Profile simulations of gas chopping etching processes : model development and comparison with experiments

The progress in microsystem technology or nano technology places extended requirements to the fabrication processes. The trend is moving towards structuring within the nanometer scale on the one hand, and towards fabrication of structures with high aspect ratio (ratio of vertical vs. lateral dimensions) and large depths in the 100 µm scale on the other hand. Current procedures for the microstructuring of silicon are wet chemical etching and dry or plasma etching. A modern plasma etching technique for the structuring of silicon is the so-called "gas chopping" etching technique (also called "time-multiplexed etching"). In this etching technique, passivation cycles, which prevent lateral underetching of sidewalls, and etching cycles, which etch preferably in the vertical direction because of the sidewall passivation, are constantly alternated during the complete etching process. To do this, a CHF3/CH4 plasma, which generates CF monomeres is employed during the passivation cycle, and a SF6/Ar, which generates fluorine radicals and ions plasma is employed during the etching cycle. Depending on the requirements on the etched profile, the durations of the individual passivation and etching cycles are in the range of a few seconds up to several minutes. The profiles achieved with this etching process crucially depend on the flow of reactants, i.e. CF monomeres during the passivation cycle, and ions and fluorine radicals during the etching cycle, to the bottom of the profile, especially for profiles with high aspect ratio. With regard to the predictability of the etching processes, knowledge of the fundamental effects taking place during a gas chopping etching process, and their impact onto the resulting profile is required. For this purpose in the context of this work, a model for the description of the profile evolution of such etching processes is proposed, which considers the reactions (etching or deposition) at the sample surface on a phenomenological basis. Furthermore, the reactant transport inside the etching trench is modelled, based on angular distribution functions and on absorption probabilities at the sidewalls and bottom of the trench. A comparison of the simulated profiles with corresponding experimental profiles reveals that the proposed model reproduces the experimental profiles, if the angular distribution functions and absorption probabilities employed in the model is in agreement with data found in the literature. Therefor the model developed in the context of this work is an adequate description of the effects taking place during a gas chopping plasma etching process.

Model Calculations and Implementation of Filters and Hybrid Green VCSELs based on Optical Thin Film Stacks

This work introduced the novel conception of complex coupled hybrid VCSELs for the first time. Alternating organic and inorganic layers in the lasers provide periodic variation of refractive index and optical gain, which enable single mode operation and low threshold of the VCSELs. Model calculations revealed great reduction of the lasing threshold with factors over 30, in comparison with the existing micro-cavity lasers. Tunable green VCSEL has been also designed, implemented and analyzed taking advantage of the broad photoluminescence spectra of the organics. Free standing optical thin films without compressive stress are technologically implemented. Multiple membrane stacks with air gap in between have been fabricated for the implementation of complex coupled VCSEL structures. Complex coupled hybrid VCSEL is a very promising approach to fill the gaps in the green spectral range of the semiconductor lasers.

Model-Based Troubleshooting of Digital Systems

This thesis describes a methodology, a representation, and an implemented program for troubleshooting digital circuit boards at roughly the level of expertise one might expect in a human novice. Existing methods for model-based troubleshooting have not scaled up to deal with complex circuits, in part because traditional circuit models do not explicitly represent aspects of the device that troubleshooters would consider important. For complex devices the model of the target device should be constructed with the goal of troubleshooting explicitly in mind. Given that methodology, the principal contributions of the thesis are ways of representing complex circuits to help make troubleshooting feasible. Temporally coarse behavior descriptions are a particularly powerful simplification. Instantiating this idea for the circuit domain produces a vocabulary for describing digital signals. The vocabulary has a level of temporal detail sufficient to make useful predictions abut the response of the circuit while it remains coarse enough to make those predictions computationally tractable. Other contributions are principles for using these representations. Although not embodied in a program, these principles are sufficiently concrete that models can be constructed manually from existing circuit descriptions such as schematics, part specifications, and state diagrams. One such principle is that if there are components with particularly likely failure modes or failure modes in which their behavior is drastically simplified, this knowledge should be incorporated into the model. Further contributions include the solution of technical problems resulting from the use of explicit temporal representations and design descriptions with tangled hierarchies.

A Statistical Image-Based Shape Model for Visual Hull Reconstruction and 3D Structure Inference

We present a statistical image-based shape + structure model for Bayesian visual hull reconstruction and 3D structure inference. The 3D shape of a class of objects is represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes are then estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We show how the use of a class-specific prior in a visual hull reconstruction can reduce the effect of segmentation errors from the silhouette extraction process. The proposed method is applied to a data set of pedestrian images, and improvements in the approximate 3D models under various noise conditions are shown. We further augment the shape model to incorporate structural features of interest; unknown structural parameters for a novel set of contours are then inferred via the Bayesian reconstruction process. Model matching and parameter inference are done entirely in the image domain and require no explicit 3D construction. Our shape model enables accurate estimation of structure despite segmentation errors or missing views in the input silhouettes, and works even with only a single input view. Using a data set of thousands of pedestrian images generated from a synthetic model, we can accurately infer the 3D locations of 19 joints on the body based on observed silhouette contours from real images.

Model-Based Matching by Linear Combinations of Prototypes

We describe a method for modeling object classes (such as faces) using 2D example images and an algorithm for matching a model to a novel image. The object class models are "learned'' from example images that we call prototypes. In addition to the images, the pixelwise correspondences between a reference prototype and each of the other prototypes must also be provided. Thus a model consists of a linear combination of prototypical shapes and textures. A stochastic gradient descent algorithm is used to match a model to a novel image by minimizing the error between the model and the novel image. Example models are shown as well as example matches to novel images. The robustness of the matching algorithm is also evaluated. The technique can be used for a number of applications including the computation of correspondence between novel images of a certain known class, object recognition, image synthesis and image compression.

