Control of Large-size SiC Growth Process Using Modeling tool

Large size bulk silicon carbide (SiC) crystals are commonly grown by the physical vapor transport (PVT) method. The PVT growth of SiC crystals involves sublimation and condensation, chemical reactions, stoichiometry, mass transport, induced thermal stress, as well as defect and micropipes generation and propagation. The quality and polytype of as-grown SiC crystals are related to the temperature distribution inside the growth chamber during the growth process, it is critical to predict the temperature distribution from the measured temperatures outside the crucible by pyrometers. A radio-frequency induction-heating furnace was used for the growth of large-size SiC crystals by the PVT method in the present study. Modeling and simulation have been used to develop the SiC growth process and to improve the SiC crystal quality. Parameters such as the temperature measured at the top of crucible, temperature measured at the bottom of the crucible, and inert gas pressure are used to control the SiC growth process. By measuring the temperatures at the top and bottom of the crucible, the temperatures inside the crucible were predicted with the help of modeling tool. SiC crystals of 6H polytype were obtained and characterized by the Raman scattering spectroscopy and SEM, and crystals of few millimeter size grown inside the crucible were found without micropipes. Expansion of the crystals were also performed with the help of modeling and simulation.

Growth of Silicon Carbide Bulk Crystals by Physical Vapor Transport Method and Modeling Efforts in the Process Optimization

Silicon carbide bulk crystals were grown in an induction-heating furnace using the physical vapor transport method. Crystal growth modeling was performed to obtain the required inert gas pressure and temperatures for sufficiently large growth rates. The SiC crystals were expanded by designing a growth chamber having a positive temperature gradient along the growth interface. The obtained 6H-SiC crystals were cut into wafers and characterized by Raman scattering spectroscopy and X-ray diffraction, and the results showed that most parts of the crystals had good crystallographic structures.

Valorization of scrap tires pyrolysis oil (STPO) through a 2-stage hydrotreating-hydrocracking strategy. Process variables and kinetic modeling

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Gaussian mixture modeling with Gaussian process latent variable models

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets. © 2010 Springer-Verlag.

The process of making the business case for technology: A sales and marketing perspective for technologists

Technological investment is a key driver of innovation and the evaluation of technology potential is becoming increasingly important in this context. There is a range of approaches and tools for developing an understanding of the value of technology. However the process of communicating this potential to possible customers is not well documented in terms of theory and practice and falls outside the skill set of many technologists. This paper seeks to integrate the concepts of marketing and consultative selling into making business cases for new technologies. It describes an exploratory study which results in an outline process activity model for technologists wishing to build an effective business case for securing investment internally or when selling a technology externally. Following a review of literature, we suggest that there is potential to learn from market research and consultative sales techniques, and propose a five step process. The work has been industrially validated and forms a novel foundation for further development. © 2012 Elsevier Inc.

Modeling and optimization of semiconductor manufacturing process with neural networks

A neural network-based process model is proposed to optimize the semiconductor manufacturing process. Being different from some works in several research groups which developed neural network-based models to predict process quality with a set of process variables of only single manufacturing step, we applied this model to wafer fabrication parameters control and wafer lot yield optimization. The original data are collected from a wafer fabrication line, including technological parameters and wafer test results. The wafer lot yield is taken as the optimization target. Learning from historical technological records and wafer test results, the model can predict the wafer yield. To eliminate the "bad" or noisy samples from the sample set, an experimental method was used to determine the number of hidden units so that both good learning ability and prediction capability can be obtained.