Matching Items (127)
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- All Subjects: Electrical Engineering
- Creators: Chakrabarti, Chaitali
- Creators: Goodnick, Stephen
Description
Process variations have become increasingly important for scaled technologies starting at 45nm. The increased variations are primarily due to random dopant fluctuations, line-edge roughness and oxide thickness fluctuation. These variations greatly impact all aspects of circuit performance and pose a grand challenge to future robust IC design. To improve robustness, efficient methodology is required that considers effect of variations in the design flow. Analyzing timing variability of complex circuits with HSPICE simulations is very time consuming. This thesis proposes an analytical model to predict variability in CMOS circuits that is quick and accurate. There are several analytical models to estimate nominal delay performance but very little work has been done to accurately model delay variability. The proposed model is comprehensive and estimates nominal delay and variability as a function of transistor width, load capacitance and transition time. First, models are developed for library gates and the accuracy of the models is verified with HSPICE simulations for 45nm and 32nm technology nodes. The difference between predicted and simulated σ/μ for the library gates is less than 1%. Next, the accuracy of the model for nominal delay is verified for larger circuits including ISCAS'85 benchmark circuits. The model predicted results are within 4% error of HSPICE simulated results and take a small fraction of the time, for 45nm technology. Delay variability is analyzed for various paths and it is observed that non-critical paths can become critical because of Vth variation. Variability on shortest paths show that rate of hold violations increase enormously with increasing Vth variation.
ContributorsGummalla, Samatha (Author) / Chakrabarti, Chaitali (Thesis advisor) / Cao, Yu (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Arizona State University (Publisher)
Created2011
Description
One of the challenges in future semiconductor device design is excessive rise of power dissipation and device temperatures. With the introduction of new geometrically confined device structures like SOI, FinFET, nanowires and continuous incorporation of new materials with poor thermal conductivities in the device active region, the device thermal problem is expected to become more challenging in coming years. This work examines the degradation in the ON-current due to self-heating effects in 10 nm channel length silicon nanowire transistors. As part of this dissertation, a 3D electrothermal device simulator is developed that self-consistently solves electron Boltzmann transport equation with 3D energy balance equations for both the acoustic and the optical phonons. This device simulator predicts temperature variations and other physical and electrical parameters across the device for different bias and boundary conditions. The simulation results show insignificant current degradation for nanowire self-heating because of pronounced velocity overshoot effect. In addition, this work explores the role of various placement of the source and drain contacts on the magnitude of self-heating effect in nanowire transistors. This work also investigates the simultaneous influence of self-heating and random charge effects on the magnitude of the ON current for both positively and negatively charged single charges. This research suggests that the self-heating effects affect the ON-current in two ways: (1) by lowering the barrier at the source end of the channel, thus allowing more carriers to go through, and (2) via the screening effect of the Coulomb potential. To examine the effect of temperature dependent thermal conductivity of thin silicon films in nanowire transistors, Selberherr's thermal conductivity model is used in the device simulator. The simulations results show larger current degradation because of self-heating due to decreased thermal conductivity . Crystallographic direction dependent thermal conductivity is also included in the device simulations. Larger degradation is observed in the current along the [100] direction when compared to the [110] direction which is in agreement with the values for the thermal conductivity tensor provided by Zlatan Aksamija.
ContributorsHossain, Arif (Author) / Vasileska, Dragica (Thesis advisor) / Ahmed, Shaikh (Committee member) / Bakkaloglu, Bertan (Committee member) / Goodnick, Stephen (Committee member) / Arizona State University (Publisher)
Created2011
Description
Programmable Metallization Cell (PMC) is a technology platform which utilizes mass transport in solid or liquid electrolyte coupled with electrochemical (redox) reactions to form or remove nanoscale metallic electrodeposits on or in the electrolyte. The ability to redistribute metal mass and form metallic nanostructure in or on a structure in situ, via the application of a bias on laterally placed electrodes, creates a large number of promising applications. A novel PMC-based lateral microwave switch was fabricated and characterized for use in microwave systems. It has demonstrated low insertion loss, high isolation, low voltage operation, low power and low energy consumption, and excellent linearity. Due to its non-volatile nature the switch operates with fewer biases and its simple planar geometry makes possible innovative device structures which can be potentially integrated into microwave power distribution circuits. PMC technology is also used to develop lateral dendritic metal electrodes. A lateral metallic dendritic network can be grown in a solid electrolyte (GeSe) or electrodeposited on SiO2 or Si using a water-mediated method. These dendritic electrodes grown in a solid electrolyte (GeSe) can be used to lower resistances for applications like self-healing interconnects despite its relatively low light transparency; while the dendritic electrodes grown using water-mediated method can be potentially integrated into solar cell applications, like replacing conventional Ag screen-printed top electrodes as they not only reduce resistances but also are highly transparent. This research effort also laid a solid foundation for developing dendritic plasmonic structures. A PMC-based lateral dendritic plasmonic structure is a device that has metallic dendritic networks grown electrochemically on SiO2 with a thin layer of surface metal nanoparticles in liquid electrolyte. These structures increase the distribution of particle sizes by connecting pre-deposited Ag nanoparticles into fractal structures and result in three significant effects, resonance red-shift, resonance broadening and resonance enhancement, on surface plasmon resonance for light trapping simultaneously, which can potentially enhance thin film solar cells' performance at longer wavelengths.
