R-3 Repository :: Browsing by Author "Massoud, Yehia"

Browsing by Author "Massoud, Yehia"

Now showing 1 - 13 of 13

Accurate estimation of design metrics in deep submicron circuits: RLC interconnect delay and crosstalk induced power
(2006) Mondal, Mosin; Massoud, Yehia
The advent of the deep submicron (DSM) era was accompanied by a number of challenging effects that severely impact the performance of modern integrated circuits. In this thesis, we present models and methods for the correct estimation of two design metrics: RLC delay and crosstalk induced dynamic power. We develop an efficient analytical model for the loop self inductance that accurately estimates inductance at a given frequency. This model can be used for fast and accurate RLC delay calculation. The simplicity, precision and efficiency of our model can greatly facilitate any application that requires fast estimation of inductance, such as inductance aware physical synthesis. We also present an integrated methodology for analyzing crosstalk induced power dissipation in cell based digital designs. Techniques for estimating the switching and short circuit power are presented. A new cell pre-characterization technique for estimating the additional short circuit power is developed. A heuristic for computing the additional energy on a switching victim net is also presented in this work. Results demonstrate that the models and techniques developed in this work are accurate and efficient.
Automated Detection and Differential Diagnosis of Non-small Cell Lung Carcinoma Cell Types Using Label-free Molecular Vibrational Imaging
(2012-09-05) Hammoudi, Ahmad; Varman, Peter J.; Massoud, Yehia; Wong, Stephen T. C.; Clark, John W., Jr.; Aazhang, Behnaam
Lung carcinoma is the most prevalent type of cancer in the world, considered to be a relentlessly progressive disease, with dismal mortality rates to patients. Recent advances in targeted therapy hold the premise for the delivery of better, more effective treatments to lung cancer patients, that could significantly enhance their survival rates. Optimizing care delivery through targeted therapies requires the ability to effectively identify and diagnose lung cancer along with identifying the lung cancer cell type specific to each patient, $$\textit{small cell carcinoma}$$, $$\textit{adenocarcinoma}$$, or $$\textit{squamous cell carcinoma}$$. Label free optical imaging techniques such as the $$\textit{Coherent anti-stokes Raman Scattering microscopy}$$ have the potential to provide physicians with minimally invasive access to lung tumor sites, and thus allow for better cancer diagnosis and sub-typing. To maximize the benefits of such novel imaging techniques in enhancing cancer treatment, the development of new data analysis methods that can rapidly and accurately analyze the new types of data provided through them is essential. Recent studies have gone a long way to achieving those goals but still face some significant bottlenecks hindering the ability to fully exploit the diagnostic potential of CARS images, namely, the streamlining of the diagnosis process was hindered by the lack of ability to automatically detect cancer cells, and the inability to reliably classify them into their respective cell types. More specifically, data analysis methods have thus far been incapable of correctly identifying and differentiating the different non-small cel lung carcinoma cell types, a stringent requirement for optimal therapy delivery. In this study we have addressed the two bottlenecks named above, through designing an image processing framework that is capable of, automatically and accuratly, detecting cancer cells in two and three dimensional CARS images. Moreover, we built upon this capability with a new approach at analyzing the segmented data, that provided significant information about the cancerous tissue and ultimately allowed for the automatic differential classification of non-small cell lung carcinoma cell types, with superb accuracies.
Classification Techniques for Undersampled Electromyography and Electrocardiography
(2012-10-01) Wilhelm, Keith; Varman, Peter J.; Massoud, Yehia; Clark, John W., Jr.; Koushanfar, Farinaz
Electrophysiological signals including electrocardiography (ECG) and electromyography (EMG) are widely used in clinical environments for monitoring of patients and for diagnosis of conditions including cardiac and neuromuscular disease. Due to the wealth of information contained in these signals, many additional applications would be facilitated by full-time acquisition combined with automated analysis. Recent performance gains in portable computing devices and large scale computing platforms provide the necessary computational resources to process and store this data; however challenges at the sensor level have prevented monitoring systems from reaching the practicality and convenience necessary for widespread, continuous use. In this thesis, we examine the feasibility of applying techniques from the compressive sensing field to the acquisition and analysis of electrophysiological signals. These techniques allow signals to be acquired in compressed form, thereby providing a means to reduce power consumption of monitoring devices. We demonstrate the effects of several methods of compressive sampling and reconstruction on standard compression and reconstruction error metrics. Additionally, we investigate the effects of compressive sensing on the accuracy of automated signal analysis techniques for extracting useful information from ECG and EMG signals.
