Conformality of Al2O3 deposited by thermal, plasma-enhanced and ozone-based atomic layer deposition

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A multicriteria analysis of stated preferences among freight transport alternatives

Stated preferences data can be of different types: choice data, rankings or ratings. In all cases, these data can be used in different ways as inputs of econometric discrete choice models. This allows to estimate the weights of the different attributes characterizing an alternative. For freight transport, an alternative's attributes would be, for example, reliability, safety, frequency, etc., besides time and cost. Depending on the data sample, number of alternatives and number of attributes, it is possible to proceed to an analysis of individual data or of aggregated data. In case one is interested to analyze individual behaviors in depth, the option exists to rely on some kind of multicriteria analysis for deriving individual utility functions (actually, decision functions) rather than on a classic discrete choice model. Such a procedure also can be useful for deriving individual utilities as input in a hybrid model combining individual utilities with group data. Such a multicriteria approach is envisaged in the context of a stated preference experiment that is currently applied to freight shippers in Belgium. The data in this case are rankings of alternatives, and there is multicritera method that is particularly well adapted for such data: the UTA models developed by Jacquet-Lagrèze and Siskos. It is based on the specification of an additive utility made of non-linear partial utility functions that are piecewise linear. This allows the convenient set-up of a linear goal programming problem which estimates all the functions and their weights. The paper intends to present the ranking experiment, and to use some of the preliminary interviews to illustrate this UTA methodology. Also, it will be shown how it can be used to derive equivalent money values for each attributes on the basis of the cost attribute, and how to distinguish valuations in terms of willingness to pay and willingness to accept a compensation.

Modeling the conformality of atomic layer deposition: the effect of sticking probability

The key advantage of atomic layer deposition (ALD) is undoubtedly the excellent step coverage, which allows for conformal deposition of thin films in high-aspect-ratio structures. In this paper, a model is proposed to predict the deposited film thickness as a function of depth inside a hole. The main model parameters are the gas pressure, the deposition temperature, and the initial sticking probability of the precursor molecules. Earlier work by Gordon et al. assumed a sticking probability of 0/100% for molecules hitting a covered/uncovered section of the wall of the hole, thus resulting in a stepwise film-thickness profile. In this work, the sticking probability is related to the surface coverage theta by Langmuir’s equation s(theta) = s0(1−theta), whereby the initial sticking probability s0 is now an adjustable model parameter. For s0~=100%, the model predicts a steplike profile, in agreement with Gordon et al., while for smaller values of s0, a gradual decreasing coverage profile is predicted. Furthermore, experiments were performed to quantify the conformality for the trimethylaluminum (TMA)/H2O ALD process using macroscopic test structures. It is shown that the experimental data and the simulation results follow the same trends

Decision diagrams in machine learning: an empirical study on real-life credit-risk data.

Decision trees are a widely used knowledge representation in machine learning. However, one of their main drawbacks is the inherent replication of isomorphic subtrees, as a result of which the produced classifiers might become too large to be comprehensible by the human experts that have to validate them. Alternatively, decision diagrams, a generalization of decision trees taking on the form of a rooted, acyclic digraph instead of a tree, have occasionally been suggested as a potentially more compact representation. Their application in machine learning has nonetheless been criticized, because the theoretical size advantages of subgraph sharing did not always directly materialize in the relatively scarce reported experiments on real-world data. Therefore, in this paper, starting from a series of rule sets extracted from three real-life credit-scoring data sets, we will empirically assess to what extent decision diagrams are able to provide a compact visual description. Furthermore, we will investigate the practical impact of finding a good attribute ordering on the achieved size savings.Advantages; Classifiers; Credit scoring; Data; Decision; Decision diagrams; Decision trees; Empirical study; Knowledge; Learning; Real life; Representation; Size; Studies;

A tool-supported approach to inter-tabular verification.

The use of decision tables to verify KBS has been advocated several times in the V&V literature. However, one of the main drawbacks of those system is that they fail to detect anomalies which occur over rule chains. In a decision table based context this means that anomalies which occur due to interactions between tables are neglected. These anomalies are called inter-tabular anomalies. In this paper we investigate an approach that deals with inter-tabular anomalies. One of the prerequisites for the approach was that it could be used by the knowledge engineer during the development of the KBS. This requires that the anomaly check can be performed on-line. As a result, the approach partly uses heuristics where exhaustive checks would be too inefficient. All detection facilities that will be described, have been implemented in a table-based development tool called PROLOGA. The use of this tool will be briefly illustrated. In addition, some experiences in verifying large knowledge bases are discussed.

Du gène à la fleur

National audienceAu-delà de l'expérimentation in vitro, l'expérimentation sur ordinateur devrait permettre de mieux comprendre les conditions de croissance des plantes