ContributorsRen, Minghan (Author) / Kozicki, Michael (Thesis advisor) / Schroder, Dieter (Committee member) / Roedel, Ronald (Committee member) / Barnaby, Hugh (Committee member) / Arizona State University (Publisher)
Created2011
Description
Redundant Binary (RBR) number representations have been extensively used in the past for high-throughput Digital Signal Processing (DSP) systems. Data-path components based on this number system have smaller critical path delay but larger area compared to conventional two's complement systems. This work explores the use of RBR number representation for implementing high-throughput DSP systems that are also energy-efficient. Data-path components such as adders and multipliers are evaluated with respect to critical path delay, energy and Energy-Delay Product (EDP). A new design for a RBR adder with very good EDP performance has been proposed. The corresponding RBR parallel adder has a much lower critical path delay and EDP compared to two's complement carry select and carry look-ahead adder implementations. Next, several RBR multiplier architectures are investigated and their performance compared to two's complement systems. These include two new multiplier architectures: a purely RBR multiplier where both the operands are in RBR form, and a hybrid multiplier where the multiplicand is in RBR form and the other operand is represented in conventional two's complement form. Both the RBR and hybrid designs are demonstrated to have better EDP performance compared to conventional two's complement multipliers. The hybrid multiplier is also shown to have a superior EDP performance compared to the RBR multiplier, with much lower implementation area. Analysis on the effect of bit-precision is also performed, and it is shown that the performance gain of RBR systems improves for higher bit precision. Next, in order to demonstrate the efficacy of the RBR representation at the system-level, the performance of RBR and hybrid implementations of some common DSP kernels such as Discrete Cosine Transform, edge detection using Sobel operator, complex multiplication, Lifting-based Discrete Wavelet Transform (9, 7) filter, and FIR filter, is compared with two's complement systems. It is shown that for relatively large computation modules, the RBR to two's complement conversion overhead gets amortized. In case of systems with high complexity, for iso-throughput, both the hybrid and RBR implementations are demonstrated to be superior with lower average energy consumption. For low complexity systems, the conversion overhead is significant, and overpowers the EDP performance gain obtained from the RBR computation operation.
ContributorsMahadevan, Rupa (Author) / Chakrabarti, Chaitali (Thesis advisor) / Kiaei, Sayfe (Committee member) / Cao, Yu (Committee member) / Arizona State University (Publisher)
Created2011
Description
In very small electronic devices the alternate capture and emission of carriers at an individual defect site located at the interface of Si:SiO2 of a MOSFET generates discrete switching in the device conductance referred to as a random telegraph signal (RTS) or random telegraph noise (RTN). In this research work, the integration of random defects positioned across the channel at the Si:SiO2 interface from source end to the drain end in the presence of different random dopant distributions are used to conduct Ensemble Monte-Carlo ( EMC ) based numerical simulation of key device performance metrics for 45 nm gate length MOSFET device. The two main performance parameters that affect RTS based reliability measurements are percentage change in threshold voltage and percentage change in drain current fluctuation in the saturation region. It has been observed as a result of the simulation that changes in both and values moderately decrease as the defect position is gradually moved from source end to the drain end of the channel. Precise analytical device physics based model needs to be developed to explain and assess the EMC simulation based higher VT fluctuations as experienced for trap positions at the source side. A new analytical model has been developed that simultaneously takes account of dopant number variations in the channel and depletion region underneath and carrier mobility fluctuations resulting from fluctuations in surface potential barriers. Comparisons of this new analytical model along with existing analytical models are shown to correlate with 3D EMC simulation based model for assessment of VT fluctuations percentage induced by a single interface trap. With scaling of devices beyond 32 nm node, halo doping at the source and drain are routinely incorporated to combat the threshold voltage roll-off that takes place with effective channel length reduction. As a final study on this regard, 3D EMC simulation method based computations of threshold voltage fluctuations have been performed for varying source and drain halo pocket length to illustrate the threshold voltage fluctuations related reliability problems that have been aggravated by trap positions near the source at the interface compared to conventional 45 nm MOSFET.