Compact models for nanophotonic structures and on -chip interconnects
(2007) Alam, Mehboob; Massoud, Yehia
Over the last few years, scaling in deep submicron technologies has shifted the paradigm from device-dominated to interconnect-dominated design methodology. Consequently, there is an increasing interest towards the miniaturization of the guiding medium in nanoscale integrated circuits by exploring plasmon-based waveguides to alleviate the scaling issues associated with today's copper interconnect. In this thesis, we seek short and long-term solutions of on-chip interconnect by developing accurate compact models of on-chip interconnects and impedance characterization of nanophotonic structures. The developed system models are compact and accurate over the operating frequency range and the adopted approach have provided many critical insights and produced many important results. This thesis first presents a new modeling strategy that represents the nanostructure by its equivalent impedance. By applying either quasistatic approximation or separately solving for voltage and current for dominant mode, we reduce the field problem to a circuit problem. The impedance expressed in terms of circuit components is dependent on the material constant as well as the operating frequency. The modeling methodology is successfully applied to nanoparticles and oscillating nanosphere. The proposed model characterizes plasmon resonance in these nanostructures, thereby providing basic building block to develop spice models of complex plasmon-based waveguide for sub-wavelength propagation. We also presented several techniques to develop compact models of on-chip interconnects and passive components for accurate estimation of power, noise and delay of high speed integrated circuits. The automated method generates reduced order models that are accurate across either a narrow or a wide-range of frequencies. The proposed methods are based on Krylov subspace method with interpolation points dynamically selected using either spline based algorithm or discrete wavelet transform. Narrow and wideband frequency projection are also achieved using spectral zeros by applying either a frequency selective scheme or an adaptive wavelet transform to dynamically select spectral zeros. To demonstrate the efficacy of the approach, we simulated complex circuit models of spiral inductors, RLC networks and interconnect busses. The results indicate greater accuracy than techniques that apply other Krylov subspace methods or Singular Value Decomposition (SVD) based methods for model order reduction of on-chip interconnects.
Design of a Fast, Efficient and Controlled DNA Shearing System Based on Lateral Acoustic Waves
(2012) Dev, Kapil; Massoud, Yehia
With the continuous research and advances in Deoxyribonucleic acid (DNA) sequencing technologies, the need for an efficient DNA shearing system has increased more than ever before. In this thesis, we propose a fast, efficient and controlled DNA shearing system based on a uniquely designed ultrasonic transducer, called Fresnel Annular Sector Actuator (FASA). Based on the simulation and experimental results, a circular array of four 90°-FASA elements is chosen as the basic unit for the proposed shearing system. DNA is successfully sheared from 300 to 1500 base-pair lengths. The shearing performance of the system is independent of the source of DNA over a large range of concentrations of the DNA. Finally, multiple FASA elements, excited by separate BF-signals, are used to increase the throughput of the proposed shearing system.
Design Techniques for Robust Analog Signal Acquisition
(2012-10-02) Singal, Vikas; Varman, Peter J.; Massoud, Yehia; Clark, John W., Jr.; Koushanfar, Farinaz
The random demodulator architecture is a compressive sensing based receiver that allows the reconstruction of frequency-sparse signals from measurements acquired at a rate below the signal’s Nyquist rate. This in turn results in tremendous power savings in receivers because of the direct correlation between the power consumption of analog-to-digital converters (ADCs) in communication receivers and the sampling rate at which these ADCs operate. In this thesis, we propose design techniques for a robust and efficient random demodulator. We tackle two critical components that are most critical, the resetting mechanism of the integrator and the random sequence. On the one hand, the resetting mechanism can pose challenges in practical settings that can degrade the performance of the random demodulator. We propose practical approaches to mitigate the effect of resetting and propose resetting schemes that provide robust performance. On the other hand, the random sequence is a central part in the system and the properties of this sequence directly affect the properties of the whole system. We study the performance of the random demodulator under many practical random sequences such as maximal length sequences and Kasami sequences and provide pros and cons of using each in the random demodulator.
Efficient Architectures for Wideband Receivers
(2012-08-29) El Smaili, Sami; Varman, Peter J.; Massoud, Yehia; Clark, John W., Jr.; Koushanfar, Farinaz
Reducing power consumption of radio receivers is becoming more critical with the advancement of biomedical portable and implantable devices due to the stringent power requirements in such applications. Compressive sensing promises to tremendously reduce the power of radio receivers by allowing the reconstruction of sparse signals from measurements acquired at a sub-Nyquist rate. A key component in compressive sensing systems is the random signal which is used to acquire the measurements. Most e orts have been devoted to the design of signals with high randomness but little have been devoted to manipulating the random signal to suite a speci fic application, meet certain specifi cations, or enhance the performance of the system. This thesis tackles compressive sensing systems from this angle. We first propose an architecture that alleviates a critical requirement in compressive sensing: that the random signal should run at the Nyquist rate, which becomes prohibitive as the signal bandwidth increases. We provide theoretical and experimental results that demonstrate the e ectiveness of the proposed architecture. Secondly, we propose a framework for manipulating the random signal in the frequency domain as suitable for speci c applications. We use the framework to develop an architecture for recon gurable ultra wide-band radios.
