Carbon nanotubes as interconnect for next generation network on chip

Multi-core processors provide better performance when compared with their single-core equivalent. Recently, Networks-on-Chip (NoC) have emerged as a communication methodology for multi core chips. Network-on-Chip uses packet based communication for establishing a communication path between multiple cores connected via interconnects. Clock frequency, energy consumption and chip size are largely determined by these interconnects. According to the International Technology Roadmap for Semiconductors (ITRS), in the next five years up to 80% of microprocessor power will be consumed by interconnects. In the sub 100nm scaling range, interconnect behavior limits the performance and correctness of VLSI systems. The performance of copper interconnects tend to get reduced in the sub 100nm range and hence we need to examine other interconnect options. Single Wall Carbon Nanotubes exhibit better performance in sub 100nm processing technology due to their very large current carrying capacity and large electron mean free paths. This work suggests using Single Wall Carbon Nanotubes (SWCNT) as interconnects for Networks-on-Chip as they consume less energy and gives more throughput and bandwidth when compared with traditional Copper wires

Carbon Nanotube Capacitors

We introduce a vertical carbon nanotube capacitor with high capacitance per unit area. Using an electrical model of single-walled, metallic carbon nanotubes and the extracted capacitance values of a carbon nanotube bundle network, we develop an electrical model for the capacitor. The device can exhibit a capacitance greater than 175fF/mu m(2)

Carbon Nanotube Interconnect Modeling for Very Large Scale Integrated Circuits

In this research, we have studied and analyzed the physical and electrical properties of carbon nanotubes. Based on the reported models for current transport behavior in non-ballistic CNT-FETs, we have built a dynamic model for non-ballistic CNT-FETs. We have also extended the surface potential model of a non-ballistic CNT-FET to a ballistic CNT-FET and developed a current transport model for ballistic CNT-FETs. We have studied the current transport in metallic carbon nanotubes. By considering the electron-electron interactions, we have modified two-dimensional fluid model for electron transport to build a semi-classical one-dimensional fluid model to describe the electron transport in carbon nanotubes, which is regarded as one-dimensional system. Besides its accuracy compared with two-dimensional fluid model and Lüttinger liquid theory, one-dimensional fluid model is simple in mathematical modeling and easier to extend for electronic transport modeling of multi-walled carbon nanotubes and single-walled carbon nanotube bundles as interconnections. Based on our reported one-dimensional fluid model, we have calculated the parameters of the transmission line model for the interconnection wires made of single-walled carbon nanotube, multi-walled carbon nanotube and single-walled carbon nanotube bundle. The parameters calculated from these models show close agreements with experiments and other proposed models. We have also implemented these models to study carbon nanotube for on-chip wire inductors and it application in design of LC voltage-controlled oscillators. By using these CNT-FET models and CNT interconnects models, we have studied the behavior of CNT based integrated circuits, such as the inverter, ring oscillator, energy recovery logic; and faults in CNT based circuits