ContributorsAshraf, Nabil Shovon (Author) / Vasileska, Dragica (Thesis advisor) / Schroder, Dieter (Committee member) / Goodnick, Stephen (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2011
Description
As existing solar cell technologies come closer to their theoretical efficiency, new concepts that overcome the Shockley-Queisser limit and exceed 50% efficiency need to be explored. New materials systems are often investigated to achieve this, but the use of existing solar cell materials in advanced concept approaches is compelling for multiple theoretical and practical reasons. In order to include advanced concept approaches into existing materials, nanostructures are used as they alter the physical properties of these materials. To explore advanced nanostructured concepts with existing materials such as III-V alloys, silicon and/or silicon/germanium and associated alloys, fundamental aspects of using these materials in advanced concept nanostructured solar cells must be understood. Chief among these is the determination and predication of optimum electronic band structures, including effects such as strain on the band structure, and the material's opto-electronic properties. Nanostructures have a large impact on band structure and electronic properties through quantum confinement. An additional large effect is the change in band structure due to elastic strain caused by lattice mismatch between the barrier and nanostructured (usually self-assembled QDs) materials. To develop a material model for advanced concept solar cells, the band structure is calculated for single as well as vertical array of quantum dots with the realistic effects such as strain, associated with the epitaxial growth of these materials. The results show significant effect of strain in band structure. More importantly, the band diagram of a vertical array of QDs with different spacer layer thickness show significant change in band offsets, especially for heavy and light hole valence bands when the spacer layer thickness is reduced. These results, ultimately, have significance to develop a material model for advance concept solar cells that use the QD nanostructures as absorbing medium. The band structure calculations serve as the basis for multiple other calculations. Chief among these is that the model allows the design of a practical QD advanced concept solar cell, which meets key design criteria such as a negligible valence band offset between the QD/barrier materials and close to optimum band gaps, resulting in the predication of optimum material combinations.
ContributorsDahal, Som Nath (Author) / Honsberg, Christiana (Thesis advisor) / Goodnick, Stephen (Committee member) / Roedel, Ronald (Committee member) / Ponce, Fernando (Committee member) / Arizona State University (Publisher)
Created2011
Description
A primary motivation of research in photovoltaic technology is to obtain higher efficiency photovoltaic devices at reduced cost of production so that solar electricity can be cost competitive. The majority of photovoltaic technologies are based on p-n junction, with efficiency potential being much lower than the thermodynamic limits of individual technologies and thereby providing substantial scope for further improvements in efficiency. The thesis explores photovoltaic devices using new physical processes that rely on thin layers and are capable of attaining the thermodynamic limit of photovoltaic technology. Silicon heterostructure is one of the candidate technologies in which thin films induce a minority carrier collecting junction in silicon and the devices can achieve efficiency close to the thermodynamic limits of silicon technology. The thesis proposes and experimentally establishes a new theory explaining the operation of silicon heterostructure solar cells. The theory will assist in identifying the optimum properties of thin film materials for silicon heterostructure and help in design and characterization of the devices, along with aiding in developing new devices based on this technology. The efficiency potential of silicon heterostructure is constrained by the thermodynamic limit (31%) of single junction solar cell and is considerably lower than the limit of photovoltaic conversion (~ 80 %). A further improvement in photovoltaic conversion efficiency is possible by implementing a multiple quasi-fermi level system (MQFL). A MQFL allows the absorption of sub band gap photons with current being extracted at a higher band-gap, thereby allowing to overcome the efficiency limit of single junction devices. A MQFL can be realized either by thin epitaxial layers of alternating higher and lower band gap material with nearly lattice matched (quantum well) or highly lattice mismatched (quantum dot) structure. The thesis identifies the material combination for quantum well structure and calculates the absorption coefficient of a MQFl based on quantum well. GaAsSb (barrier)/InAs(dot) was identified as a candidate material for MQFL using quantum dot. The thesis explains the growth mechanism of GaAsSb and the optimization of GaAsSb and GaAs heterointerface.
