Opportunities for radio frequency nanoelectronic integrated circuits using carbon-based technologies

This thesis presents a body of work on the modeling of and performance predictions for carbon nanotube field-effect transistors (CNFET) and graphene field-effect transistors (GFET). While conventional silicon-based CMOS is expected to reach its ultimate scaling limits during the next decade, these two novel technologies are promising candidates for future high-performance electronics. The main goal of this work is to investigate on the opportunities of using such carbon-based electronics for RF integrated circuits. This thesis addresses 1) the modeling of noise and process variability in CNFETs, 2) RF performance predictions for CNFETs, and 3) an accurate GFET compact model. This work proposes the first CNFET noise compact model. Noise is of primary importance for RF applications and its description significantly increases the insight gained from simulation studies. Furthermore, a CNFET variability model is presented, which handles tube synthesis and metal tube removal imperfections. These two model extensions have been added to the Stanford CNFET compact model and allow for the variability-aware RF performance assessment of the CNFET technology. In continuation, comprehensive RF performance projections for CNFETs are provided both on the device and circuit level. The overall set of ITRS RF-CMOS technology requirement FoMs is determined and shows that the CNFET performs excellently in terms of speed, gain, and minimum noise figure. Furthermore, for the first time FoMs are reported for the basic RF building blocks low-noise amplifier and oscillator. In addition, it is shown that CNFET downscaling yields significant performance improvements. Based on these analyses it is confirmed that the CNFET has the potential to outperform Si-CMOS in RF applications. A third key contribution of this thesis is the development of an accurate GFET compact model. Previous compact models simplify several physical aspects, which can cause erroneous simulation results. Here, an accurate yet simple mathematical description of the GFET’s current-voltage relation is proposed and implemented in Verilog-A. Comprehensive error analyses are done in order to highlight the advantages of the new approach. Furthermore, the model is verified against measurement results. The developed GFET model is an important step towards better understanding the characteristics and opportunities of graphene-based analog circuitry

Degradation in FPGAs: Monitoring, Modeling and Mitigation

This dissertation targets the transistor aging degradation as well as the associated thermal challenges in FPGAs (since there is an exponential relation between aging and chip temperature). The main objectives are to perform experimentation, analysis and device-level model abstraction for modeling the degradation in FPGAs, then to monitor the FPGA to keep track of aging rates and ultimately to propose an aging-aware FPGA design flow to mitigate the aging

Fabrication, characterization, and modeling of silicon multi-gate devices

Ph.DDOCTOR OF PHILOSOPH

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

Low-frequency noise in downscaled silicon transistors: Trends, theory and practice

By the continuing downscaling of sub-micron transistors in the range of few to one deca-nanometers, we focus on the increasing relative level of the low-frequency noise in these devices. Large amount of published data and models are reviewed and summarized, in order to capture the state-of-the-art, and to observe that the 1/area scaling of low-frequency noise holds even for carbon nanotube devices, but the noise becomes too large in order to have fully deterministic devices with area less than 10nm×10nm. The low-frequency noise models are discussed from the point of view that the noise can be both intrinsic and coupled to the charge transport in the devices, which provided a coherent picture, and more interestingly, showed that the models converge each to other, despite the many issues that one can find for the physical origin of each model. Several derivations are made to explain crossovers in noise spectra, variable random telegraph amplitudes, duality between energy and distance of charge traps, behaviors and trends for figures of merit by device downscaling, practical constraints for micropower amplifiers and dependence of phase noise on the harmonics in the oscillation signal, uncertainty and techniques of averaging by noise characterization. We have also shown how the unavoidable statistical variations by fabrication is embedded in the devices as a spatial “frozen noise”, which also follows 1/area scaling law and limits the production yield, from one side, and from other side, the “frozen noise” contributes generically to temporal 1/f noise by randomly probing the embedded variations during device operation, owing to the purely statistical accumulation of variance that follows from cause-consequence principle, and irrespectively of the actual physical process. The accumulation of variance is known as statistics of “innovation variance”, which explains the nearly log-normal distributions in the values for low-frequency noise parameters gathered from different devices, bias and other conditions, thus, the origin of geometric averaging in low-frequency noise characterizations. At present, the many models generally coincide each with other, and what makes the difference, are the values, which, however, scatter prominently in nanodevices. Perhaps, one should make some changes in the approach to the low-frequency noise in electronic devices, to emphasize the “statistics behind the numbers”, because the general physical assumptions in each model always fail at some point by the device downscaling, but irrespectively of that, the statistics works, since the low-frequency noise scales consistently with the 1/area law

