We have quantitatively modeled the conduction current and charge storage of an HFET in terms its physical
dimensions and material properties. For DC or small-signal RF operation, no adjustable parameters are necessary to
predict the terminal characteristics of the device. Linear performance measures such as small-signal gain and input
admittance can be predicted directly from the geometric structure and material properties assumed for the device design.
We have validated our model at low-frequency against experimental I-V measurements and against two-dimensional
device simulations. We discuss our recent extension of our model to include a larger class of electron velocity-field
curves. We also discuss the recent reformulation of our model to facilitate its implementation in commercial large-signal
high-frequency circuit simulators.
Large signal RF operation is more complex. First, the highest CW microwave power is fundamentally bounded
by a brief, reversible channel breakdown in each RF cycle. Second, the highest experimental measurements of efficiency,
power, or linearity always require harmonic load pull and possibly also harmonic source pull. Presently, our model
accounts for these facts with an adjustable breakdown voltage and with adjustable load impedances and source
impedances for the fundamental frequency and its harmonics. This has allowed us to validate our model for large signal
RF conditions by simultaneously fitting experimental measurements of output power, gain, and power added efficiency
of real devices.
We show that the resulting model can be used to compare alternative device designs in terms of their large
signal performance, such as their output power at 1dB gain compression or their third order intercept points. In addition,
the model provides insight into new device physics features enabled by the unprecedented current and voltage levels of
AlGaN/GaN HFETs, including non-ohmic resistance in the source access regions and partial depletion of the 2DEG in
the drain access region.
KEYWORDS: Reliability, Gallium arsenide, Data modeling, Electrodes, Semiconductors, Field effect transistors, Amplifiers, Transistors, Device simulation, Ka band
High voltage HFET's fabricated from nitride semiconductors utilizing the AlGaN/GaN heterojunction or
GaAs using field plates demonstrate excellent RF output power performance. The nitride HFET's produce
RF output power greater than an order of magnitude higher than available from GaAs and InP based
devices, and GaAs FET's fabricated with field-plates can produce RF output power about a factor of two
greater than standard FET's. However, the FET's demonstrate a reliability problem where the dc current
and RF output power continually decrease as a function of time. The problem is more serious in the nitride
HFET's, although both nitride-based and GaAs-based devices suffer reliability problems. The reliability
problem is related to the conduction characteristics of the gate electrode and an electron tunneling
mechanism where electrons leak from the gate to the surface of the semiconductor. In this work the physics
responsible for this behavior are investigated and described. Physics-based models suitable for use in RF
circuit harmonic-balance simulators have been developed, with excellent agreement between measured and
simulated data. Design techniques to reduce the reliability problem will be discussed.
The problems of high resolution image reconstruction are approached in this project as an optimization problem. Assuming an ideal image is blurred, noise corrupted, and sub-sampled to produce the measured image, we pose the estimation of the enlarged image as a maximum-a-posteriori (MAP) restoration process and the mean field annealing optimization technique is used to solve the multi-model objective function. The iterative interpolation process incorporates two terms into its objective function. The first term is the 'noise' term which models the burring and subsampling of the acquisition system. By using the system point spread function and the noise characteristics, the measured pixels at the sub-sampled-grid are mapped into the grid of the original image. A second term, the a-priori term is formulated to fore the prior constraints such as noise smoothing and edge preserving into the interpolation process. The resulted image is a noise reduced, deblurred, and enlarged image. The proposed algorithm are used to zoom several medical images, along with existing techniques such as pixel replication, linear interpolation, and spectrum extrapolation. The resulted images indicate that the proposed algorithm can smooth noise extensively while keeping the image features. The images zoomed by other methods suffer from noise and look less favorable in comparison.
The filtered-backprojection (FBP) algorithm and statistical model based iterative algorithms such as the maximum likelihood (ML) reconstruction are the two major classes of tomographic reconstruction method. The FBP method is widely used in clinical setting while iterative methods have attracted research interests in the past decade. In this paper we study the performance of the FBP and the ML methods using simulated projection data. The results indicate that the best image that the FBP or the ML algorithm can generate is the compromise of image smoothness and sharpness. The filter cutoff frequency for the FBP algorithm or the number of iterations for the ML algorithm has to be selected carefully.