ContributorsGhosha, Kuṇāla (Author) / Bowden, Stuart (Thesis advisor) / Honsberg, Christiana (Thesis advisor) / Vasileska, Dragica (Committee member) / Goodnick, Stephen (Committee member) / Arizona State University (Publisher)
Created2011
Description
In this work, we present approximate adders and multipliers to reduce data-path complexity of specialized hardware for various image processing systems. These approximate circuits have a lower area, latency and power consumption compared to their accurate counterparts and produce fairly accurate results. We build upon the work on approximate adders and multipliers presented in [23] and [24]. First, we show how choice of algorithm and parallel adder design can be used to implement 2D Discrete Cosine Transform (DCT) algorithm with good performance but low area. Our implementation of the 2D DCT has comparable PSNR performance with respect to the algorithm presented in [23] with ~35-50% reduction in area. Next, we use the approximate 2x2 multiplier presented in [24] to implement parallel approximate multipliers. We demonstrate that if some of the 2x2 multipliers in the design of the parallel multiplier are accurate, the accuracy of the multiplier improves significantly, especially when two large numbers are multiplied. We choose Gaussian FIR Filter and Fast Fourier Transform (FFT) algorithms to illustrate the efficacy of our proposed approximate multiplier. We show that application of the proposed approximate multiplier improves the PSNR performance of 32x32 FFT implementation by 4.7 dB compared to the implementation using the approximate multiplier described in [24]. We also implement a state-of-the-art image enlargement algorithm, namely Segment Adaptive Gradient Angle (SAGA) [29], in hardware. The algorithm is mapped to pipelined hardware blocks and we synthesized the design using 90 nm technology. We show that a 64x64 image can be processed in 496.48 µs when clocked at 100 MHz. The average PSNR performance of our implementation using accurate parallel adders and multipliers is 31.33 dB and that using approximate parallel adders and multipliers is 30.86 dB, when evaluated against the original image. The PSNR performance of both designs is comparable to the performance of the double precision floating point MATLAB implementation of the algorithm.
ContributorsVasudevan, Madhu (Author) / Chakrabarti, Chaitali (Thesis advisor) / Frakes, David (Committee member) / Gupta, Sandeep (Committee member) / Arizona State University (Publisher)
Created2013
Description
Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to possessing capability to perform damage detection, the ability to monitor the level of damage over time can provide extremely useful information in assessing the operational worthiness of a structure and in determining whether the structure should be repaired or removed from service. In this work, a sequential Bayesian approach with active sensing is employed for monitoring crack growth within fatigue-loaded materials. The monitoring approach is based on predicting crack damage state dynamics and modeling crack length observations. Since fatigue loading of a structural component can change while in service, an interacting multiple model technique is employed to estimate probabilities of different loading modes and incorporate this information in the crack length estimation problem. For the observation model, features are obtained from regions of high signal energy in the time-frequency plane and modeled for each crack length damage condition. Although this observation model approach exhibits high classification accuracy, the resolution characteristics can change depending upon the extent of the damage. Therefore, several different transmission waveforms and receiver sensors are considered to create multiple modes for making observations of crack damage. Resolution characteristics of the different observation modes are assessed using a predicted mean squared error criterion and observations are obtained using the predicted, optimal observation modes based on these characteristics. Calculation of the predicted mean square error metric can be computationally intensive, especially if performed in real time, and an approximation method is proposed. With this approach, the real time computational burden is decreased significantly and the number of possible observation modes can be increased. Using sensor measurements from real experiments, the overall sequential Bayesian estimation approach, with the adaptive capability of varying the state dynamics and observation modes, is demonstrated for tracking crack damage.
ContributorsHuff, Daniel W (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Kovvali, Narayan (Committee member) / Chakrabarti, Chaitali (Committee member) / Chattopadhyay, Aditi (Committee member) / Arizona State University (Publisher)
Created2013
Description
Adaptive processing and classification of electrocardiogram (ECG) signals are important in eliminating the strenuous process of manually annotating ECG recordings for clinical use. Such algorithms require robust models whose parameters can adequately describe the ECG signals. Although different dynamic statistical models describing ECG signals currently exist, they depend considerably on a priori information and user-specified model parameters. Also, ECG beat morphologies, which vary greatly across patients and disease states, cannot be uniquely characterized by a single model. In this work, sequential Bayesian based methods are used to appropriately model and adaptively select the corresponding model parameters of ECG signals. An adaptive framework based on a sequential Bayesian tracking method is proposed to adaptively select the cardiac parameters that minimize the estimation error, thus precluding the need for pre-processing. Simulations using real ECG data from the online Physionet database demonstrate the improvement in performance of the proposed algorithm in accurately estimating critical heart disease parameters. In addition, two new approaches to ECG modeling are presented using the interacting multiple model and the sequential Markov chain Monte Carlo technique with adaptive model selection. Both these methods can adaptively choose between different models for various ECG beat morphologies without requiring prior ECG information, as demonstrated by using real ECG signals. A supervised Bayesian maximum-likelihood (ML) based classifier uses the estimated model parameters to classify different types of cardiac arrhythmias. However, the non-availability of sufficient amounts of representative training data and the large inter-patient variability pose a challenge to the existing supervised learning algorithms, resulting in a poor classification performance. In addition, recently developed unsupervised learning methods require a priori knowledge on the number of diseases to cluster the ECG data, which often evolves over time. In order to address these issues, an adaptive learning ECG classification method that uses Dirichlet process Gaussian mixture models is proposed. This approach does not place any restriction on the number of disease classes, nor does it require any training data. This algorithm is adapted to be patient-specific by labeling or identifying the generated mixtures using the Bayesian ML method, assuming the availability of labeled training data.
ContributorsEdla, Shwetha Reddy (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Arizona State University (Publisher)
Created2012