Limits on Fundamental Limits to Computation

An indispensable part of our lives, computing has also become essential to industries and governments. Steady improvements in computer hardware have been supported by periodic doubling of transistor densities in integrated circuits over the last fifty years. Such Moore scaling now requires increasingly heroic efforts, stimulating research in alternative hardware and stirring controversy. To help evaluate emerging technologies and enrich our understanding of integrated-circuit scaling, we review fundamental limits to computation: in manufacturing, energy, physical space, design and verification effort, and algorithms. To outline what is achievable in principle and in practice, we recall how some limits were circumvented, compare loose and tight limits. We also point out that engineering difficulties encountered by emerging technologies may indicate yet-unknown limits.Comment: 15 pages, 4 figures, 1 tabl

A thermal simulation process based on electrical modeling for complex interconnect, packaging, and 3DI structures

To reduce the product development time and achieve first-pass silicon success, fast and accurate estimation of very-large-scale integration (VLSI) interconnect, packaging and 3DI (3D integrated circuits) thermal profiles has become important. Present commercial thermal analysis tools are incapable of handling very complex structures and have integration difficulties with existing design flows. Many analytical thermal models, which could provide fast estimates, are either too specific or oversimplified. This paper highlights a methodology, which exploits electrical resistance solvers for thermal simulation, to allow acquisition of thermal profiles of complex structures with good accuracy and reasonable computation cost. Moreover, a novel accurate closed-form thermal model is developed. The model allows an isotropic or anisotropic equivalent medium to replace the noncritical back-end-of-line (BEOL) regions so that the simulation complexity is dramatically reduced. Using these techniques, this paper introduces the thermal modeling of practical complex VLSI structures to facilitate thermal guideline generation. It also demonstrates the benefits of the proposed anisotropic equivalent medium approximation for real VLSI structures in terms of the accuracy and computational cost. © 2006 IEEE.published_or_final_versio

Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT

Statistical Methods for Semiconductor Manufacturing

In this thesis techniques for non-parametric modeling, machine learning, filtering and prediction and run-to-run control for semiconductor manufacturing are described. In particular, algorithms have been developed for two major applications area: - Virtual Metrology (VM) systems; - Predictive Maintenance (PdM) systems. Both technologies have proliferated in the past recent years in the semiconductor industries, called fabs, in order to increment productivity and decrease costs. VM systems aim of predicting quantities on the wafer, the main and basic product of the semiconductor industry, that may be physically measurable or not. These quantities are usually ’costly’ to be measured in economic or temporal terms: the prediction is based on process variables and/or logistic information on the production that, instead, are always available and that can be used for modeling without further costs. PdM systems, on the other hand, aim at predicting when a maintenance action has to be performed. This approach to maintenance management, based like VM on statistical methods and on the availability of process/logistic data, is in contrast with other classical approaches: - Run-to-Failure (R2F), where there are no interventions performed on the machine/process until a new breaking or specification violation happens in the production; - Preventive Maintenance (PvM), where the maintenances are scheduled in advance based on temporal intervals or on production iterations. Both aforementioned approaches are not optimal, because they do not assure that breakings and wasting of wafers will not happen and, in the case of PvM, they may lead to unnecessary maintenances without completely exploiting the lifetime of the machine or of the process. The main goal of this thesis is to prove through several applications and feasibility studies that the use of statistical modeling algorithms and control systems can improve the efficiency, yield and profits of a manufacturing environment like the semiconductor one, where lots of data are recorded and can be employed to build mathematical models. We present several original contributions, both in the form of applications and methods. The introduction of this thesis will be an overview on the semiconductor fabrication process: the most common practices on Advanced Process Control (APC) systems and the major issues for engineers and statisticians working in this area will be presented. Furthermore we will illustrate the methods and mathematical models used in the applications. We will then discuss in details the following applications: - A VM system for the estimation of the thickness deposited on the wafer by the Chemical Vapor Deposition (CVD) process, that exploits Fault Detection and Classification (FDC) data is presented. In this tool a new clustering algorithm based on Information Theory (IT) elements have been proposed. In addition, the Least Angle Regression (LARS) algorithm has been applied for the first time to VM problems. - A new VM module for multi-step (CVD, Etching and Litography) line is proposed, where Multi-Task Learning techniques have been employed. - A new Machine Learning algorithm based on Kernel Methods for the estimation of scalar outputs from time series inputs is illustrated. - Run-to-Run control algorithms that employ both the presence of physical measures and statistical ones (coming from a VM system) is shown; this tool is based on IT elements. - A PdM module based on filtering and prediction techniques (Kalman Filter, Monte Carlo methods) is developed for the prediction of maintenance interventions in the Epitaxy process. - A PdM system based on Elastic Nets for the maintenance predictions in Ion Implantation tool is described. Several of the aforementioned works have been developed in collaborations with major European semiconductor companies in the framework of the European project UE FP7 IMPROVE (Implementing Manufacturing science solutions to increase equiPment pROductiVity and fab pErformance); such collaborations will be specified during the thesis, underlying the practical aspects of the implementation of the proposed technologies in a real industrial environment