This work addresses an optimization approach to sensor fusion and applies the technique to magnetic resonance image (MRI) restoration. Several images are related using a physical model (spin equation) to corresponding basis images. The basis images (proton density and two nuclear relaxation times) are determined from the MRI data and subsequently used to obtain excellent restorations. The method also has been applied to image restoration problems in other domains. All images are modeled as Markov random fields (MRF). Four maximum a posteriori (MAP) restorations are presented. The `product' and `sum' forms for basis (signal) and spatial correlations are discussed, compared, and evaluated for various situations and features. A novel method of global optimization necessary for the nonlinear techniques is also introduced. This approach to sensor fusion, using global optimization, MRF models, and Bayesian techniques, has been generalized and applied to other problem domains, such as the restoration of multiple-modality laser range and luminance signals.
Systems that select an optimal or nearly optimal member from a specified search set are reviewed with special emphasis on stochastic approaches such as simulated annealing, genetic algorithms, as well as other probabilistic heuristics. Because of local minima, selecting a global optimum may require time that increases exponentially in the problem size. Stochastic search provides advantages in robustness, generality, and simplicity over other approaches and is more efficient than exhaustive deterministic search.
A new stochastic optimization algorithm is introduced in which a pipeline of many biased stochastic procedures cooperate to concurrently sample the usual Boltzmann distribution for different temperatures. Convergence and efficiency of the pipeline algorithm is proved under certain conditions. Experimental confirmation is provided using seven standard test problems in nonlinear optimization.
Our intent is to obtain images which most clearly differentiate soft tissue types in Magnetic Resonance Image data. We model the three unknown intrinsic parameter images and the data images as Markov random fields and compare maximum likelihood restorations with two maximum a posteriori (MAP) restorations. The mathematical model of the imaging process is strongly nonlinear in the region of interest, but does not appear to introduce local minima in the resulting constrained multidimensional optimization procedure. The application of non- quadratic prior probabilities however does require global optimization. We have developed a unique approach towards image restoration that produces images with significant improvements when compared to the original data. We have extended previous results that attempt to determine the intrinsic parameters from the MRI data, and have used these intrinsic parameter images to synthesize MR images. MR images with different TE and TR parameters do not require additional use of an MR scanner, since excellent synthetic MR images are obtained using the restored proton density and nuclear relaxation time images.
In this paper a new algorithm to estimate dense displacement fields from a sequence of images is developed. The algorithm is based on modeling the displacement fields as Markov Random fields. The Markov Random fields-Gibbs equivalence is then used to convert the problem into one of finding an appropriate energy function that describes the motion and any constraints imposed on it. Mean field annealing, a technique which finds global minima in nonconvex optimization problems, is used to minimize the energy function, and solve for the optimum displacement fields. The algorithm results in accurate estimates even for scenes with noise or discontinuities.
We define genetic annealing as simulated annealing applied to a population of several solutions when candidates are generated from more than one (parent) solution at a time. We show that such genetic annealing algorithms can inherit the convergence properties of simulated annealing. We present two examples, one that generates each candidate by crossing pairs of parents and a second that generates each candidate from the entire population. We experimentally apply these two extreme versions of genetic annealing to a problem in vector quantization.
Stochastic simulated annealing (SSA) is a popular method for solving optimization functions in which the objective function has multiple minima. Not only can SSA find minima, but it has been proven to converge (under certain conditions) to the global minimum. The principal drawback to SSA has been its convergence rate. In order to preserve the conditions of the convergence proof, the algorithm must be run so slowly as to be impractical for many applications. In this paper, an extension to SSA is described which allows the user to provide additional a priori information to the algorithm which may allow much more rapid convergence. The new method, called `compensated simulated annealing' (CSA) is also guaranteed to converge. A problem of finding a minimum path through a recurrent multilayer graph is described. Then a practical motivating application from medical imaging is presented. The graph structure is used to model the boundary of an artery in an intra-arterial ultrasound image. The optimization problem is posed and solved by SSA and CSA as a means of comparing the two methods. The CSA approach is shown to converge significantly faster than SSA.
KEYWORDS: Stochastic processes, Algorithms, Image processing, Image restoration, Annealing, Signal processing, Machine vision, Computer vision technology, Process modeling, Signal to noise ratio
A new stochastic technique is described for the Bayesian restoration of gray-level images corrupted by white noise. The proposed technique is related to simulated annealing but generates candidates more efficiently for gray-level images than either the Gibbs sampler or the Metropolis procedure. For a logarithmic cooling schedule, asymptotic convergence of the algorithm is proved by analyzing the corresponding inhomogeneous Markov chain. For an exponential cooling schedule, the new technique is shown experimentally to restore floating point images in 1/50 of the time required for the usual simulated annealing. Experimental restorations of gray-level images corrupted by white noise are presented.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.