edited by B-T Chu (University of North Carolina, Charlotte) & S-S Chen (University of North Carolina, Charlotte)

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.

• Manufacturing Diagnosis and Control: A Task-Specific Approach (W F Punch III et al.)
• The Theory and Application of Diagnostic and Control Expert System Based on Plant Model (J Suzuki & M Iwamasa)
• Integrated Problem Solving for the Diagnosis of Interacting Process Malfunctions (J K McDowell & J F Davis)
• A Neural Network Model for Diagnostic Problem Solving (Y Peng & J A Reggia)
• Process Control System for VLSI Fabrication (E Sachs et al.)
• Development and Application of Equipment-Specific Process Models for Semiconductor Manufacturing (K-K Lin & C Spanos)
• Continuous Equipment Diagnosis Using Evidence Integration — An LPCVD Application (N H Chang & C Spanos)
• Equipment/Instrument Diagnosis with Continuous and Discrete Causal Relationship (B-T B Chu)
• Intelligent Control of Semiconductor Manufacturing Processes (S-S Chen)
• A Machine Learning Approach to Diagnosis and Control with Applications in Semiconductor Manufacturing (K B Irani et al.)