In this paper, we present a neural based solution developed for noise reduction and image enhancement using the ZISC, an IBM hardware processor which implements the Restricted Coulomb Energy algorithm and the K-Nearest Neighbor algorithm. Artificial neural networks present the advantages of processing time reduction in comparison with classical models, adaptability, and the weighted property of pattern learning. The goal of the developed application is image enhancement in order to restore old movies (noise reduction, focus correction, etc.), to improve digital television images, or to treat images which require adaptive processing (medical images, spatial images, special effects, etc.). Image results show a quantitative improvement over the noisy image as well as the efficiency of this system. Further enhancements are being examined to improve the output of the system.
In this paper, we will describe the basic features and capabilities of the IBM ZISC036, a massively parallel chip which implements the Restricted Coulomb Energy algorithm and the K-Nearest Neighbor algorithm. Both of the aforementioned algorithms, their learning and recognition phases, and the basic architectural structure of this hardware implementation will be discussed. The ZISC036 chip containing thirty-six neurons has the advantages of processing time reduction in comparison with classical models, adaptability, and pattern learning,; it is both easy to program and operate. A neuron is a processor capable of prototype and associated information storage as well as distance computation and communication with other neurons. At the end of this paper to show the advantage of this model and illustrate the principle of the ZISC, we will present two applications of the ZISC, one for image contour extraction, and the other for visual probe mask inspection on wafers.
KEYWORDS: Neural networks, Prototyping, Manufacturing, Semiconducting wafers, Data processing, Modeling, Very large scale integration, Image processing, Signal processing, Genetic algorithms
Prediction and modeling in the case of non linear systems (or processes), especially of complex industrial processes are known being a class of involved problems. In this paper, we deal with the production yield prediction dilemma in VLSI manufacturing. An RBF neural networks based approach and its hardware implementation on a ZISC neural board have been presented. Experimental results comparing our approach with an expert have been reported and discussed.
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