6-T SRAM cell design with nanoscale double-gate SOI MOSFETs: impact of source/drain engineering and circuit topology

The impact of source/drain engineering on the performance of a six-transistor (6-T) static random access memory (SRAM) cell, based on 22 nm double-gate (DG) SOI MOSFETs, has been analyzed using mixed-mode simulation, for three different circuit topologies for low voltage operation. The trade-offs associated with the various conflicting requirements relating to read/write/standby operations have been evaluated comprehensively in terms of eight performance metrics, namely retention noise margin, static noise margin, static voltage/current noise margin, write-ability current, write trip voltage/current and leakage current. Optimal design parameters with gate-underlap architecture have been identified to enhance the overall SRAM performance, and the influence of parasitic source/drain resistance and supply voltage scaling has been investigated. A gate-underlap device designed with a spacer-to-straggle (s/sigma) ratio in the range 2-3 yields improved SRAM performance metrics, regardless of circuit topology. An optimal two word-line double-gate SOI 6-T SRAM cell design exhibits a high SNM similar to 162 mV, I-wr similar to 35 mu A and low I-leak similar to 70 pA at V-DD = 0.6 V, while maintaining SNM similar to 30% V-DD over the supply voltage (V-DD) range of 0.4-0.9 V.

Source/drain extension region engineering in nanoscale double gate SOI MOSFETs: Novel design methodology for low-voltage analog applications

The present paper proposes for the first time, a novel design methodology based on the optimization of source/drain extension (SDE) regions to significantly improve the trade-off between intrinsic voltage gain (A(vo)) and cut-off frequency (f(T)) in nanoscale double gate (DG) devices. Our results show that an optimally designed 25 nm gate length SDE region engineered DG MOSFET operating at drain current of 10 mu A/mu m, exhibits up to 65% improvement in intrinsic voltage gain and 85% in cut-off frequency over devices designed with abrupt SIDE regions. The influence of spacer width, lateral source/drain doping gradient and symmetric as well as asymmetrically designed SDE regions on key analog figures of merit (FOM) such as transconductance (g(m)), transconductance-to-current ratio (g(m)/I-ds), Early voltage (V-EA), output conductance (g(ds)) and gate capacitances are examined in detail. The present work provides new opportunities for realizing future low-voltage/low-power analog circuits with nanoscale SDE engineered DG MOSFETs. (C) 2007 Elsevier B.V. All rights reserved.

Engineering source/drain extension regions in nanoscale double gate (DG) SOI MOSFETs: Analytical model and design considerations

In this paper, we propose for the first time, an analytical model for short channel effects in nanoscale source/drain extension region engineered double gate (DG) SOI MOSFETs. The impact of (i) lateral source/drain doping gradient (d), (ii) spacer width (s), (iii) spacer to doping gradient ratio (s/d) and (iv) silicon film thickness (T-si), on short channel effects - threshold voltage (V-th) and subthreshold slope (S), on-current (I-on), off-current (I-on) and I-on/I-off is extensively analysed by using the analytical model and 2D device simulations. The results of the analytical model confirm well with simulated data over the entire range of spacer widths, doping gradients and effective channel lengths. Results show that lateral source/drain doping gradient along with spacer width can not only effectively control short channel effects, thus presenting low off-current, but can also be optimised to achieve high values of on-currents. The present work provides valuable design insights in the performance of nanoscale DG Sol devices with optimal source/drain engineering and serves as a tool to optimise important device and technological parameters for 65 nm technology node and below. (c) 2006 Elsevier Ltd. All rights reserved.

Ferroelectrics at the Nanoscale

There are several factors which make the investigation and understanding of nanoscale ferroelectrics particularly timely and important. Firstly, there is a market pressure, primarily from the electronics industry, to integrate ferroelectrics into devices with progressive decreases in size and increases in morphological complexity. This is perhaps best illustrated through the roadmaps for product development in FeRAM (Ferroelectric Randorn Access Memory) where the need for increases in bit density will require a move from 2D planar capacitor structures to 3D trenched capacitors in the next few years. Secondly, there is opportunity for novel exploration, as it is only relatively recently that developments in thin film growth of complex oxides, self-assembly techniques and high-resolution 'top-down' patterning have converged to allow the fabrication of isolated and well-defined ferroelectric nanoshapes, the properties of which are not known. Thirdly, there is an expectation that the behaviour of small scale ferroelectrics will be different from bulk, as this group of functional materials is highly sensitive to boundary/surface conditions, which are expected to dominate the overall response when sizes are reduced into the nanoscale regime. This feature article attempts to introduce some of the current areas of discovery and debate surrounding studies on ferroelectrics at the nanoscale. The focus is directed primarily at the search for novel size-related properties and behaviour which are not necessarily observed in bulk.