Resilient Design for Process and Runtime Variations

The main objective of this thesis is to tackle the impact of parameter variations in order to improve the chip performance and extend its lifetime

Synthesis of silicon nanocrystal memories by sputter deposition

In Silizium-Nanokristall-Speichern werden im Gate-Oxid eines Feldeffekttransistors eingebettete Silizium Nanokristalle genutzt, um Elektronen lokal zu speichern. Die gespeicherte Ladung bestimmt dann den Zustand der Speicherzelle. Ein wichtiger Aspekt in der Technologie dieser Speicher ist die Erzeugung der Nanokristalle mit einerwohldefinierten Größenverteilung und einem bestimmten Konzentrationsprofil im Gate-Oxid. In der vorliegenden Arbeit wurde dazu ein sehr flexibler Ansatz untersucht: die thermische Ausheilung von SiO2/SiOx (x < 2) Stapelschichten. Es wurde ein Sputterverfahren entwickelt, das die Abscheidung von SiO2 und SiOx Schichten beliebiger Zusammensetzung erlaubt. Die Bildung der Nanokristalle wurde in Abhängigkeit vom Ausheilregime und der SiOx Zusammensetzung charakterisiert, wobei unter anderem Methoden wie Photolumineszenz, Infrarot-Absorption, spektroskopische Ellipsometrie und Elektronenmikroskopie eingesetzt wurden. Anhand von MOS-Kondensatoren wurden die elektrischen Eigenschaften derart hergestellter Speicherzellen untersucht. Die Funktionalität der durch Sputterverfahren hergestellten Nanokristall-Speicher wurde erfolgreich nachgewiesen.In silicon nanocrystal memories, electronic charge is discretely stored in isolated silicon nanocrystals embedded in the gate oxide of a field effect transistor. The stored charge determines the state of the memory cell. One important aspect in the technology of silicon nanocrystal memories is the formation of nanocrystals near the SiO2-Si interface, since both, the size distribution and the depth profile of the area density of nanocrystals must be controlled. This work has focussed on the formation of gate oxide stacks with embedded nanocrystals using a very flexible approach: the thermal annealing of SiO2/SiOx (x < 2) stacks. A sputter deposition method allowing to deposit SiO2 and SiOx films of arbitrary composition has been developed and optimized. The formation of Si NC during thermal annealing of SiOX has been investigated experimentally as a function of SiOx composition and annealing regime using techniques such as photoluminescence, infrared absorption, spectral ellipsometry, and electron microscopy. To proof the concept, silicon nanocrystal memory capacitors have been prepared and characterized. The functionality of silicon nanocrystal memory devices based on sputtered gate oxide stacks has been successfully demonstrated

Variability-Aware Circuit Performance Optimisation Through Digital Reconfiguration

This thesis proposes optimisation methods for improving the performance of circuits imple- mented on a custom reconfigurable hardware platform with knowledge of intrinsic variations, through the use of digital reconfiguration. With the continuing trend of transistor shrinking, stochastic variations become first order effects, posing a significant challenge for device reliability. Traditional device models tend to be too conservative, as the margins are greatly increased to account for these variations. Variation-aware optimisation methods are then required to reduce the performance spread caused by these substrate variations. The Programmable Analogue and Digital Array (PAnDA) is a reconfigurable hardware plat- form which combines the traditional architecture of a Field Programmable Gate Array (FPGA) with the concept of configurable transistor widths, and is used in this thesis as a platform on which variability-aware circuits can be implemented. A model of the PAnDA architecture is designed to allow for rapid prototyping of devices, making the study of the effects of intrinsic variability on circuit performance – which re- quires expensive statistical simulations – feasible. This is achieved by means of importing statistically-enhanced transistor performance data from RandomSPICE simulations into a model of the PAnDA architecture implemented in hardware. Digital reconfiguration is then used to explore the hardware resources available for performance optimisation. A bio-inspired optimisation algorithm is used to explore the large solution space more efficiently. Results from test circuits suggest that variation-aware optimisation can provide a significant reduction in the spread of the distribution of performance across various instances of circuits, as well as an increase in performance for each. Even if transistor geometry flexibility is not available, as is the case of traditional architectures, it is still possible to make use of the substrate variations to reduce spread and increase performance by means of function relocation

