This paper reports the implementation in three dimensions (3D) of diffusion models for low dose implanted dopants in silicon and the various numerical issues associated with it. In order to allow the end-users to choose between high accuracy or small calculation time, a conventional and 5-species diffusion models have been implemented in the 3D module DIFOX-3D belonging to the PROMPT plateform. By comparison with one and two-dimensional (1D and 2D) simulations performed with IMPACT-4, where calibrated models exist, the validity of this 3D models have been checked. Finally, the results obtained for a 3-dimensional simulation of a rapid thermal annealing step involved in the manufacturing of a MOS transistor are presented what show the capability of this module to handle the optimization of real devices.

Silicon self-interstitial atom diffusion and implantation induced damage were studied by using molecular dynamics methods. The diffusion coefficient of interstitial silicon was calculated using molecular dynamics method based on the Stillinger-Weber potential. A comparison was made between the calculation method based on the Einstein relationship and the method based on a hopping analysis. For interstitial silicon diffusion, atomic site exchanges to the lattice atoms occur, and thus the total displacement-based calculation underestimates the ideal value of the diffusivity of the interstitial silicon. In addition with calculating the diffusion constant, we also identified its migration pathway and barrier energy in the case of Stillinger-Weber potential. Through a study of molecular dynamics calculation for the arsenic ion implantation process, it was found that the damage self-recovering process depends on the extent of damage. That is, damage caused by a single large impact easily disappears. In contrast, the damage leaves significant defects when two large impacts in succession cause an overlapped damage region.

Pure green ZnTe light-emitting diodes (LEDs) were first realized reproducibly based on high quality ZnTe substrates and a simple thermal diffusion process. This success which overcomes the compensation effect in II-VI materials is due to the use of high quality p-type ZnTe single crystals with low dislocation densities of the level of 2000 cm-2 grown by the vertical gradient freezing (VGF) method and the suppression of as compensating point defects by low temperature annealing with covering the surface of the substrates by the deposition of n-type dopant, Al. The thermal diffusion coefficient and the activation energy of Al were determined from the pn interface observed by scanning electron spectroscopy (SEM). The formation of the intrinsic pn junctions was confirmed from the electron-beam induced current (EBIC) observation and I-V measurement. The bright 550 nm electroluminescence (EL) from these pn-junctions was reproducibly observed under room light at room temperature, with the lifetime exceeding 1000 hrs.

We propose a traffic measurement system which uses trap and polling methods. To obtain its performance we consider a queueing model with a single server and evaluate a packet delay. In our multi-cast traffic, packets are modeled as a batch with a batch size distribution {gk}. The batch arrival process is observed as two processes on the basis of batch size. For a batch whose size is more than or equal to a threshold L, the batch will be trapped by our traffic measurement system (in queueing model, it will enter a queue immediately after its arrival). For a batch whose size is less than L, it will be observed at a polling cycle T (in queueing model, it will be temporarily stored in a buffer and all these small batches will be cyclically noticed with a cycle T). We analyze this queueing model by a diffusion approximation and compare the packet delay observed by our traffic measurement system with the L=1 original batch arrival model. Evaluating the results of the diffusion approximation, we illustrate that our traffic measurement system has functions not only to give an accurate estimation of the mean waiting time but also reduce the number of measurements by choosing appropriate parameters L and T.

This paper explores a possibility of constructing massively parallel molecular computing systems using molecular electronic devices called enzyme transistors. The enzyme transistor is, in a sense, an artificial catalyst which selects a specific substrate molecule and transforms it into a specific product. Using this primitive function, various active continuous media for signal transfer/processing can be realized. Prominent examples discussed in this paper are: (i) Turing pattern formation and (ii) excitable wave propagation in a two-dimensional enzyme transistor array. This paper demonstrates the potential of enzyme transistors for creating reaction-diffusion dynamics that performs useful computations in a massively parallel fashion.

In this paper, a general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The formula is the generalized probabilistic diffusion equation based on Bayesian model, and can be implemented into some different forms corresponding to the probabilistic models in the disparity neighborhood system or configuration. The probabilistic models are independence and similarity among the neighboring disparities in the configuration. The independence probabilistic model guarantees the discontinuity at the object boundary region, and the similarity model does the continuity or the high correlation of the disparity distribution. According to the experimental results, the proposed algorithm had good estimation performance. This result showes that the derived formula generalizes the probabilistic diffusion based on Bayesian MAP algorithm for disparity estimation. Also, the proposed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to O(n()) from O(n()4) of the generalized formula.

