In recent years, the requirement of compact devices caused an increasing use of Surface Mount Technology. This technology guarantees the reduction of the size of electronic packages by exploiting solder joint interconnection technology. Nevertheless, parameter variations can occur during the deposition and printing of the soldering paste on a board, compromising its correct working. In this paper, it is proposed a fuzzy architecture for computing an index which provides a quantitative refined assessment about the quality of the soldered interconnections. This task is performed by reproducing the modus operandi of the human experts during their assessments. The proposed architecture consists of three modules connected in series: a feature extraction block and two fuzzy ones. The presented solution keeps the benefits of a neurofuzzy system previously proposed in literature, like the reduction of equipment and computational costs. Moreover, it implies two further advantages: the influence of the human experts in its design is reduced and its implementation is reasonable. Experimental results confirm such advantages, in fact, the architecture approximates the human assessments reliably.

Recently, surface mount technology is extensively used in the production of printed circuit boards due to the high level of miniaturization and to the increase of density in the electronic device integration. In such production process several defects could occur on the final electronic components, compromising its correct working. In this paper a neurofuzzy solution to process information deriving from an automatic optical system is proposed. The designed system provides a global quality index of a solder joint, starting from the assessment of a human inspector. This target is achieved by reproducing themodusoperandiof the expert, evaluating the area, the shape and the barycentre position of a solder joint. The proposed architecture is constituted by three supervised neural networks and two fuzzy rule-based modules which automate expert's work and provide a refined evaluation of the quality. The considered solution presents some attractive advantages: a complex acquisition system is not needed, equipment costs could be reduced by shifting the assessment of a solder joint on the fuzzy parts. Moreover, intermediate variables used in the method could be employed as control parameters in the production process under analysis.

In this paper an automatic inspection method for the diagnosis of solder joint defects on integrated circuits mounted in surface mounting technology is presented. The diagnosis is handled as a classification problem with a neural network approach. Two classes of solder joints have been fixed with respect to the amount of the soldering paste. These correspond to acceptable and non acceptable solder joints to assure the correct working of the integrated circuits. The images of the boards under test are acquired by an acquisition system; then they are pre-processed to extract the region of interest for the diagnosis. A "geometric" features vector is extracted from this region and it feeds a multi layer perceptron neural network. Experimental results show that these networks perform high recognition rate and the robustness of the proposed method.

The ultrasonic inspection technique takes a relevant place in not destructive defect detection. It can be very useful to determine the state of not accessible structure. In this paper a method based on ultrasonic waves inspection to evaluate the dimensions of flaws in not accessible pipes is shown. The method performs the extraction of time and frequency features from simulated ultrasonic waves and the proper reduction of the number of these features. Then a neural network classification evaluates the dimension of the flaws in the pipe under test. The results show low error rates for all classes considered.

The defect detection on manufactures is extremely important in the optimization of industrial processes; particularly, the visual inspection plays a fundamental role. The visual inspection is often carried out by a human expert. However, new technology features have made this inspection unreliable. For this reason, many researchers have been engaged to develop automatic analysis processes of manufactures and automatic optical inspections in the industrial production of printed circuit boards. Among the defects that could arise in this industrial process, those of the solder joints are very important, because they can lead to an incorrect functioning of the board; moreover, the amount of the solder paste can give some information on the quality of the industrial process. In this paper, a neural network-based automatic optical inspection system for the diagnosis of solder joint defects on printed circuit boards assembled in surface mounting technology is presented. The diagnosis is handled as a pattern recognition problem with a neural network approach. Five types of solder joints have been classified in respect to the amount of solder paste in order to perform the diagnosis with a high recognition rate and a detailed classification able to give information on the quality of the manufacturing process. The images of the boards under test are acquired and then preprocessed to extract the region of interest for the diagnosis. Three types of feature vectors are evaluated from each region of interest, which are the images of the solder joints under test, by exploiting the properties of the wavelet transform and the geometrical characteristics of the preprocessed images. The performances of three different classifiers which are a multilayer perceptron, a linear vector quantization, and a K-nearest neighbor classifier are compared. The n-fold cross-validation has been exploited to select the best architecture for the neural classifiers, while a number of experiments have been devoted to estimating the best value of K in the K-NN. The results have proved that the MLP network fed with the GW-features has the best recognition rate. This approach allows to carry out the diagnosis burden on image processing, feature extraction, and classification algorithms, reducing the cost and the complexity of the acquisition system. In fact, the experimental results suggest that the reason for the high recognition rate in the solder joint classification is due to the proper preprocessing steps followed as well as to the information contents of the features

In this paper we describe a method to recognize missing components on manufactured products. The proposed approach exploits the wavelet transform to extract features from the acquired data, while the diagnosis is performed by means of a neural network. The results show that this method achieves an high recognition rate. At the same time the method allows to use a very cheap diagnostic system.

We describe a technique to compare two data partitions of two different data sets as frequently occurs in defect detection. The comparison is obtained dividing each data set in partitions by means of an unsupervised neural network and associating an undirected complete weighted graph structure to these partitions. Then, a graph matching operation returns an estimation of the level of similarity between the data sets.

Unsupervised Competitive Neural Networks (UCN) have been recognized as a powerful tool for pattern analysis, feature extraction and clustering analysis. Nevertheless, the inhibitory interactions among the units of the network, required by the winner-take-all paradigm, constitute a crucial step for the implementation of competitive networks in analog VLSI. The aim of this paper is to present an unsupervised competitive neural network characterized by local inhibitory interactions among its cells. The kernel of this network is a neural unit based on a modified competitive learning law in which the threshold changes in the learning stage. It is shown that the proposed neuron unit is able, during the learning stage, to perform an automatic selection of patterns that belong to a cluster, moving towards its centroid. The properties of this network, related to the robustness of the final results and to the choice of the number of the elements, are examined in a set of numerical simulations adopting a data set composed of Gaussian mixtures and uniform noise.

This paper presents a new approach to fuzzy clustering using a membership function sensitive to density. It is a fuzzy membership function which allows the action range of the neural units matching the area they reach, even when the data set is contaminated by uniformly distributed noise points, without a need to fix a priori the number of clusters.

In this paper a new approach to information storage and retrieval using cellular neural networks is developed. The objective is achieved by considering a suitable discrete-time model of these networks and by designing them so that the input information are fed via external inputs rather than initial conditions. The technique, which exploits globally asymptotically stable networks, leads to a facilitation of their hardware implementation.

In this paper a new unsupervised neural network, characterized by the absence of competition between its elements, is introduced. The kernel of this network is a new neural unit able to perform clustering even acting alone. It is shown how this network overcomes some of the major drawbacks of classical unsupervised competitive architectures.>

The problem of improving the computational efficiency of a method for an iterative solution of a linear circuit equation, known as the tree relaxation (TR) method, is investigated. It is shown that the adoption of the tree-branch voltages instead of the node voltages, resulting in a tree relaxation modified (TR) method, represents a more advantageous choice for the network variables. The extra processing needed for setting up the fundamental cut-set matrix allows a significant reduction of the computational burdens to be obtained. These savings are even more substantial if a transient analysis is to be performed. It is shown that the choice of the tree-branch voltages yields further advantages if a parallel version of the TRM is to be implemented.>