We report on measured Hall mobility versus temperature for high‐quality modulation‐doped AlGaAs/GaAs samples after exposure by electrons and x rays at doses and energies typically used in lithography. We find that b...
We report on measured Hall mobility versus temperature for high‐quality modulation‐doped AlGaAs/GaAs samples after exposure by electrons and x rays at doses and energies typically used in lithography. We find that bare samples exposed by 50 keV electrons suffered significant mobility degradation over the temperature range of 4.2–300 K (as much as a factor of 30). X‐ray‐exposed samples did not show any mobility degradation. Two‐dimensional electron densities were not dramatically affected by either exposure technique, although e‐beam exposed samples did show a slight decrease in carrier density. Our results are consistent with previous reports of mobility degradation in some e‐beam evaporators.
We report on the fabrication of AlGaAs/GaAs split‐gate electron waveguide devices of lengths between 0.1 and 2 μm using x‐ray lithography, and the measurements of these devices at liquid‐helium temperatures and up...
We report on the fabrication of AlGaAs/GaAs split‐gate electron waveguide devices of lengths between 0.1 and 2 μm using x‐ray lithography, and the measurements of these devices at liquid‐helium temperatures and up to 15 K. An x‐ray mask (parent mask) was fabricated using e‐beam lithography and replicated using proximity x‐ray lithography (λ=1.32 nm) to generate a replica (daughter) mask. The daughter mask was then aligned to patterns on a high‐mobility AlGaAs/GaAs sample and x ray exposed using a conformable mask fixture. The conductance of the electron waveguides was measured as a function of the split‐gate bias. Sharp 2e2/h conductance steps were observed in devices up to 0.75 μm long at T=2 K. The features in the conductance remain visible up to 15 K.
We report on the fabrication of quasi‐one‐dimensional wires on modulation‐doped GaAs/AlGaAs using a novel conformable x‐ray mask technology which allows us to expose arbitrary sized samples, including samples much...
We report on the fabrication of quasi‐one‐dimensional wires on modulation‐doped GaAs/AlGaAs using a novel conformable x‐ray mask technology which allows us to expose arbitrary sized samples, including samples much smaller than the membrane area, using our laboratory’s standard 31 mm‐diam silicon‐nitride x‐ray mask. After optical alignment, the sample and mask are brought into contact electrically, and then loaded into a specially designed cartridge which allows a vacuum to be pulled between mask and substrate. The vacuum causes the x‐ray mask to conform around the sample. We find that a vacuum hold down is necessary to allow easy separation of the sample from the mask with minimal risk to both.
This paper describes a method for reducing the information contained in an image sequence, while retaining the information necessary for the interpretation of the sequence by a human observer. The method consists of f...
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This paper describes a method for reducing the information contained in an image sequence, while retaining the information necessary for the interpretation of the sequence by a human observer. The method consists of first locating the redundant information, reducing the degree of redundancy, and coding the result. The sequence is treated as a single 3-D data volume, the voxels of which are grouped into several regions, obtained by a 3-D split and merge algorithm. To find these regions, we first obtain an initial region space by splitting the image sequence until the gray-level variation over each region can be approximated by a 3-D polynomial, to a specified accuracy. This results in a set of parallelepipedic regions of various sizes. To represent the gray-level variation over these regions, the coefficients of the approximating polynomial are used as features. The most similar regions are then merged, using a region adjacency graph. The information is coded by representing the borders of the regions using a pyramidal structure in the x, y, t space. The coefficients of the approximating polynomials are coded in a straightforward manner. For 256 x 256 pixel, 25 frames/s image sequences, compressions allowing transmission rates near 64 kbit/s are obtained.
The medial axis transform (MAT) is a sparse representation of shape, which, being reversible, has potential for binary image compression. The MAT also provides structural information not accessible with alternative bi...
