Blanket and Selective Copper CVD from Cu (Fod)2 For Multilevel Metallization

1990 ◽  
Vol 181 ◽  
Author(s):  
Alain E. Kaloyeros ◽  
Arjun N. Saxena ◽  
Kenneth Brooks ◽  
Sumanta Ghosh ◽  
Eric Eisenbraun
Keyword(s):  
Large Scale ◽  
Chemical Vapor ◽  
Precursor Chemistry ◽  
Limiting Factors ◽  
Organic Chemical ◽  
Working Pressure ◽  
X Ray ◽  
Wide Range ◽  
Large Scale Integration ◽  
Scale Integration

ABSTRACTAs the focus of integration technology inevitably shifts from the present very large scale integration (VLSI) to ultra large scale integration (ULSI) schemes, thus leading to continuous decrease in circuit dimensions, the limitations of present multilevel metallization technologies become increasingly important. Because of the appreciably higher speeds and more complex multi-functional layering involved in the newest ULSI circuits, the electrical resistance and capacitance of presently used interconnects and their electromigration and stress resistance stand as major limiting factors to signal processing throughput. In this paper, some recent results achieved by the present investigators in their studies of blanket and selective low-temperature metal-organic chemical vapor deposition (LTMOCVD) of copper for potential use in multilevel metallizations in ULSIC’s are presented. The films were produced at 300–400°C in atmospheres of pure H2 or Ar from the β-diketonate precursor bis(6, 6, 7,7, 8, 8, 8-heptafluoro-2, 2-dimethy1-3, 5-octanediono)copper(II), Cu(fod)2. The films were analyzed by x-ray diffraction (XRD), Rutherford Backscattering (RBS), Auger electron spectroscopy (AES), scanning electron microscopy (SEM), energy-dispersive x-ray spectroscopy (EDXS), and four-point resistivity probe. The results of these studies showed that films deposited on metallic substrates were uniform, continuous, adherent, highly pure, and exhibited very low resistivity, as low as 1.8 μΩcm for films deposited in pure H2 atmosphere. Preliminary investigations of selective LTMOCVD of copper showed that selectivity is indeed possible, but is a function of a wide range of parameters that include reactor geometry, substrate type and temperature, working pressure, type of carrier gas, and precursor chemistry.

Control of deposition profile of Cu for large-scale integration (LSI) interconnects by plasma chemical vapor deposition

2005 ◽  
Vol 77 (2) ◽  
pp. 391-398 ◽  
Author(s):  
Kosuke Takenaka ◽  
Masaharu Shiratani ◽  
Manabu Takeshita ◽  
Makoto Kita ◽  
Kazunori Koga ◽  
...  
Keyword(s):  
Chemical Vapor Deposition ◽  
Vapor Deposition ◽  
Large Scale ◽  
Ion Irradiation ◽  
Chemical Vapor ◽  
Ionic Species ◽  
Plasma Chemical ◽  
Plasma Chemical Vapor Deposition ◽  
Large Scale Integration ◽  
Scale Integration

H-assisted plasma chemical vapor deposition (HAPCVD) realizes control of deposition profile of Cu in trenches. The key to the control is ion irradiation to surfaces. With increasing the flux and energy of ions, the profile changes from conformal to subconformal and then to an anisotropic one, for which Cu material is filled from the bottom of the trench without deposition on the sidewall. H3+ and ArH+ are identified as the major ionic species which contribute to the control, and hence the deposition profile also depends on a ratio R = H2/(Ar + H2).

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

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
David W. McKellar ◽  
Lauren D. Walter ◽  
Leo T. Song ◽  
Madhav Mantri ◽  
Michael F. Z. Wang ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Muscle Repair ◽  
Transcriptomic Data ◽  
Mouse Skeletal Muscle ◽  
Wide Range ◽  
Large Scale Integration ◽  
Scale Integration

AbstractSkeletal 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.

Study on Electronic Industry with an Application of Rectilinear Embedding in VLSI Placement

2013 ◽  
Vol 345 ◽  
pp. 355-358
Author(s):  
Li Yan Pan ◽  
Yan Pei Liu
Keyword(s):  
Large Scale ◽  
Outerplanar Graph ◽  
Electronic Industry ◽  
Lagrangian Inversion ◽  
Multiple Parameters ◽  
Vlsi Physical Design ◽  
Wide Range ◽  
Large Scale Integration ◽  
Scale Integration ◽  
The Relationship

The electronic industry has developed quickly in last few years, with the rapid growth of Very Large Scale Integration technology. Placement layout is considered as the original step in VLSI physical design. The rectilinear embedding, which originates from graph theory, has wide range of application in VLSI placement. In this paper, we constructed a mathematical model for VLSI placement. Firstly, the VLSI placement was converted to quadrangulation by using rectilinear embedding speculative knowledge. Then we provided generating functions for two types of quadrangulations with graph multiple parameters. And the explicit formulae were obtained by employing Lagrangian inversion. Furthermore, we found the relationship between outerplanar graph and Hamilton graph, so the counting result of Hamilton quadrangulation was derived. The quadrangulation calculation can be applied to the establishment of arithmetical algorithms, which can be widely used in the optimization of VLSI placement.

