kiwifruit) 'Hongyang' draft genome has 164\ua0Mb of sequences unassigned to pseudo-chromosomes, and omissions have been identified in the gene models

Online Cooperative Teaching Mode Based on Self-Direction Theory in Method of Sport Science Research

As an important indicator for measuring professional talents, the capacity for scientific research has received more and more attention. The course of Method of Sport Science Research can systematically teach students to learn the theory of scientific research, grasp professional method of scientific research and lay a foundation for cultivating scientific thought and scientific research ability of students of sports specialty. Based on the content characteristics and knowledge foundation of the course, students have not enjoyed this course very much and it has been difficult for them to understand. Besides, it is difficult to achieve good teaching effect because of poor mastery and application of knowledge after learning. On this basis, “12345” online learning mode based on self-direction theory was designed in this study. This process includes 1 thought, 2 mentalities, 3 dimensions, 4 levels and 5 steps. This theory was applied in the smart classroom of Method of Sport Science Research. The online cooperative teaching combined with network interaction technology and integrated with visual exchange technology was conducted. Besides, data analysis advantage of network platform was adopted to record the data of teacher-student interactions. Then, students’ learning was systematically understood through the interaction analysis. Finally, fuzzy C mean was integrated in the above teaching mode to conduct blended group research, implement targeted individual teaching of for Method of Sport Science Research and achieve the target interaction with students according to accuracy of big data analysis. The experimental results show that the teaching mode can fully mobilize students’ enthusiasm, stimulate their learning initiative and enhance learning efficiency

Recent Progress and Challenges Regarding Carbon Nanotube On-Chip Interconnects

Along with deep scaling transistors and complex electronics information exchange networks, very-large-scale-integrated (VLSI) circuits require high performance and ultra-low power consumption. In order to meet the demand of data-abundant workloads and their energy efficiency, improving only the transistor performance would not be sufficient. Super high-speed microprocessors are useless if the capacity of the data lines is not increased accordingly. Meanwhile, traditional on-chip copper interconnects reach their physical limitation of resistivity and reliability and may no longer be able to keep pace with a processor’s data throughput. As one of the potential alternatives, carbon nanotubes (CNTs) have attracted important attention to become the future emerging on-chip interconnects with possible explorations of new development directions. In this paper, we focus on the electrical, thermal, and process compatibility issues of current on-chip interconnects. We review the advantages, recent developments, and dilemmas of CNT-based interconnects from the perspective of different interconnect lengths and through-silicon-via (TSV) applications

Nested Named Entity Recognition Based on Dual Stream Feature Complementation

Named entity recognition is a basic task in natural language processing, and there is a large number of nested structures in named entities. Nested named entities become the basis for solving many tasks in NLP. A nested named entity recognition model based on dual-flow features complementary is proposed for obtaining efficient feature information after text coding. Firstly, sentences are embedded at both the word level and the character level of the words, then sentence context information is obtained separately via the neural network Bi-LSTM; Afterward, two vectors perform low-level feature complementary to reinforce low-level semantic information; Sentence-local information is captured with the multi-head attention mechanism, then the feature vector is sent to the high-level feature complementary module to obtain deep semantic information; Finally, the entity word recognition module and the fine-grained division module are entered to obtain the internal entity. The experimental results show that the model has a great improvement in feature extraction compared to the classical model