Top-Down Fabrication of Gate-All-Around Vertically-Stacked Silicon Nanowire FETs with Controllable Polarity

Asthe currentMOSFET scaling trend is facing strong limitations, technologies exploiting novel degrees of freedom at physical and architecture level are promising candidates to enable the continuation of Moore's predictions. In this paper, we report on the fabrication of novel ambipolar Silicon nanowire (SiNW) Schottky-barrier (SB) FET transistors featuring two independent gate-all-around electrodes and vertically stacked SiNW channels. A top-down approach was employed for the nanowire fabrication, using an e-beam lithography defined design pattern. In these transistors, one gate electrode enables the dynamic configuration of the device polarity (n- or p-type) by electrostatic doping of the channel in proximity of the source and drain SBs. The other gate electrode, acting on the center region of the channel switches ON or OFF the device. Measurement results on silicon show I-on/I-off > 10(6) and subthreshold slopes approaching the thermal limit, SS approximate to 64 mV/dec (70 mV/dec) for p(n)-type operation in the same physical device. Finally, we show that the XOR logic operation is embedded in the device characteristic, and we demonstrate for the first time a fully functional two-transistor XOR gate

Subthreshold Modeling and Simulation of Silicon Nanotube Field Effect Transistors (SiNTFETs)

The MOS technologies with low device geometry and new architectures have accelerated the pace of computational technology. In order to uphold the challenges of scaling in sub 20nm regime and meet the aggressive specifications of ITRS, a novel and non-conventional devices have to intervene. So came the ultimate solution- Silicon Nanotube Field Effect Transistor (SiNTFET) with its unique architecture which enhances the electrical characteristics of the device and the performance. In this work, an analytical model of surface potential and threshold voltage for SiNTFETs are developed. The two dimensional poisson’s equation with a cylindrical coordinate system, has been evaluated to find surface potential. The concentration of the inversion charge has been evaluated in the channel in subthreshold regime using the surface potential equation and the Boltzmann equation. The threshold voltage of the device is stated as the gate voltage for which the calculated inversion charge equals the threshold charge. Assuming this definition, the threshold voltage of the device for different channel lengths is mathematically modeled. The effect on threshold voltage by the variation of physical parameters is detailed analysed. The physical parameters include gate oxide thickness, tube thickness and core thickness. The effects of DIBL and voltage roll-off are discussed. The model results are verified with the simulation results obtained by using device simulator, ATLASTM. It is observed that for short channel lengths (<30nm), the model values vary from the simulated data; that is because the quantum mechanical effects are neglected during modeling which are vital in those channel lengths. The objective of the work is to provide a basic model for threshold voltage of the SiNTFET. The electrical characteristics show that device has a potential to set a new technology road map and meet the ULSI application

DESIGN, MODELING, OPTIMIZATION, AND BENCHMARKING OF INTERCONNECTS AND SCALING TECHNOLOGIES AND THEIR CIRCUIT AND SYSTEM LEVEL IMPACT

This research focuses on the future of integrated circuit (IC) scaling technologies at the device and back end of line (BEOL) level. This work includes high level modeling of different technologies and quantifying potential performance gains on a circuit and system level. From the device side, this research looks at the scaling challenges and the future scaling drivers for conventional charge-based devices implemented at the 7nm technology node and beyond. It examines the system-level performance of stacking device logic in addition to tunneling field effect transistors (TFET) and their potential as beyond-CMOS devices. Finally, this research models and benchmarks BEOL scaling challenges and evaluates proposed technological advancements such as metal barrier scaling for copper interconnects and replacing local interconnects with ruthenium. Potential impact on performance, power, and area of these interconnect technologies is quantified for fully placed and routed circuits.Ph.D

Statistical compact model strategies for nano CMOS transistors subject of atomic scale variability

One of the major limiting factors of the CMOS device, circuit and system simulation in sub 100nm regimes is the statistical variability introduced by the discreteness of charge and granularity of matter. The statistical variability cannot be eliminated by tuning the layout or by tightening fabrication process control. Since the compact models are the key bridge between technology and design, it is necessary to transfer reliably the MOSFET statistical variability information into compact models to facilitate variability aware design practice. The aim of this project is the development of a statistical extraction methodology essential to capture statistical variability with optimum set of parameters particularly in industry standard compact model BSIM. This task is accomplished by using a detailed study on the sensitivity analysis of the transistor current in respect to key parameters in compact model in combination with error analysis of the fitted Id-Vg characteristics. The key point in the developed direct statistical compact model strategy is that the impacts of statistical variability can be captured in device characteristics by tuning a limited number of parameters and keeping the values for remaining major set equal to their default values obtained from the “uniform” MOSFET compact model extraction. However, the statistical compact model extraction strategies will accurately represent the distribution and correlation of the electrical MOSFET figures of merit. Statistical compact model parameters are generated using statistical parameter generation techniques such as uncorrelated parameter distributions, principal component analysis and nonlinear power method. The accuracy of these methods is evaluated in comparison with the results obtained from ‘atomistic’ simulations. The impact of the correlations in the compact model parameters has been analyzed along with the corresponding transistor figures of merit. The accuracy of the circuit simulations with different statistical compact model libraries has been studied. Moreover, the impact of the MOSFET width/length on the statistical trend of the optimum set of statistical compact model parameters and electrical figures of merit has been analyzed with two methods to capture geometry dependencies in proposed statistical models