Metal-insulator-metal based nanoscale photonic components
(2007) Hosseini, Amir; Massoud, Yehia
In terms of speed and bandwidth, photonic components are superior to their electronic counterparts. However, the diffraction limit has restricted downscaling of the conventional dielectric waveguides, where electromagnetic modes are confined to an optically dens core. Electromagnetic modes traveling along dielectric-metallic interfaces, known as surface plasmon polaritons (SPPs), have been proposed as a solution to the diffraction limit. Among possible plasmonic-based configurations, those which focus light into the dielectric core in a metal-insulator-metal (MIM) structure allow the manipulation and transmission of light at the nanoscale. In this thesis, first we develop an efficient method for designing the geometry of dielectric strip plasmonic structures, 2D generalizations for MIM structures, for future subwavelength wave-guiding applications. We formulate and solve the dielectric strip design optimization problem to ensure mono-mode propagation while balancing propagation losses and light confinement. Then, MIM-based Brag reflectors, photonic crystal and ring resonators, as building blocks of the future optical circuits, are presented and investigated. We show that compact MIM-based components may exhibit characteristics that are different from conventional dielectric optical components.
Method and apparatus for on-line compressed sensing
(2014-04-01) Baraniuk, Richard G.; Baron, Dror Z.; Duarte, Marco F.; Elnozahi, Mohamed; Wakin, Michael B.; Davenport, Mark A.; Laska, Jason N.; Tropp, Joel A.; Massoud, Yehia; Kirolos, Sami; Ragheb, Tamer; Rice University; United States Patent and Trademark Office
A typical data acquisition system takes periodic samples of a signal, image, or other data, often at the so-called Nyquist/Shannon sampling rate of two times the data bandwidth in order to ensure that no information is lost. In applications involving wideband signals, the Nyquist/Shannon sampling rate is very high, even though the signals may have a simple underlying structure. Recent developments in mathematics and signal processing have uncovered a solution to this Nyquist/Shannon sampling rate bottleneck for signals that are sparse or compressible in some representation. We demonstrate and reduce to practice methods to extract information directly from an analog or digital signal based on altering our notion of sampling to replace uniform time samples with more general linear functionals. One embodiment of our invention is a low-rate analog-to-information converter that can replace the high-rate analog-to-digital converter in certain applications involving wideband signals. Another embodiment is an encoding scheme for wideband discrete-time signals that condenses their information content.
Modeling and design of carbon nanotube interconnect for mixed-signal VLSI applications
(2008) Nieuwoudt, Arthur; Massoud, Yehia
In future nanoscale integrated circuits, process technology scaling coupled with increasing operating frequencies will exacerbate the resistivity, electromigration, and delay problems that plague interconnect in today's designs. Metallic carbon nanotubes are a promising future replacement for on-chip copper interconnect due to their large conductivity and current carrying capabilities. In this research, we develop modeling and design techniques for carbon nanotube-based interconnect solutions. We create an equivalent RLC circuit model for individual and bundled single-walled and multi-walled carbon nanotubes, which we leverage to determine the optimal design for nanotube-based interconnect solutions. Using the proposed modeling and design techniques, we investigate the performance and reliability of nanotube-based structures in future mixed-signal VLSI applications including digital interconnect and passive components for analog integrated circuits. We also examine the nanotube properties and fabrication requirements necessary for nanotube-based interconnect to be a competitive solution compared to standard copper technology. The results indicate that nanotube-based interconnect solutions will have the potential to revolutionize the next generation of integrated circuits in mixed-signal VLSI applications.
