This Cooperative Research and Development Agreement (CRADA) between Innovative Computing Technologies, Inc. (IC Tech) and Martin Marietta Energy Systems (MMES) was undertaken to contribute to improved process control for microelectronic device fabrication. Process data from an amorphous silicon thin film deposition experiment was acquired to validate the performance of an intelligent, adaptive, neurally-inspired control software module designed to provide closed loop control of plasma processing machines used in the microelectronics industry. Data acquisition software was written using LabView The data was collected from an inductively coupled plasma (ICP) source, which was available for this project through LMES's RF/Microwave Technology Center. Experimental parameters measured were RF power, RF current and voltage on the antenna delivering power to the plasma, hydrogen and silane flow rate, chamber pressure, substrate temperature and H-alpha optical emission. Experimental results obtained were poly-crystallin silicon deposition rate, crystallinity, crystallographic orientation and electrical conductivity. Owing to experimental delays resulting from hardware failures, it was not possible to assemble a complete data for IC Tech use within the time and resource constraints of the CRADA. IC Tech was therefore not able to verify the performance of their existing models and control structures and validate model performance under this CRADA.

The MURI Center on Modeling and Control of Plasma Processing at the University of Michigan started in September, 1995, and concluded technical work at the end of August 2001. As the name indicates, the major research goals of the center are in the areas of modeling and control of plasma deposition and etching processing. These plasma processes are used extensively in the manufacture of integrated circuits as well as active matrix liquid crystal displays. These applications areas motivate our selection of research problems in modeling and control. Significant accomplishments were made in all of these areas (as will be discussed in the body of the report) Particular program highlights include: (1) An optical technique was developed to monitor in situ and in real time the critical dimensions and wall-shapes of evolving features in reactive ion etchers. An advanced signal processing scheme was devised to use this technique to perform the first fully-automated etch-to-target-dimension etches. One-nanometer-level (or better) accuracy was demonstrated enabling possibilities for extremely high accuracy semiconductor fabrication control. (2) The state-of-the-art of 1st principles plasma equipment modeling was advanced so that the entire system of the sensors, plasma process equipment, and control systems could be modeled numerically. (3) Novel RF Sensing to non-invasively measure the electrical state of plasma systems was developed and applications to detecting common faults were demonstrated. (4) Improved statistical methods for detecting and identifying the causes of spatially clustered defects in semiconductor manufacturing. (5) Development of a novel ion-beam modification process for the deposition of Al films which are more resistant to grain-growth.

This volume demonstrates that the key to the modeling, diagnosis and control of the next generation manufacturing processes is to integrate knowledge-based systems with traditional techniques. An up-to-date study is given here of this relatively recent development.The book is for those working primarily with traditional techniques and those working in the knowledge-based systems field. Both sets of readers will find it to be a source of many specific ideas about the integration of knowledge-based systems with traditional techniques, and carrying a wealth of useful references.

Intelligent Control with LabVIEWTM is a fresh and pragmatic approach to the understanding of a subject often clouded by too much mathematical theory. It exploits the full suite of tools provided by LabVIEWTM, showing the student how to design, develop, analyze, and visualize intelligent control algorithms quickly and simply. Block diagrams are used to follow the progress of an algorithm through the design process and allow seamless integration with hardware systems for rapid deployment in laboratory experiments. This text delivers a thorough grounding in the main tools of intelligent control: fuzzy logic systems; artificial neural networks; neuro-fuzzy systems; evolutionary methods; and predictive methods. Learning and teaching are facilitated by: extensive use of worked examples; end of chapter problems with separate solutions; and provision of intelligent control tools for LabVIEWTM.

Despite the available general literature in intelligent control, there is a definite lack of knowledge and know-how in practical applications of intelligent control in drying. This book fills that gap. Intelligent Control in Drying serves as an innovative and practical guide for researchers and professionals in the field of drying technologies, providing an overview of control principles and systems used in drying operations, from classical to model-based to adaptive and optimal control. At the same time, it lays out approaches to synthesis of control systems, based on the objectives and control strategies, reflecting complexity of drying process and material under drying. This essential reference covers both fundamental and practical aspects of intelligent control, sensor fusion and dynamic optimization with respect to drying.

This book provides for the first time a good understanding of the etching profile technologies that do not disturb the plasma. Three types of sensors are introduced: on-wafer UV sensors, on-wafer charge-up sensors and on-wafer sheath-shape sensors in the plasma processing and prediction system of real etching profiles based on monitoring data. Readers are made familiar with these sensors, which can measure real plasma process surface conditions such as defect generations due to UV-irradiation, ion flight direction due to charge-up voltage in high-aspect ratio structures and ion sheath conditions at the plasma/surface interface. The plasma etching profile realistically predicted by a computer simulation based on output data from these sensors is described.