Interconnect roles for emerging memory technologies in 3D architecture

Compositional Tuning of Negative Differential Resistance in Bulk Silver Iodide Memristor

New Journal of Chemistry ◽

10.1039/d0nj05427e ◽

2020 ◽

Author(s):

SMITA GAJANAN NAIK ◽

Mohammad Hussain Kasim Rabinal

Keyword(s):

Power Consumption ◽

Negative Differential Resistance ◽

Silver Iodide ◽

Differential Resistance ◽

Fast Response ◽

Low Power Consumption ◽

Switching Effect ◽

Memory Switching ◽

Bulk Silver ◽

Emerging Memory

Electrical memory switching effect has received a great interest to develop emerging memory technology such as memristors. The high density, fast response, multi-bit storage and low power consumption are their...

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Identifying the Root Cause of Source-Drain Leakage Caused Soft Fail in Advanced Bulk FinFET Devices

10.31399/asm.cp.istfa2018p0383 ◽

2018 ◽

Author(s):

Liangshan Chen ◽

Yuting Wei ◽

Tanya Schaeffer ◽

Chongkhiam Oh

Keyword(s):

Careful Analysis ◽

Physical Analysis ◽

Device Structure ◽

Tem Analysis ◽

Root Cause ◽

3D Architecture ◽

Cause Of Failure ◽

Electrical Analysis ◽

Electrical Data

Abstract The paper reports the investigation on the root cause of source-drain leakage in bulk FinFET devices. While the failing device was readily isolated by nanoprobing technique and the electrical analysis pinpointed the potential defect location inside the Fin channel, the identification of physical root cause went through extreme challenges imposed by the tiny-sized device and the unique FinFET 3D architecture. The initial TEM analysis was misled by the projection of a species in the lamella surface and thus could not explain the electrical data. Careful analysis on the device structure was able to identify the origin of the species and led to the discovery of the actual root cause. This paper will provide the analysis details leading to the findings, and highlight the role of electrical understanding in not only providing guidance for physical analysis but also revealing the true root cause of failure in FinFET devices.

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Parametric Surface Modelling for Tea Leaf Point Cloud Based on Non-Uniform Rational Basis Spline Technique

Sensors ◽

10.3390/s21041304 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1304

Author(s):

Wenchao Wu ◽

Yongguang Hu ◽

Yongzong Lu

Keyword(s):

Point Cloud ◽

Principal Component ◽

Point Clouds ◽

Parametric Surface ◽

B Spline ◽

Surface Modelling ◽

Modelling Method ◽

3D Architecture ◽

Tea Leaf ◽

Fitting Error

Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1 mm, MV < 2 mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud.

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Expanding 3D Nanoprinting Performance by Blurring the Electron Beam

Micromachines ◽

10.3390/mi12020115 ◽

2021 ◽

Vol 12 (2) ◽

pp. 115

Author(s):

Lukas Seewald ◽

Robert Winkler ◽

Gerald Kothleitner ◽

Harald Plank

Keyword(s):

Electron Beam ◽

Direct Write ◽

Design Flexibility ◽

Individual Branch ◽

Inclination Angles ◽

3D Architecture ◽

20 Nm ◽

Surface Morphologies ◽

Nm Range ◽

Mechanical Rigidity

Additive, direct-write manufacturing via a focused electron beam has evolved into a reliable 3D nanoprinting technology in recent years. Aside from low demands on substrate materials and surface morphologies, this technology allows the fabrication of freestanding, 3D architectures with feature sizes down to the sub-20 nm range. While indispensably needed for some concepts (e.g., 3D nano-plasmonics), the final applications can also be limited due to low mechanical rigidity, and thermal- or electric conductivities. To optimize these properties, without changing the overall 3D architecture, a controlled method for tuning individual branch diameters is desirable. Following this motivation, here, we introduce on-purpose beam blurring for controlled upward scaling and study the behavior at different inclination angles. The study reveals a massive boost in growth efficiencies up to a factor of five and the strong delay of unwanted proximal growth. In doing so, this work expands the design flexibility of this technology.