The problem of defining an appropriate measure of the degree of nonstationarity for stochastic processes that exhibit cyclostationarity is addressed. After discussing several candidate measures of degree of nonstation...
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The problem of defining an appropriate measure of the degree of nonstationarity for stochastic processes that exhibit cyclostationarity is addressed. After discussing several candidate measures of degree of nonstationarity, one particularly promising measure is adopted. By decomposing this measure, several component measures are arrived at. Bounds on these measures are derived and their utility in applications involving signal detection and estimation is established. Examples are presented to illustrate the calculation of degrees of nonstationarity for several types of cyclostationary signals.
Based on the assumption that most probability densities in real life can be approximated by a mixture of Gaussian densities, we propose here a three-layer adaptive network with each neuron in the lower hidden layer re...
Based on the assumption that most probability densities in real life can be approximated by a mixture of Gaussian densities, we propose here a three-layer adaptive network with each neuron in the lower hidden layer representing a Gaussian basis function (covariance matrix equal to where I is a unit matrix) to estimate various probability densities and serve as a Bayes classifier. The width of the basis function may be the same for all neurons in this layer or it may vary from one neuron to another. This paper investigates the effectiveness of the network for both cases and presents a localized learning algorithm to adjust the network parameters. The network was trained with artificial data derived from known mixtures of memoryless Gaussian sources as well as exponential and Gamma densities. The performance of the network as a pattern density estimator was measured in terms of the relative difference between the target probability density function (p.d.f.) which generates the training and testing data and the network output representing the estimation. Samples from two mixtures corresponding to two classes were used to test the network capability as a classifier by comparing its error rate against that of a Bayes classifier. Both one- and two-dimensional cases were explored. The successfulness of the network depended on how well the target p.d.f.’s were represented by the training samples, the number of hidden neurons employed in the network and how thoroughly the network was trained. It was also found that allowing each basis function to have an independent width had a predominant effect on the network performance.
A systematic method for automatic custom layout of analog integrated circuits is presented. This method uses analog circuit recognition and critical net analysis techniques to derive proper layout constraints for anal...
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A systematic method for automatic custom layout of analog integrated circuits is presented. This method uses analog circuit recognition and critical net analysis techniques to derive proper layout constraints for analog circuit performance optimization. Constraint-driven analog floorplanning and routing techniques are developed to generate custom layouts which incorporate the layout constraints. This method can be applied to handle a wide variety of analog circuit modules as well as analog subsystems. Experimental results on CMOS operational amplifiers and a comparator are presented.< >
The authors develop a path metric for sequential search based on the linear model. The metric forms the heart of an edge-linking algorithm that combines edge elements enhanced by an optimal filter. From a starting nod...
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The authors develop a path metric for sequential search based on the linear model. The metric forms the heart of an edge-linking algorithm that combines edge elements enhanced by an optimal filter. From a starting node, transitions are made to the goal nodes by a maximum likelihood metric. This metric requires only local calculations on the search space and its use in edge linking provides more accurate results than other linking techniques.< >
A locally connected multi-layer stochastic neural network and its associated VLSI array neuroprocessors have been developed for high-performance image flow computing systems. An extendable VLSI neural chip has been de...
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A locally connected multi-layer stochastic neural network and its associated VLSI array neuroprocessors have been developed for high-performance image flow computing systems. An extendable VLSI neural chip has been designed with a silicon area of 4.6*6.8 mm/sup 2/ in a MOSIS 2 mu m scalable CMOS process. The mixed analog-digital design techniques are utilized to achieve compact and programmable synapses with gain-adjustable neurons and winner-take-all cells for massively parallel neural computation. Hardware annealing through the control of the neurons' gain helps to efficiently search the optimal solutions. Computing of image flow using one 2 mu m 72-neuron neural chip can be accelerated by a factor of 187 more than a Sun-4/260 workstation. Real-time image flow processing on industrial images is practical using an extended array of VLSI neural chips. Actual examples on moving trucks are presented.< >
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