An efficient neural network approach for nanoscale FinFET modelling and circuit simulation

The present paper demonstrates the suitability of artificial neural network (ANN) for modelling of a FinFET in nano-circuit simulation. The FinFET used in this work is designed using careful engineering of source-drain extension, which simultaneously improves maximum frequency of oscillation f(max) because of lower gate to drain capacitance, and intrinsic gain A(V0) = g(m)/g(ds), due to lower output conductance g(ds). The framework for the ANN-based FinFET model is a common source equivalent circuit, where the dependence of intrinsic capacitances, resistances and dc drain current I-d on drain-source V-ds and gate-source V-gs is derived by a simple two-layered neural network architecture. All extrinsic components of the FinFET model are treated as bias independent. The model was implemented in a circuit simulator and verified by its ability to generate accurate response to excitations not used during training. The model was used to design a low-noise amplifier. At low power (J(ds) similar to 10 mu A/mu m) improvement was observed in both third-order-intercept IIP3 (similar to 10 dBm) and intrinsic gain A(V0) (similar to 20 dB), compared to a comparable bulk MOSFET with similar effective channel length. This is attributed to higher ratio of first-order to third-order derivative of I-d with respect to gate voltage and lower g(ds), in FinFET compared to bulk MOSFET. Copyright (C) 2009 John Wiley & Sons, Ltd.

Domain Wall Propagation in Meso and Nanoscale Ferroelectrics

As part of an ongoing programme to evaluate the extent to which external morphology alters domain wall mobility in ferroelectrics, the electrical switching characteristics of single-crystal BaTiO3 nanorods and thin film plates have been measured and compared. It was found that ferroelectric nanorods were more readily switched than thin plates; increasing the shape constraint therefore appears to enhance switchability. This observation is broadly consistent with previous work, in which local notches patterned along the length of nanorods enhanced switching (McMillen et al 2010 Appl. Phys. Lett. 96 042904), while antinotches had the opposite effect (McQuaid et al 2010 Nano Lett. 10 3566). In this prior work, local enhancement and denudation of the electric field was expected at the notch and antinotch sites, respectively, and this was thought to be the reason for the differences in switching behaviour observed. However, for the simple nanorods and plates investigated here, no differences in the electric field distributions are expected. To rationalise the functional measurements, domain development during switching was imaged directly by piezoresponse force microscopy. A two-stage process was identified, in which narrow needle-like reverse domains initially form across the entire interelectrode gap and then subsequently coarsen through domain wall propagation perpendicular to the applied electric field. To be consistent with the electrical switching data, we suggest that the initial formation of needle domains occurs more readily in the nanorods than in the plates.

Nonconservative current-driven dynamics: beyond the nanoscale

Long metallic nanowires combine crucial factors for nonconservative current-driven atomic motion. These systems have degenerate vibrational frequencies, clustered about a Kohn anomaly in the dispersion relation, that can couple under current to form nonequilibrium modes of motion growing exponentially in time. Such motion is made possible by nonconservative current-induced forces on atoms, and we refer to it generically as the waterwheel effect. Here the connection between the waterwheel effect and the stimulated directional emission of phonons propagating along the electron flow is discussed in an intuitive manner. Nonadiabatic molecular dynamics show that waterwheel modes self-regulate by reducing the current and by populating modes in nearby frequency, leading to a dynamical steady state in which nonconservative forces are counter-balanced by the electronic friction. The waterwheel effect can be described by an appropriate effective nonequilibrium dynamical response matrix. We show that the current-induced parts of this matrix in metallic systems are long-ranged, especially at low bias. This nonlocality is essential for the characterisation of nonconservative atomic dynamics under current beyond the nanoscale.

Spiking neural network connectivity and its potential for temporal sensory processing and variable binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well-understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modeling of neural circuits found in the brain.

Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modelling of neural circuits found in the brain. In recent years, much of the focus in neuron modelling has moved to the study of the connectivity of spiking neural networks. Spiking neural networks provide a vehicle to understand from a computational perspective, aspects of the brain’s neural circuitry. This understanding can then be used to tackle some of the historically intractable issues with artificial neurons, such as scalability and lack of variable binding. Current knowledge of feed-forward, lateral, and recurrent connectivity of spiking neurons, and the interplay between excitatory and inhibitory neurons is beginning to shed light on these issues, by improved understanding of the temporal processing capabilities and synchronous behaviour of biological neurons. This research topic aims to amalgamate current research aimed at tackling these phenomena.

Mutism and Amnesia following High-Voltage Electrical Injury: Psychogenic Symptomatology Triggered by Organic Dysfunction?