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Mud volcanism by repeated roof collapse: 3D architecture and evolution of a mud volcano cluster offshore Nigeria

Marine and Petroleum Geology ◽

10.1016/j.marpetgeo.2019.07.033 ◽

2019 ◽

Vol 110 ◽

pp. 368-387 ◽

Cited By ~ 3

Author(s):

Matthieu Dupuis ◽

Patrice Imbert ◽

Francis Odonne ◽

Bruno Vendeville

Keyword(s):

Mud Volcano ◽

Roof Collapse ◽

Mud Volcanism ◽

3D Architecture

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Structure-preserving retargeting of irregular 3D architecture

Proceedings of the 2011 SIGGRAPH Asia Conference on - SA '11 ◽

10.1145/2024156.2024217 ◽

2011 ◽

Author(s):

Jinjie Lin ◽

Daniel Cohen-Or ◽

Hao Zhang ◽

Cheng Liang ◽

Andrei Sharf ◽

...

Keyword(s):

Structure Preserving ◽

3D Architecture

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Towards Neuromorphic Learning Machines Using Emerging Memory Devices with Brain-Like Energy Efficiency

Journal of Low Power Electronics and Applications ◽

10.3390/jlpea8040034 ◽

2018 ◽

Vol 8 (4) ◽

pp. 34 ◽

Cited By ~ 10

Author(s):

Vishal Saxena ◽

Xinyu Wu ◽

Ira Srivastava ◽

Kehan Zhu

Keyword(s):

Machine Learning ◽

Energy Efficiency ◽

Deep Learning ◽

Large Scale ◽

Learning Algorithms ◽

Memory Devices ◽

Cognitive Computing ◽

Mixed Signal ◽

Von Neumann ◽

Emerging Memory

The ongoing revolution in Deep Learning is redefining the nature of computing that is driven by the increasing amount of pattern classification and cognitive tasks. Specialized digital hardware for deep learning still holds its predominance due to the flexibility offered by the software implementation and maturity of algorithms. However, it is being increasingly desired that cognitive computing occurs at the edge, i.e., on hand-held devices that are energy constrained, which is energy prohibitive when employing digital von Neumann architectures. Recent explorations in digital neuromorphic hardware have shown promise, but offer low neurosynaptic density needed for scaling to applications such as intelligent cognitive assistants (ICA). Large-scale integration of nanoscale emerging memory devices with Complementary Metal Oxide Semiconductor (CMOS) mixed-signal integrated circuits can herald a new generation of Neuromorphic computers that will transcend the von Neumann bottleneck for cognitive computing tasks. Such hybrid Neuromorphic System-on-a-chip (NeuSoC) architectures promise machine learning capability at chip-scale form factor, and several orders of magnitude improvement in energy efficiency. Practical demonstration of such architectures has been limited as performance of emerging memory devices falls short of the expected behavior from the idealized memristor-based analog synapses, or weights, and novel machine learning algorithms are needed to take advantage of the device behavior. In this article, we review the challenges involved and present a pathway to realize large-scale mixed-signal NeuSoCs, from device arrays and circuits to spike-based deep learning algorithms with ‘brain-like’ energy-efficiency.

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Integrated Carbon/Red Phosphorus/Graphene Aerogel 3D Architecture via Advanced Vapor-Redistribution for High-Energy Sodium-Ion Batteries

Advanced Energy Materials ◽

10.1002/aenm.201601037 ◽

2016 ◽

Vol 6 (21) ◽

pp. 1601037 ◽

Cited By ~ 150

Author(s):

Hong Gao ◽

Tengfei Zhou ◽

Yang Zheng ◽

Yuqing Liu ◽

Jun Chen ◽

...

Keyword(s):

High Energy ◽

Sodium Ion ◽

Sodium Ion Batteries ◽

Red Phosphorus ◽

Graphene Aerogel ◽

3D Architecture

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Bioprinted Nanoparticles for Tissue Engineering Applications

ASME 2009 Summer Bioengineering Conference, Parts A and B ◽

10.1115/sbc2009-206760 ◽

2009 ◽

Author(s):

Kivilcim Buyukhatipoglu ◽

Robert Chang ◽

Wei Sun ◽

Alisa Morss Clyne

Keyword(s):

Tissue Engineering ◽

Tissue Growth ◽

Bioactive Components ◽

Engineering Applications ◽

Tissue Properties ◽

Native Tissue ◽

3D Architecture

Tissue engineering may require precise patterning of cells and bioactive components to recreate the complex, 3D architecture of native tissue. However, it is difficult to image and track cells and bioactive factors once they are incorporated into the tissue engineered construct. These bioactive factors and cells may also need to be moved during tissue growth in vitro or after implantation in vivo to achieve the desired tissue properties, or they may need to be removed entirely prior to implantation for biosafety concerns.

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