Model-Based Matching of Line Drawings by Linear Combinations of Prototypes

We describe a technique for finding pixelwise correspondences between two images by using models of objects of the same class to guide the search. The object models are 'learned' from example images (also called prototypes) of an object class. The models consist of a linear combination ofsprototypes. The flow fields giving pixelwise correspondences between a base prototype and each of the other prototypes must be given. A novel image of an object of the same class is matched to a model by minimizing an error between the novel image and the current guess for the closest modelsimage. Currently, the algorithm applies to line drawings of objects. An extension to real grey level images is discussed.

The Individual is Nothing, the Class Everything: Psychophysics and Modeling of Recognition in Obect Classes

Most psychophysical studies of object recognition have focussed on the recognition and representation of individual objects subjects had previously explicitely been trained on. Correspondingly, modeling studies have often employed a 'grandmother'-type representation where the objects to be recognized were represented by individual units. However, objects in the natural world are commonly members of a class containing a number of visually similar objects, such as faces, for which physiology studies have provided support for a representation based on a sparse population code, which permits generalization from the learned exemplars to novel objects of that class. In this paper, we present results from psychophysical and modeling studies intended to investigate object recognition in natural ('continuous') object classes. In two experiments, subjects were trained to perform subordinate level discrimination in a continuous object class - images of computer-rendered cars - created using a 3D morphing system. By comparing the recognition performance of trained and untrained subjects we could estimate the effects of viewpoint-specific training and infer properties of the object class-specific representation learned as a result of training. We then compared the experimental findings to simulations, building on our recently presented HMAX model of object recognition in cortex, to investigate the computational properties of a population-based object class representation as outlined above. We find experimental evidence, supported by modeling results, that training builds a viewpoint- and class-specific representation that supplements a pre-existing repre-sentation with lower shape discriminability but possibly greater viewpoint invariance.

Learning-Based Approach to Estimation of Morphable Model Parameters

We describe the key role played by partial evaluation in the Supercomputing Toolkit, a parallel computing system for scientific applications that effectively exploits the vast amount of parallelism exposed by partial evaluation. The Supercomputing Toolkit parallel processor and its associated partial evaluation-based compiler have been used extensively by scientists at MIT, and have made possible recent results in astrophysics showing that the motion of the planets in our solar system is chaotically unstable.

Uncertainty Propagation in Model-Based Recognition

Building robust recognition systems requires a careful understanding of the effects of error in sensed features. Error in these image features results in a region of uncertainty in the possible image location of each additional model feature. We present an accurate, analytic approximation for this uncertainty region when model poses are based on matching three image and model points, for both Gaussian and bounded error in the detection of image points, and for both scaled-orthographic and perspective projection models. This result applies to objects that are fully three- dimensional, where past results considered only two-dimensional objects. Further, we introduce a linear programming algorithm to compute the uncertainty region when poses are based on any number of initial matches. Finally, we use these results to extend, from two-dimensional to three- dimensional objects, robust implementations of alignmentt interpretation- tree search, and ransformation clustering.

An alternative method for dating unknown tephras based on a segmented regression model

In CoDaWork’05, we presented an application of discriminant function analysis (DFA) to 4 different compositional datasets and modelled the first canonical variable using a segmented regression model solely based on an observation about the scatter plots. In this paper, multiple linear regressions are applied to different datasets to confirm the validity of our proposed model. In addition to dating the unknown tephras by calibration as discussed previously, another method of mapping the unknown tephras into samples of the reference set or missing samples in between consecutive reference samples is proposed. The application of these methodologies is demonstrated with both simulated and real datasets. This new proposed methodology provides an alternative, more acceptable approach for geologists as their focus is on mapping the unknown tephra with relevant eruptive events rather than estimating the age of unknown tephra. Kew words: Tephrochronology; Segmented regression

Job demands, job resources and individual innovation at work : Going beyond Karasek's model?

Resumen tomado de la publicaci??n. Resumen tambi??n en ingl??s

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

A prediction model for establishing the disability degree in adults with spinal cord injury: results based on WHO-DAS II perspective

Objective: To establish a prediction model of the degree of disability in adults with Spinal CordInjury (SCI ) based on the use of the WHO-DAS II . Methods: The disability degree was correlatedwith three variable groups: clinical, sociodemographic and those related with rehabilitation services.A model of multiple linear regression was built to predict disability. 45 people with sci exhibitingdiverse etiology, neurological level and completeness participated. Patients were older than 18 andthey had more than a six-month post-injury. The WHO-DAS II and the ASIA impairment scale(AIS ) were used. Results: Variables that evidenced a significant relationship with disability were thefollowing: occupational situation, type of affiliation to the public health care system, injury evolutiontime, neurological level, partial preservation zone, ais motor and sensory scores and number ofclinical complications during the last year. Complications significantly associated to disability werejoint pain, urinary infections, intestinal problems and autonomic disreflexia. None of the variablesrelated to rehabilitation services showed significant association with disability. The disability degreeexhibited significant differences in favor of the groups that received the following services: assistivedevices supply and vocational, job or educational counseling. Conclusions: The best predictiondisability model in adults with sci with more than six months post-injury was built with variablesof injury evolution time, AIS sensory score and injury-related unemployment.