Synthesis of silicon nanocrystal memories by sputter deposition

In Silizium-Nanokristall-Speichern werden im Gate-Oxid eines Feldeffekttransistors eingebettete Silizium Nanokristalle genutzt, um Elektronen lokal zu speichern. Die gespeicherte Ladung bestimmt dann den Zustand der Speicherzelle. Ein wichtiger Aspekt in der Technologie dieser Speicher ist die Erzeugung der Nanokristalle mit einerwohldefinierten Größenverteilung und einem bestimmten Konzentrationsprofil im Gate-Oxid. In der vorliegenden Arbeit wurde dazu ein sehr flexibler Ansatz untersucht: die thermische Ausheilung von SiO2/SiOx (x < 2) Stapelschichten. Es wurde ein Sputterverfahren entwickelt, das die Abscheidung von SiO2 und SiOx Schichten beliebiger Zusammensetzung erlaubt. Die Bildung der Nanokristalle wurde in Abhängigkeit vom Ausheilregime und der SiOx Zusammensetzung charakterisiert, wobei unter anderem Methoden wie Photolumineszenz, Infrarot-Absorption, spektroskopische Ellipsometrie und Elektronenmikroskopie eingesetzt wurden. Anhand von MOS-Kondensatoren wurden die elektrischen Eigenschaften derart hergestellter Speicherzellen untersucht. Die Funktionalität der durch Sputterverfahren hergestellten Nanokristall-Speicher wurde erfolgreich nachgewiesen.In silicon nanocrystal memories, electronic charge is discretely stored in isolated silicon nanocrystals embedded in the gate oxide of a field effect transistor. The stored charge determines the state of the memory cell. One important aspect in the technology of silicon nanocrystal memories is the formation of nanocrystals near the SiO2-Si interface, since both, the size distribution and the depth profile of the area density of nanocrystals must be controlled. This work has focussed on the formation of gate oxide stacks with embedded nanocrystals using a very flexible approach: the thermal annealing of SiO2/SiOx (x < 2) stacks. A sputter deposition method allowing to deposit SiO2 and SiOx films of arbitrary composition has been developed and optimized. The formation of Si NC during thermal annealing of SiOX has been investigated experimentally as a function of SiOx composition and annealing regime using techniques such as photoluminescence, infrared absorption, spectral ellipsometry, and electron microscopy. To proof the concept, silicon nanocrystal memory capacitors have been prepared and characterized. The functionality of silicon nanocrystal memory devices based on sputtered gate oxide stacks has been successfully demonstrated

An Ultra-Low-Energy, Variation-Tolerant FPGA Architecture Using Component-Specific Mapping

As feature sizes scale toward atomic limits, parameter variation continues to increase, leading to increased margins in both delay and energy. Parameter variation both slows down devices and causes devices to fail. For applications that require high performance, the possibility of very slow devices on critical paths forces designers to reduce clock speed in order to meet timing. For an important and emerging class of applications that target energy-minimal operation at the cost of delay, the impact of variation-induced defects at very low voltages mandates the sizing up of transistors and operation at higher voltages to maintain functionality. With post-fabrication configurability, FPGAs have the opportunity to self-measure the impact of variation, determining the speed and functionality of each individual resource. Given that information, a delay-aware router can use slow devices on non-critical paths, fast devices on critical paths, and avoid known defects. By mapping each component individually and customizing designs to a component's unique physical characteristics, we demonstrate that we can eliminate delay margins and reduce energy margins caused by variation. To quantify the potential benefit we might gain from component-specific mapping, we first measure the margins associated with parameter variation, and then focus primarily on the energy benefits of FPGA delay-aware routing over a wide range of predictive technologies (45 nm--12 nm) for the Toronto20 benchmark set. We show that relative to delay-oblivious routing, delay-aware routing without any significant optimizations can reduce minimum energy/operation by 1.72x at 22 nm. We demonstrate how to construct an FPGA architecture specifically tailored to further increase the minimum energy savings of component-specific mapping by using the following techniques: power gating, gate sizing, interconnect sparing, and LUT remapping. With all optimizations considered we show a minimum energy/operation savings of 2.66x at 22 nm, or 1.68--2.95x when considered across 45--12 nm. As there are many challenges to measuring resource delays and mapping per chip, we discuss methods that may make component-specific mapping more practical. We demonstrate that a simpler, defect-aware routing achieves 70% of the energy savings of delay-aware routing. Finally, we show that without variation tolerance, scaling from 16 nm to 12 nm results in a net increase in minimum energy/operation; component-specific mapping, however, can extend minimum energy/operation scaling to 12 nm and possibly beyond.</p