This paper reports the evaluation results of the channel boron distribution in the deep sub-0.1 [µm] n-MOSFETs for the first time. It has been found that the boron depletion effect becomes dominant and the reverse short channel effect becomes less significant in the deep sub-0.1 [µm] n-MOSFETs. It has been also found that the sheet charge distribution responsible for the reverse short channel effect is localized within a distance of 100 [nm] from the source/drain-extension junction.

We have developed a practical 3-D integrated process simulator (3-D MIPS) based on the orthogonal grid. 3-D MIPS has a 3-D topography simulator (3-D MULSS) and 3-D impurity simulator which simulates the processes of ion implantation, impurity diffusion and oxidation. In particular, its diffusion and segregation model is new and practical. It assumes the continuity of impurity concentration at the material boundary in order to coordinate with the topography simulator (3-D MULSS) with cells in which two or more kinds of materials exist. And then, we introduced a time-step control method using the Dufort-Frankel method of diffusion analysis for stable calculation, and a selective oxidation model to apply to more general structures than LOCOS structure. After that, the 3-D MIPS diffusion model is evaluated compared with experimental data. Finally, the 3-D MIPS is applied to 3-D simulations of the nMOS Tr. structure with LOCOS isolation, wiring interconnect and pn-junction capacitances, and DRAM storage node area.

Recent years have seen great advances in our understanding and modeling of the coupled diffusion of dopants and defects in silicon during integrated circuit fabrication processes. However, the ever-progressing shrinkage of device dimensions and tolerances leads to new problems and a need for even better models. In this review, we address some of the advances in the understanding of defect-mediated diffusion, focusing on the equations and parameters appropriate for modeling of dopant diffusion in submicron structures.

The full-band Monte Carlo technique is currently the most accurate device simulation method, but its usefulness is limited because it is very CPU intensive. This work describes efficient algorithms in detail, which raise the efficiency of the full-band Monte Carlo method to a level where it becomes applicable in the device design process beyond exemplary simulations. The k-space is discretized with a nonuniform tetrahedral grid, which minimizes the discretization error of the linear energy interpolation and memory requirements. A consistent discretization of the inverse mass tensor is utilized to formulate efficient transport parameter estimators. Particle scattering is modeled in such a way that a very fast rejection technique can be used for the generation of the final state eliminating the main cause of the inefficiency of full-band Monte Carlo simulations. The developed full-band Monte Carlo simulator is highly efficient. For example, in conjunction with the nonself-consistent simulation technique CPU times of a few CPU minutes per bias point are achieved for substrate current calculations. Self-consistent calculations of the drain current of a 60nm-NMOSFET take about a few CPU hours demonstrating the feasibility of full-band Monte Carlo simulations.

This paper outlines the modeling requirements of integrated circuit (IC) fabrication processes that have lead to and sustained the development of computer-aided design of technology (i. e. TCAD). Over a period spanning more than two decades the importance of TCAD modeling and the complexity of required models has grown steadily. The paper also illustrates typical applications where TCAD has been powerful and strategic to IC scaling of processes. Finally, the future issues of atomic-scale modeling and the need for an hierarchical approach to capture and use such detailed information at higher levels of simulation are discussed.

This letter introduces a new digital halftoning technique based on error diffusion along a random space-filling curve. The purpose of introducing randomness is to erase regular patterns which tend to arise in an image area of uniform intensity. A simple algorithm for generating a random space-filling curve is proposed based on a random spanning tree and maze traversal. Some experimental results are also given.

In this paper, a scheme to remove clouds and their shadows from remotely sensed images of Landsat TM over land has been proposed. The scheme uses the image fusion technique to automatically recognize and remove contamination of clouds and their shadows, and integrate complementary information into the composite image from multitemporal images. The cloud regions can be detected on the basis of the reflectance differences with the other regions. Based on the fact that shadows smooth the brightness changes of the ground, the shadow regions can be detected successfully by means of wavelet transform. Further, an area-based detection rule is developed in this paper and the multispectral characteristics of Landsat TM images are used to alleviate the computational load. Because the wavelet transform is adopted for the image fusion, artifacts are invisible in the fused images. Finally, the performance of the proposed scheme is demonstrated experimentally.

We propose a new method of progressive transmission of continuous tone images using multi-level error diffusion method. Assuming that the pixels are ordered and the error is diffused to later pixels, multi-level error-diffused images are resolved into a multiple number of bit planes. In an image with 8 bits per pixel, the number of the bit planes that we construct is 9, and the 2-level, 3-level, 5-level,, error-diffused images are produced by a successive use of the bit planes. The original image is finally achieved precisely.

