Institute for Systems Research

In this research, an ULVAC ERA-1000 selective tungsten chemical vapor deposition system located at the University of Maryland was studied where a temperature difference as large as 120 ^oC between the system wafer temperature reading and the thermocoupled instrumented wafer measurement was found during the manual processing mode.

The goal of this research was to develop a simplified, but accurate, three-dimensional transport model that is capable of describing the observed reactor behavior.

A hybrid approach combining experimental and simulation studies was used for model development. Several sets of experiments were conducted to investigate the effects of process parameters on wafer temperature.

A three-dimensional gas flow and temperature model was developed and used to compute the energy transferred across the gas/wafer interface. System dependent heat transfer parameters were formulated as a nonlinear parameter estimation problem and identified using experimental measurements.

Good agreement was found between the steady-state wafer temperature predictions and experimental data at various gas compositions, and the wafer temperature dynamics were successfully predicted using a temperature model considering the energy exchanges between the thermocouple, wafer, and showerhead.

In this paper, RtR control methods are generalized. The set-valued RtR controllers with ellipsoidapproximation are compared with other RtR controllers bysimulation according to the following criteria: A good RtR controller should be able to compensate for variousdisturbances, such as process drifts, process shifts (step disturbance)and model errors; moreover, it should beable to deal with limitations, bounds, cost requirement, multipletargets and time delays that are often encountered in realprocesses.

Preliminary results show the good performance of the set-valued RtRcontroller. Furthermore, this paper shows that it is insufficient to uselinear models to approximate nonlinear processes and it is necessary to developnonlinear model based RtR controllers.

In this report, we first introduce belief networks in the light of knowledge representation under uncertainty, then in the remainingsections we give the descriptions of the semantics, inference mechanisms and some issues related to learning belief networks, respectively. This report is not intended to be a tutorial for beginners. Rather it aims to point out some important aspects of belief networks and summarize some important algorithms.

We formulate this task as an inverse problem, specifying a well-defined cost function that has to be optimized under constraints.

In particular, we propose the use of a Linear Regularization method, which ﲭaximizes the smoothness of the estimate. Our main theoretical contribution is a Theorem, which shows that, for smooth enough distributions, we can achieve full recovery from summary data.

Our theorem is closely related to the well known Shannon-Nyquist sampling theorem.

We describe how to apply this theory to a variety of database problems, that involve partial information, such as OLAP, data warehousing and histograms in query optimization. Our main practical contribution is that the Linear Regularization method is extremely effective, both on synthetic and on real data. Our experiments show that the proposed approach almost consistently outperforms the ﲵniformity assumption, achieving significant savings in root-mean-square error: up to 20% for stock price data, and up to 90% for smoother data sets.

We formulate this task as an inverse problem, specifying a well-defined cost function that has to be optimized under constraints.

In particular, we propose the use of a Linear Regularization method, which ﲭaximizes the smoothness of the estimate. Our main theoretical contribution is a Theorem, which shows that, for smooth enough distributions, we can achieve full recovery from summary data.

Our theorem is closely related to the well known Shannon-Nyquist sampling theorem.

We describe how to apply this theory to a variety of database problems, that involve partial information, such as OLAP, data warehousing and histograms in query optimization. Our main practical contribution is that the Linear Regularization method is extremely effective, both on synthetic and on real data. Our experiments show that the proposed approach almost consistently outperforms the ﲵniformity assumption, achieving significant savings in root-mean-square error: up to 20% for stock price data, and up to 90% for smoother data sets.