Phase Noise in CMOS Phase-Locked Loop Circuits

Phase-locked loops (PLLs) have been widely used in mixed-signal integrated circuits. With the continuously increasing demand of market for high speed, low noise devices, PLLs are playing a more important role in communications. In this dissertation, phase noise and jitter performances are investigated in different types of PLL designs. Hot carrier and negative bias temperature instability effects are analyzed from simulations and experiments. Phase noise of a CMOS phase-locked loop as a frequency synthesizer circuit is modeled from the superposition of noises from its building blocks: voltage-controlled oscillator, frequency divider, phase-frequency detector, loop filter and auxiliary input reference clock. A linear time invariant model with additive noise sources in frequency domain is presented to analyze the phase noise. The modeled phase noise results are compared with the corresponding experimentally measured results on phase-locked loop chips fabricated in 0.5 m n-well CMOS process. With the scaling of CMOS technology and the increase of electrical field, MOS transistors have become very sensitive to hot carrier effect (HCE) and negative bias temperature instability (NBTI). These two reliability issues pose challenges to designers for designing of chips in deep submicron CMOS technologies. A new strategy of switchable CMOS phase-locked loop frequency synthesizer is proposed to increase its tuning range. The switchable PLL which integrates two phase-locked loops with different tuning frequencies are designed and fabricated in 0.5 µm CMOS process to analyze the effects under HCE and NBTI. A 3V 1.2 GHz programmable phase-locked loop frequency synthesizer is designed in 0.5 μm CMOS technology. The frequency synthesizer is implemented using LC voltage-controlled oscillator (VCO) and a low power dual-modulus prescaler. The LC VCO working range is from 900MHz to 1.4GHz. Current mode logic (CML) is used in designing high speed D flip-flop in the dual-modulus prescaler circuits for low power consumption. The power consumption of the PLL chip is under 30mW. Fully differential LC VCO is used to provide high oscillation frequency. A new design of LC VCO using carbon nanotube (CNT) wire inductor has been proposed. The PLL design using CNT-LC VCO shows significant improvement in phase noise due to high-Q LC circuit

VLSI Design

This book provides some recent advances in design nanometer VLSI chips. The selected topics try to present some open problems and challenges with important topics ranging from design tools, new post-silicon devices, GPU-based parallel computing, emerging 3D integration, and antenna design. The book consists of two parts, with chapters such as: VLSI design for multi-sensor smart systems on a chip, Three-dimensional integrated circuits design for thousand-core processors, Parallel symbolic analysis of large analog circuits on GPU platforms, Algorithms for CAD tools VLSI design, A multilevel memetic algorithm for large SAT-encoded problems, etc

Solid State Circuits Technologies

The evolution of solid-state circuit technology has a long history within a relatively short period of time. This technology has lead to the modern information society that connects us and tools, a large market, and many types of products and applications. The solid-state circuit technology continuously evolves via breakthroughs and improvements every year. This book is devoted to review and present novel approaches for some of the main issues involved in this exciting and vigorous technology. The book is composed of 22 chapters, written by authors coming from 30 different institutions located in 12 different countries throughout the Americas, Asia and Europe. Thus, reflecting the wide international contribution to the book. The broad range of subjects presented in the book offers a general overview of the main issues in modern solid-state circuit technology. Furthermore, the book offers an in depth analysis on specific subjects for specialists. We believe the book is of great scientific and educational value for many readers. I am profoundly indebted to the support provided by all of those involved in the work. First and foremost I would like to acknowledge and thank the authors who worked hard and generously agreed to share their results and knowledge. Second I would like to express my gratitude to the Intech team that invited me to edit the book and give me their full support and a fruitful experience while working together to combine this book

Fault and Defect Tolerant Computer Architectures: Reliable Computing With Unreliable Devices

This research addresses design of a reliable computer from unreliable device technologies. A system architecture is developed for a fault and defect tolerant (FDT) computer. Trade-offs between different techniques are studied and yield and hardware cost models are developed. Fault and defect tolerant designs are created for the processor and the cache memory. Simulation results for the content-addressable memory (CAM)-based cache show 90% yield with device failure probabilities of 3 x 10(-6), three orders of magnitude better than non fault tolerant caches of the same size. The entire processor achieves 70% yield with device failure probabilities exceeding 10(-6). The required hardware redundancy is approximately 15 times that of a non-fault tolerant design. While larger than current FT designs, this architecture allows the use of devices much more likely to fail than silicon CMOS. As part of model development, an improved model is derived for NAND Multiplexing. The model is the first accurate model for small and medium amounts of redundancy. Previous models are extended to account for dependence between the inputs and produce more accurate results

