Ghislain Imbert De Tremiolles

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.

Effects of global perturbations on learning capability in a CMOS analogue implementation of synchronous Boltzmann machine

All of the presented implementations of Artificial Neural Networks (A.N.N.) have been supposed to be working in ideal conditions, however, real applications will be subject to local and global perturbations. Since 1994, we have investigated the behaviour modelling of electronic A.N.N. with global perturbation conditions. We have scrutinised the behaviour analysis of a CMOS analogue implementation of synchronous Boltzmann Machine model with both ambient temperature and electrical perturbation. In this paper we present, using our model, the analysis of these global perturbations effects on learning capability of the above mentioned CMOS based analogue implementation. Simulation and experimental results have been exposed validating our concepts.

Global perturbation effects on learning capability in a CMOS analog implementation of synchronous Boltzmann machine

A very large number of works concerning the area of Artificial Neural Networks (ANN) deal with implementation of these models, especially as digital or analogue CMOS integrated circuits. All of the presented implementations of A.N.N. have been supposed to be working in ideal conditions but real applications will be subject to global perturbations. Unfortunately, very few papers analyze the behavior of analogue implementation of neural network with such kind of perturbations. Since 1994, we have investigated the behavior modeling of electronic A.N.N. with global perturbation conditions. We have scrutinized the behavior analysis of a CMOS analogue implementation of synchronous Boltzmann Machine model with both ambient temperature and electrical perturbations (supply voltage) perturbation. In this paper we present, using our model, the analysis of these global perturbations effects on learning capability in a CMOS analogue implementation of synchronous Boltzmann Machine Simulation and experimental results have been exposed validating our concepts.

Perturbation effects analysis in analog implementation of a stochastic artificial neural network

Analogue implementation of Artificial Neural Networks (ANN) especially as CMOS integrated circuits show several attractive features. During the last decade, numerous works show that small size analogue ANN operate correctly. However, today the efforts are focused on real industrial size application of ANN that will require large networks. On the other hand, all of the presented implementations of ANN have been supposed to be working in ideal conditions but real applications will be subject to some global perturbations. Especially in the case of the analogue and mixed digital/analogue implementation, the behavior analysis of the neural network with perturbation conditions is thus inevitable. Unfortunately, very few papers analyze the behavior of analogue neural network with global perturbations. We have investigated modeling and experimental validation of the behavior of analogue ANN in the case of a global perturbation of the network. We have analyzed the behavior of a CMOS analogue implementation of synchronous Boltzmann Machine model when the neural circuit is subject to perturbations. The perturbations we have considered concern the supply voltage of the neural circuit and ambient temperature in which the circuit operates. In this paper we present the analysis of the behavior of the analogue implementation of synchronous Boltzmann Machine with electrical and thermal perturbations. Simulation and experimental results have been exposed.

Analysis of limitations in analogue implementation of stochastic artificial neural networks

The implementation of artificial neural networks (ANN) as CMOS analog integrated circuits shows several attractive features. Stochastic models, and especially the Boltzmann Machine shows a number of many attractive features. Numerous papers show that small size analog networks operate correctly. However, recent studies on artificial models point out that classification is their most successful application field: so real pattern recognition tasks will require large networks. On the other hand, all of the presented implementations of ANN have been supposed to be working in ideal conditions but real applications will subject to perturbations. For a digital implementation of ANN perturbation effects could be neglected in a fifth-order approximation. But for the analog and mixed digital/analog implementation cases, the behavior analysis of the neural network with perturbation conditions is inevitable. Unfortunately, very few papers analyze the behavior of analog neural networks with perturbation or their limitations. In this paper we present the analysis of a CMOS analog implementation of synchronous Boltzmann Machine models behavior with physical temperature perturbations. The relation between the T parameter of the Boltzmann Machines model and the physical temperature of circuit has been presented. Simulation results have been given, temperature effects compensation have been discussed, and experimental results have been exposed.