1 November 1999 Neural nets for modeling, optimization, and control in semiconductor manufacturing
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This paper provides an overview of our recent work in the development of neural network models for optimization and control of electronic manufacturing processes. The concept of physical-neural network models and model transfer are described and demonstrated to be effective in building accurate neural network models economically. Process diagnostic techniques using multiple neural networks are reviewed and shown to be accurate for fault diagnosis. Finally, recent strategies in integration of statistical and neural network tools for process control are discussed. Several examples from electronics manufacturing such as chemical vapor deposition and fine pitch stencil printing are described to illustrate application of the basic concepts discussed.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Roop L. Mahajan, Roop L. Mahajan, } "Neural nets for modeling, optimization, and control in semiconductor manufacturing", Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); doi: 10.1117/12.367694; https://doi.org/10.1117/12.367694


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