Comparative Study of Low Dielectric Constant Material Deposited Using Different Precursors

2011 ◽  
Vol 233-235 ◽  
pp. 2480-2485
Author(s):  
Yi Lung Cheng ◽  
Yi Shiung Lu ◽  
Tai Jung Chiu
Keyword(s):  
Thermal Stability ◽  
Vapor Deposition ◽  
Large Scale ◽  
Dielectric Material ◽  
Chemical Vapor ◽  
Electrical Performance ◽  
Low Dielectric Constant ◽  
Low Dielectric ◽  
Large Scale Integration ◽  
Scale Integration

Two kinds of organosilicate precursors, trimethylsilane (3MS) and diethoxymethylsilane (DEMS), were used to produce low-k films by plasma-enhanced chemical vapor deposition (PECVD) in this work. The experimental results indicate that DEMS-based low-k films have superior electrical performance and better thermal stability as compared to 3MS-based low-k films. Therefore, DEMS-based films are the promising low-k materials which can be integrated in very large scale integration circuit as an inter-layer dielectric material.

scholarly journals Timing Optimization During the Physical Synthesis of Cell-Based VLSI Circuits

2017 ◽  
Author(s):  
Vinícius Dos Santos Livramento ◽  
José Luís Güntzel
Keyword(s):  
Integrated Circuits ◽  
Large Scale ◽  
Cmos Technology ◽  
Vlsi Circuits ◽  
Consumer Electronics ◽  
Clock Frequency ◽  
Wide Range ◽  
Large Scale Integration ◽  
On Chip ◽  
Scale Integration

The evolution of CMOS technology made possible integrated circuits with billions of transistors assembled into a single silicon chip, giving rise to the jargon Very-Large-Scale Integration (VLSI). VLSI circuits span a wide range class of applications, including Application Specific Circuits and Systems-On-Chip. The latter are responsible for fueling the consumer electronics market, especially in the segment of smartphones and tablets, which are responsible for pushing hardware performance requirements every new generation. The required clock frequency affects the performance of a VLSI circuit and induces timing constraints that must be properly handled by synthesis tools. This thesis focuses on techniques for timing closure of cellbased VLSI circuits, i.e. techniques able to iteratively reduce the number of timing violations until the synthesis of the synchronous digital system reaches the specified target frequency.

scholarly journals Strength in numbers: Large-scale integration of single-cell transcriptomic data reveals rare, transient muscle progenitor cell states in muscle regeneration

2020 ◽  
Author(s):  
David W. McKellar ◽  
Lauren D. Walter ◽  
Leo T. Song ◽  
Madhav Mantri ◽  
Michael F.Z. Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Skeletal Muscle ◽  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Muscle Repair ◽  
Transcriptomic Data ◽  
Wide Range ◽  
Large Scale Integration ◽  
Scale Integration

ABSTRACTSkeletal 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 provide spatial information that is needed to understand the context in which myogenic differentiation occurs. Here, we demonstrate how large-scale integration of new and public single-cell and spatial transcriptomic data can overcome these limitations. We created a large-scale single-cell transcriptomic dataset of mouse skeletal muscle by integration, consensus annotation, and analysis of 23 newly collected scRNAseq datasets and 79 public single-cell (scRNAseq) and single-nucleus (snRNAseq) RNA-sequencing datasets. The resulting compendium includes nearly 350,000 cells and spans a wide range of ages, injury, and repair conditions. Combined, these data enabled identification of the predominant cell types in skeletal muscle with robust, consensus gene expression profiles, and resolved cell subtypes, including endothelial subtypes distinguished by vessel-type of origin, fibro/adipogenic progenitors marked by stem potential, 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, short-lived 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. This analysis identified the temporal variation in the colocalization of immune cell subtype interactions with myogenic progenitors during injury recovery. We provide a public web tool to enable interactive exploration and visualization of this rich single-cell transcriptomic resource. Our work supports the utility of large-scale integration of single-cell transcriptomic data as a tool for biological discovery.

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