Statistical circuit simulations - from ‘atomistic’ compact models to statistical standard cell characterisation

This thesis describes the development and application of statistical circuit simulation methodologies to analyse digital circuits subject to intrinsic parameter fluctuations. The specific nature of intrinsic parameter fluctuations are discussed, and we explain the crucial importance to the semiconductor industry of developing design tools which accurately account for their effects. Current work in the area is reviewed, and three important factors are made clear: any statistical circuit simulation methodology must be based on physically correct, predictive models of device variability; the statistical compact models describing device operation must be characterised for accurate transient analysis of circuits; analysis must be carried out on realistic circuit components. Improving on previous efforts in the field, we posit a statistical circuit simulation methodology which accounts for all three of these factors. The established 3-D Glasgow atomistic simulator is employed to predict electrical characteristics for devices aimed at digital circuit applications, with gate lengths from 35 nm to 13 nm. Using these electrical characteristics, extraction of BSIM4 compact models is carried out and their accuracy in performing transient analysis using SPICE is validated against well characterised mixed-mode TCAD simulation results for 35 nm devices. Static d.c. simulations are performed to test the methodology, and a useful analytic model to predict hard logic fault limitations on CMOS supply voltage scaling is derived as part of this work. Using our toolset, the effect of statistical variability introduced by random discrete dopants on the dynamic behaviour of inverters is studied in detail. As devices scaled, dynamic noise margin variation of an inverter is increased and higher output load or input slew rate improves the noise margins and its variation. Intrinsic delay variation based on CV/I delay metric is also compared using ION and IEFF definitions where the best estimate is obtained when considering ION and input transition time variations. Critical delay distribution of a path is also investigated where it is shown non-Gaussian. Finally, the impact of the cell input slew rate definition on the accuracy of the inverter cell timing characterisation in NLDM format is investigated

Modeling and Simulation of Subthreshold Characteristics of Short-Channel Fully-Depleted Recessed-Source/Drain SOI MOSFETs

Non-conventional metal-oxide-semiconductor (MOS) devices have attracted researchers‟ attention for future ultra-large-scale-integration (ULSI) applications since the channel length of conventional MOS devices approached the physical limit. Among the non-conventional CMOS devices which are currently being pursued for the future ULSI, the fully-depleted (FD) SOI MOSFET is a serious contender as the SOI MOSFETs possess some unique features such as enhanced short-channel effects immunity, low substrate leakage current, and compatibility with the planar CMOS technology. However, due to the ultra-thin source and drain regions, FD SOI MOSFETs possess large series resistance which leads to the poor current drive capability of the device despite having excellent short-channel characteristics. To overcome this large series resistance problem, the source/drain area may be increased by extending S/D either upward or downward. Hence, elevated-source/drain (E-S/D) and recessed-source/drain (Re-S/D) are the two structures which can be used to minimize the series resistance problem. Due to the undesirable issues such as parasitic capacitance, current crowding effects, etc. with E-S/D structure, the Re-S/D structure is a better choice. The FD Re-S/D SOI MOSFET may be an attractive option for sub-45nm regime because of its low parasitic capacitances, reduced series resistance, high drive current, very high switching speed and compatibility with the planar CMOS technology. The present dissertation is to deal with the theoretical modeling and computer-based simulation of the FD SOI MOSFETs in general, and recessed source/drain (Re-S/D) ultra-thin-body (UTB) SOI MOSFETs in particular. The current drive capability of Re-S/D UTB SOI MOSFETs can be further improved by adopting the dual-metal-gate (DMG) structure in place of the conventional single-metal-gate-structure. However, it will be interesting to see how the presence of two metals as gate contact changes the subthreshold characteristics of the device. Hence, the effects of adopting DMG structure on the threshold voltage, subthreshold swing and leakage current of Re-S/D UTB SOI MOSFETs have been studied in this dissertation. Further, high-k dielectric materials are used in ultra-scaled MOS devices in order to cut down the quantum mechanical tunneling of carriers. However, a physically thick gate dielectric causes fringing field induced performance degradation. Therefore, the impact of high-k dielectric materials on subthreshold characteristics of Re-S/D SOI MOSFETs needs to be investigated. In this dissertation, various subthreshold characteristics of the device with high-k gate dielectric and metal gate electrode have been investigated in detail. Moreover, considering the variability problem of threshold voltage in ultra-scaled devices, the presence of a back-gate bias voltage may be useful for ultimate tuning of the threshold voltage and other characteristics. Hence, the impact of back-gate bias on the important subthreshold characteristics such as threshold voltage, subthreshold swing and leakage currents of Re-S/D UTB SOI MOSFETs has been thoroughly analyzed in this dissertation. The validity of the analytical models are verified by comparing model results with the numerical simulation results obtained from ATLAS™, a device simulator from SILVACO Inc