A method of visualization of multimodal images by one monochromatic image is presented on the basis of the projection pursuit approach of the inverse process of the anisotropic diffusion which is a method of image restoration enhancing contrasts at edges. The extension of the projection from a linear one to nonlinear sigmoidal functions enhances the contrast further. The deterministic annealing technique is also incorporated into the optimization process for improving the contrast enhancement ability of the projection. An application of this method to a pair of MRI images of brains reveals its promising performance of superior visualization of tissues.

This paper describes methods of tracking of moving objects in a cluttered background by integrating optical flow, depth data, and/or uniform brightness regions. First, a basic method is introduced which extracts a region with uniform optical flow as the target region. Then an extended method is described in which optical flow and depth are fused. A target region is extracted by Baysian inference in term of optical flow, depth and the predicted target location. This method works only for textured objects because optical flow or depth are extracted for textured objects. In order to solve this problem, uniform regions in addition to the optical flow are used for tracking. Realtime human tracking is realized for real image sequences by using a real time processor with multiple DSPs.

Recent progress in neural network research has demonstrated the usefulness of neural networks in a variety of areas. In this work, its application in the spatial resolution improvement of a remotely sensed low resolution thermal infrared image using high spatial resolution of visible and near-infrared images from Landsat TM sensor is described. The same work is done by an algebraic method. The tests developed are explained and examples of the results obtained in each test are shown and compared with each other. The error analysis is also carried out. Future improvements of these methods are evaluated.

Presently, mobile robots are navigated by means of a number of methods, using navigating systems such as the sonar-sensing system or the visual-sensing system. These systems each have their strengths and weaknesses. For example, although the visual system enables a rich input of data from the surrounding environment, allowing an accurate perception of the area, processing of the images invariably takes time. The sonar system, on the other hand, though quicker in response, is limited in terms of quality, accuracy and range of data. Therefore, any navigation methods that involves only any one system as the primary source for navigation, will result in the incompetency of the robot to navigate efficiently in a foreign, slightly-more-complicated-than-usual surrounding. Of course, this is not acceptable if robots are to work harmoniously with humans in a normal office/laboratory environment. Thus, to fully utilise the strengths of both the sonar and visual sensing systems, this paper proposes a fusion of navigating methods involving both the sonar and visual systems as primary sources to produce a fast, efficient and reliable obstacle-avoiding and navigating system. Furthermore, to further enhance a better perception of the surroundings and to improve the navigation capabilities of the mobile robot, active sensing modules are also included. The result is an active sensor fusion system for the collision avoiding behaviour of mobile robots. This behaviour can then be incorporated into other purposive behaviours (eg. Goal Seeking, Path Finding, etc. ). The validity of this system is also shown in real robot experiments.

Antidiffusion is a process running the diffusion equation reversely in the time domain. Though extremely important for image restoration of the Gaussian blur, it is a horribly ill-posed problem, since minor noise leads to very erroneous results. To solve this ill-posed problem stably, in this paper we first apply a multiscale method to decompose images into various scale components using the Gaussian and Laplacian of Gaussian (LOG) filters. We then show that the restored images can be reconstructed from the components using shrunk Gaussian and LOG filters. Our algorithm has a closed form solution, and is robust to noise because it is performed by the integration computation (convolution), contrasting with the differential computation required by direct discretization of the antidiffusion equation. The antidiffusion algorithm is also computationally efficient since the convolution is row and column separable. Finally, a comparison between the algorithm and the well-known Wiener filter is conducted. Experiments show that our algorithm is really stable and images can be restored satisfactorily.

Materials for a new reinforcement method using an internal heating technique have been developed experimentally for fusion splices. The method employs a protective package of a carbon-fiber composite and a hot-melt adhesive in a heat-shrinkable tube. The most appropriate heating current and heating time were determined from a consideration of the decomposition temperature of the adhesive (300) and the complete shrinking temperature (115) and the minimum welding temperature of Nylon 12 (about 180). The protective package can be installed in less than 30 seconds at a power of 10 W. Air bubbles which might cause microbending were completely eliminated by using Nylon 12 as the hot-melt adhesive, irradiated polyethylene as the heat-shrinkable tube and a carbon-fiber-composite electrical heating rod which also acted a tension member. The key for preparing the carbon-fiber composite was to remove its impurities. Under the condition of temperature difference larger than 40 deg. between the shrinking temperature of the heat-shrinkable tube and the melting temperature of the hot-melt adhesive. Nylon 12 and irradiated polyethylene were needed for the complete elimination of residual bubbles. By using Nylon 12 as the hot-melt adhesive, a reliable protective package could be achieved for a fusion spliced optical fiber with a low excess loss of less than 0.06 dB/splice between -60 and +70 and a high tensile strength of 3.9 kg.