Modeling and variability aware design techniques for mixed signal systems
(2009) Ragheb, Tamer; Massoud, Yehia
The tremendous advancement in the field of integrated circuit manufacturing achieved in the past decade can largely be attributed to the successful continuous scaling of CMOS technology combined with the increase in the operating frequencies. However, the continuous feature scaling has significantly increased the impact of process variation on the performance of deep submicron integrated circuits. Process variation has become a major concern for designers since the fabricated circuit performance can vary dramatically from the predicted designed performance leading to a significant yield loss. Variability-aware design techniques have emerged as a potential solution that considers the process variation effects during the design stage. In this dissertation, we develop variability-aware design techniques for mixed-signal systems that can mitigate the impact of process variation and generate robust circuits and systems. Since Network-on-Chip (NoC) is the potential alternative for the traditional common-bus architectures specially for multi-core digital systems, we analyze the delay variation in NoC interconnects due to process variation. We develop Elmore delay based model for the delay variability in the buffered links due to the variation in the effective channel length because of lithographic errors and the variation in the interconnect parasitic resistance because of dishing. Furthermore, we develop closed form expression for probability of link failure due to delay variation in order to optimize the clock slack for minimum delay. Our simulation results demonstrate the effectiveness of this design technique. In addition, we present a new dynamic routing algorithm that has the ability to solve the physical layer problems such as static faults and electromigration effects by leveraging higher network layers. Our algorithm utilizes the communication signals between the neighbour routers to avoid the faulty links and to balance the AC currents on each link in order to alleviate the electromigration aging process. Our simulation results depict the improvement in the average latency and energy consumption using our fault-aware dynamic routing algorithm, while the results show the boost in the expected lifetime of the chip using our electromigration-aware dynamic routing algorithm. Due to the crucial importance of Low Noise Amplifier (LNA) as the performance bottleneck for RF receivers for either on-chip interconnects or off-chip wireless communication, in this dissertation, we develop different variability-aware design techniques that can mitigate the impact of process variation on the LNA performance dramatically. We develop an analytical modeling technique for both narrow band and wideband LNAs combined with a hierarchical design optimization platform to generate the required design specifications under system-on-chip integration constraints. In order to demonstrate the universality of our variability-aware design techniques for other analog/RF circuits, we leverage our presented design platform to optimize the design for LNA/Mixer pair. Our simulation results illustrate the success of our design techniques in achieving the required robust performance. Finally, we present a prototype hardware for compressive sensing based analog-to-information converters (AICs) that enables efficient communication for either on-chip or off-chip applications. We also present a behavioral modeling technique to evaluate the performance of the compressive sensing based applications.
Modeling, optimization and synthesis for fully integrated spiral inductors
(2006) Nieuwoudt, Arthur; Massoud, Yehia
Accurate and efficient modeling, optimization, and synthesis of integrated spiral inductors continue to hinder the automated design of mixed-signal circuits in system-on-chip technology. In this thesis, we develop a modeling and automated design methodology for integrated spiral inductors. We have created a wideband inductor model based on closed-form analytical expressions to capture a plethora of resistive, inductive, and capacitive parasitic effects. Leveraging the speed of the inductor model, we have developed a variability-aware automated design methodology that efficiently generates Pareto-optimal inductors based on application requirements. At its core the automated design methodology employs a scalable multi-level single-objective optimization engine that integrates the flexibility of deterministic pattern search optimization with the rapid convergence of local nonlinear convex optimization. The results demonstrate that the inductor modeling, optimization, and synthesis methodology accurately locates and characterizes near-optimal inductor designs with orders of magnitude speed improvement when compared with existing modeling and optimization techniques.
PVT variation-aware timing analysis and adaptive circuit techniques for robust synchronous integrated circuits
(2008) Kirolos, Sami M.; Massoud, Yehia
Over the last few years, considerable variability in deep submicron integrated circuits has become a major concern for designers since the actual performance can vary drastically from the predicted performance leading to yield loss. Potential solutions and research trends include better estimation of the variability impact to reduce circuit overdesign, and adaptable and redundant circuits which provide inherent circuit robustness to variability. In this thesis, we develop accurate models for PVT induced delay variability as well as self-adjusting adaptive circuits that alleviate many of the present and future problems associated with variability. We present a new model that approximates the BSIM4 MOSFET equations to derive the resistances of the pull-up and pull-down networks and thereby derive the noise rejection curves without performing expensive circuit simulations. The model can predict the effect of parameter variations on the noise rejection curves accurately since it is based upon device equations. Our model can predict the noise susceptibility under parameter variations more than five orders of magnitude faster than circuit simulations, which makes it suitable for design optimization for noise robustness. Furthermore, we present a thermally adaptive 3D clocking scheme that senses the ambient temperature and dynamically adjusts the driving strengths of the clock buffers to reduce the clock skew between terminals. Simulation results demonstrate that the dynamically adaptive design technique is capable of reducing the clock skew, leading to thermally robust clock tree designs for 3D integrated circuits. Additionally, we present an adaptive clock buffer circuit design and an adaptive clock distribution network to mitigate the effect of global power-supply variations on clock distribution buffers as well as local variations on local pipelined logic circuits. The adaptive buffer provides a supply insensitive propagation delay to minimize the clock skew in clock distribution networks, as well as dynamic clock skew scheduling to prevent timing violations. We present another adaptive circuit design that is capable of increasing the effective size-ratio for extended balanced operation in the subthreshold region as well as decreasing the size-ratio for high performance at the nominal V DD . Therefore, for designs working under DVS schemes, our technique presents a suitable solution for balanced minimum energy operation.

Browsing by Author "Massoud, Yehia"

Results Per Page

Sort Options