Use of Artificial Intelligence Techniques to Fault Diagnosis in Analog Systems Jerzy Rutkowski, Damian Grzechca Department of Automatic Control, Electronics, and Computer Science Silesian University of Technology 44-100 Gliwice, Akademicka Street 16 POLAND Abstract: - Basic concepts of fault diagnosis in analog and mixed (analog and digital) electronic systems by means of the simulation-before-test approach, the so called dictionary approach, have been presented. Special attention has been paid to application of artificial intelligence tools, such as: artificial neural networks, fuzzy sets and evolutionary computing. Key-Words: - Analog electronic fault diagnosis, parametric faults, artificial intelligence. 1 Introduction Automated testing and fault diagnosis of devices are fundamental topics in the development (design, production) and maintenance of safe and reliable complex systems. They are major steps in all production industries, and especially in electronic systems production. Such systems can be constructed almost entirely with digital components, however many systems still have analog components [Bur01]. The digital part can be tested with practically verified standard methods, aided by automatic test pattern generator and Built-In- Self-Test (BIST) [Bak96], [Ric98]. Testing the analog part is less well understood. Since 1970’s, analog fault diagnosis has become an active research area. Two major issues make the diagnosis particularly difficult: unknown deviation in tolerances of nonfaulty components and very complex nature of faults [Mil98]. They generally fall into two categories: catastrophic or hard faults and parametric or soft faults. It is claimed that 80-90 percent of analog faults involve shorted and opened resistors, capacitors, diodes and transistors [Mil89], i.e. catastrophic faults. Moreover, in case of mixed integrated circuits (ICs), the cost of the test process is about 30% of the production costs and it is estimated that 80% of costs refers to analog part which occupies less than 10% of the substrate. The theoretical foundation of analog fault diagnosis is primarily laid, although there is still a long way to go for practical applications. The load board connects the Device Under Test (DUT) to the tester resources (test program + test hardware) and all the performed tests fall into two categories: Specification Driven Tests (SDT) and Fault Driven Tests (FDT) [Hue93]. The SDT or functional tests are those which measure the DUT dynamic behaviour. The FDT or parametric tests measure the DUT responses (node voltages) for given test stimulus or stimuli, and then, component fault can be detected, and eventually further classified, located and identified. In the high level secure and safe systems, like in aviation or aeronautics, the prediction of all faults are obligatory. The pyramid in the fig. 1 shows the low level test up to high level diagnostic with fault localization and identification. Figure 1. Classification of diagnosis precision level. Today, Time-To-Market (TTM) seems to be the most important factor of a design-production process, and thus, time costly SDT have to be preceded by simple and fast FDT [Mil94]. Two different approaches to FDT can be distinguished: Simulation-Before-Test (SBT) and Simulation-After-Test SAT [Ban85]. In the SAT approach fault isolation is obtained by estimating the DUT parameters from the measured responses. All the calculations are performed on-line, after the test, what makes this approach very time consuming, and thus, impractical at a production stage of the DUT life. The SBT approach is based on comparison of the DUT responses associated with predefined test stimulus or High fault isolation level Long time consumption Basic fault isolation level Short time consumption 2nd EUROPEAN COMPUTING CONFERENCE (ECC’08) Malta, September 11-13, 2008 ISSN:1790-5109 267 ISBN: 978-960-474-002-4