Background: Mutism and dense retrograde amnesia are found both in organic and dissociative contexts. Moreover, dissociative symptoms may be modulated by right prefrontal activity. A single case, M.R., developed left hemiparesis, mutism and retrograde amnesia after a high-voltage electric shock without evidence of lasting brain lesions. M.R. suddenly recovered from his mutism following a mild brain trauma 2 years later. Methods: M.R.'s neuropsychological pattern and anatomoclinical correlations were studied through (i) language and memory assessment to characterize his deficits, (ii) functional neuroimaging during a standard language paradigm, and (iii) assessment of frontal and left insular connectivity through diffusion tractography imaging and transcranial magnetic stimulation. A control evaluation was repeated after recovery. Findings: M.R. recovered from the left hemiparesis within 90 days of the accident, which indicated a transient right brain impairment. One year later, neurobehavioral, language and memory evaluations strongly suggested a dissociative component in the mutism and retrograde amnesia. Investigations (including MRI, fMRI, diffusion tensor imaging, EEG and r-TMS) were normal. Twenty-seven months after the electrical injury, M.R. had a very mild head injury which was followed by a rapid recovery of speech. However, the retrograde amnesia persisted. Discussion: This case indicates an interaction of both organic and dissociative mechanisms in order to explain the patient's symptoms. The study also illustrates dissociation in the time course of the two different dissociative symptoms in the same patient.

Performance evaluation of routing protocols for MANETs with known connectivity patterns using evolving graphs

The assessment of routing protocols for mobile wireless networks is a difficult task, because of the networks` dynamic behavior and the absence of benchmarks. However, some of these networks, such as intermittent wireless sensors networks, periodic or cyclic networks, and some delay tolerant networks (DTNs), have more predictable dynamics, as the temporal variations in the network topology can be considered as deterministic, which may make them easier to study. Recently, a graph theoretic model-the evolving graphs-was proposed to help capture the dynamic behavior of such networks, in view of the construction of least cost routing and other algorithms. The algorithms and insights obtained through this model are theoretically very efficient and intriguing. However, there is no study about the use of such theoretical results into practical situations. Therefore, the objective of our work is to analyze the applicability of the evolving graph theory in the construction of efficient routing protocols in realistic scenarios. In this paper, we use the NS2 network simulator to first implement an evolving graph based routing protocol, and then to use it as a benchmark when comparing the four major ad hoc routing protocols (AODV, DSR, OLSR and DSDV). Interestingly, our experiments show that evolving graphs have the potential to be an effective and powerful tool in the development and analysis of algorithms for dynamic networks, with predictable dynamics at least. In order to make this model widely applicable, however, some practical issues still have to be addressed and incorporated into the model, like adaptive algorithms. We also discuss such issues in this paper, as a result of our experience.

Evaluation of the effect of the stoichiometric ratio of Ca/Cu on the electrical and microstructural properties of the CaCu3Ti4O12 polycrystalline system

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Nanoscale electromechanical properties of CaCu3Ti4O12 ceramics

Piezoresponse Force Microscopy (PFM) is used to characterize the nanoscale electromechanical properties of centrosymmetric CaCu3Ti4O12 ceramics with giant dielectric constant. Clear PFM contrast both in vertical (out-of-plane) and lateral (in-plane) modes is observed on the ceramic surface with varying magnitude and polarization direction depending on the grain crystalline orientation. Lateral signal changes its sign upon 180 degrees rotation of the sample thus ruling out spurious electrostatic contribution and confirming piezoelectric nature of the effect. Piezoresponse could be locally reversed by suitable electrical bias (local poling) and induced polarization was quite stable showing long-time relaxation (similar to 3 hrs). The electromechanical contrast in unpoled ceramics is attributed to the surface flexoelectric effect (strain gradient induced polarization) while piezoresponse hysteresis and ferroelectric-like behavior are discussed in terms of structural instabilities due to Ti off-center displacements and structural defects in this material. (C) 2011 American Institute of Physics. [doi:10.1063/1.3623767]

Electrical percolation during gelation of lithium doped siloxane-poly(oxyethylene) hybrids

Gelation mechanisms of lithium-doped Siloxane-Poly(oxyethylene) (PEO) hybrids containing polymer of two different molecular weight (500 and 1900 g/mol) were investigated through the evolution of the electrical properties during the solgel transition. The results of electrical measurements, performed by in-situ complex impedance spectroscopy, were correlated with the coordination and the dynamical properties of the lithium ions during the process as shown by Li-7 NMR measurements. For both hybrids sols, a decrease of the conductivity is observed at the initial gelation stage, due to the existence of an inverted percolation process consisting of the progressive separation of solvent molecules containing conducting species in isolated islands during the solid network formation. An increase of conductivity occurs at more advanced stages of gelation and aging, attributed to the increasing connectivity between PEO chains promoted by the formation of crosslinks of siloxane particles at their extremities, favoring hopping motions of lithium ions along the chains.

Effect of Composition on the Physical Properties at Nanoscale of PZT Thin Films

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)