Imperfection-Aware Design of CNFET Digital VLSI Circuits

Carbon nanotube field-effect transistor (CNFET) is one of the promising candidates as extensions to silicon CMOS devices. The CNFET, which is a 1-D structure with a near-ballistic transport capability, can potentially offer excellent device characteristics and order-of-magnitude better energy-delay product over standard CMOS devices. Significant challenges in CNT synthesis prevent CNFETs today from achieving such ideal benefits. CNT density variation and metallic CNTs are the dominant type of CNT variations/imperfections that cause performance variation, large static power consumption, and yield degradation. We present an imperfection-aware design technique for CNFET digital VLSI circuits by: 1) Analytical models that are developed to analyze and quantify the effects of CNT density variation on device characteristics, gate and system levels delays. The analytical models, which were validated by comparison to real experimental/simulation data, enables us to examine the space of CNFET combinational, sequential and memory cells circuits to minimize delay variations. Using these model, we drive CNFET processing and circuit design guidelines to manage/overcome CNT density variation. 2) Analytical models that are developed to analyze the effects of metallic CNTs on device characteristics, gate and system levels delay and power consumption. Using our presented analytical models, which are again validated by comparison with simulation data, it is shown that the static power dissipation is a more critical issue than the delay and the dynamic power of CNFET circuits in the presence of m-CNTs. 3) CNT density variation and metallic CNTs can result in functional failure of CNFET circuits. The complete and compact model for CNFET probability of failure that consider CNT density variation and m-CNTs is presented. This analytical model is applied to analyze the logical functional failures. The presented model is extended to predict opportunities and limitations of CNFET technology at todays Gigascale integration and beyond.\u2

Cutting Edge Nanotechnology

The main purpose of this book is to describe important issues in various types of devices ranging from conventional transistors (opening chapters of the book) to molecular electronic devices whose fabrication and operation is discussed in the last few chapters of the book. As such, this book can serve as a guide for identifications of important areas of research in micro, nano and molecular electronics. We deeply acknowledge valuable contributions that each of the authors made in writing these excellent chapters

Evaluation of process-structure-property relationships of carbon nanotube forests using simulation and deep learning

This work is aimed to explore process-structure-property relationships of carbon nanotube (CNT) forests. CNTs have superior mechanical, electrical and thermal properties that make them suitable for many applications. Yet, due to lack of manufacturing control, there is a huge performance gap between promising properties of individual CNTs and CNT forest properties that hinders their adoption into potential industrial applications. In this research, computational modelling, in-situ electron microscopy for CNT synthesis, and data-driven and high-throughput deep convolutional neural networks are employed to not only accelerate implementing CNTs in various applications but also to establish a framework to make validated predictive models that can be easily extended to achieve application-tailored synthesis of any materials. A time-resolved and physics-based finite-element simulation tool is modelled in MATLAB to investigate synthesis of CNT forests, specially to study the CNT-CNT interactions and generated mechanical forces and their role in ensemble structure and properties. A companion numerical model with similar construct is then employed to examine forest mechanical properties in compression. In addition, in-situ experiments are carried out inside Environmental Scanning Electron Microscope (ESEM) to nucleate and synthesize CNTs. Findings may primarily be used to expand the forest growth and self-assembly knowledge and to validate the assumptions of simulation package. Also, SEM images can be used as feed database to construct a deep learning model to grow CNTs by design. The chemical vapor deposition parameter space of CNT synthesis is so vast that it is not possible to investigate all conceivable combinations in terms of time and costs. Hence, simulated CNT forest morphology images are used to train machine learning and learning algorithms that are able to predict CNT synthesis conditions based on desired properties. Exceptionally high prediction accuracies of R2 > 0.94 is achieved for buckling load and stiffness, as well as accuracies of > 0.91 for the classification task. This high classification accuracy promotes discovering the CNT forest synthesis-structure relationships so that their promising performance can be adopted in real world applications. We foresee this work as a meaningful step towards creating an unsupervised simulation using machine learning techniques that can seek out the desired CNT forest synthesis parameters to achieve desired property sets for diverse applications.Includes bibliographical reference