Pascal Tannhof

Image Processing Using RBF like Neural Networks: A ZISC-036 Based Fully Parallel Implementation Solving Real World and Real Complexity Industrial Problems

The present article concerns neural based image processing and solutions developed for industrial problems using the ZISC-036 neuro-processor, an IBM hardware processor which implements the Restricted Coulomb Energy algorithm (RCE) and the K-Nearest Neighbor algorithm (KNN). The developed neural based techniques have been applied for 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.). We also have developed and implemented on ZISC-036 neuro-processor, a neural network based solution for visual probe mark inspection in VLSI production for the IBM Essonnes plant. The main characteristics of such systems are real-time control and high reliability in detection and classification tasks. Experimental results, validating presented concepts, have been reported showing quantitative and qualitative improvement as well as the efficiency our solutions.

Visual Probe Mark Inspection, Using Hardware Implementation of Artificial Neural Networks, in VLSI Production

As a result of their adaptability, artificial neural networks present good solutions for a permanently increasing range of industrials problems. So, if their usefulness has already been confirmed, very few papers deal with real applications of this kind of technology. Our goal is to present a neural based solution that we have developed for visual inspection in VLSI production for the IBM Essonnes plant. The main characteristics of such systems are real-time control and high reliability in detection and classification tasks. The presented system is based on a ZISC©, an IBM hardware implementation of the Restricted Coulomb Energy algorithm and of the K-Nearest Neighbor algorithm. The goal of the developed application is to inspect vias for probe damage during wafer tests: each via is analyzed and classified (good impact, bad impact or absence of impact). First results are really encouraging and show the efficiency of this system in manufacturing environment.

Description and practical uses of IBM ZISC036

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.

Noise reduction and image enhancement using a hardware implementation of artificial neural networks

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.

ZISC-036 Neuro-processor Based Image Processing

This paper deals with neural based image processing and developed solutions using the ZISC-036 neuro-processor, an IBM hardware processor which implements the Restricted Coulomb Energy algorithm (RCE) and the K-Nearest Neighbor algorithm (KNN). The developed neural based techniques have been applied for 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. Experimental results, validating the exposed concepts, have been reported showing quantitative and qualitative improvement as well as the efficiency of our solutions.

Neural based production yield prediction : an RBF based approach

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.