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FIGURE 37.8
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Indirect measurement: constant-current discharge.
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37.4.4.2 Adjacency Analysis: Isolation Testing on Flying Probe Systems. Adjacency analysis simplifies isolation testing on flying probers by reducing the number of measurements, even for direct measurement methods. In adjacency analysis, a database is prepared listing each network and all traces found to be immediately adjacent to that network according to a set of geometric criteria. Such locations are assumed to represent the sole opportunities for shorts and leaks. Isolation testing is then performed only between adjacent pairs, this measurement being well suited to the nature of the flying prober. It is important that attention be paid to whether such analysis is three-dimensional. Networks may cross over a network horizontally, but on a different layer of the board. A hole or defect in the intervening insulating layer may allow a short or leakage to FIGURE 37.9 Network adjacency. occur. In the example illustrated in Fig. 37.9, network A would probably be judged adjacent to B, but not to C. B would be considered adjacent to A and D. Assuming that the qualifying dimension was a bit smaller than the distance from E to either B or D, E might not be judged adjacent to either.
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DATA AND FIXTURE PREPARATION
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An essential element in any test operation is the means of generating test programs, and, in the case of fixtured tests, the design of an appropriate test fixture. There are several methods for deriving this program and fixture information. These methods vary from simple to complex and from escape-prone to very sound.The most common terms used in the language of bare board testing for the test program development are self-learning and netlist testing. Netlist testing is a misnomer that confuses computer-aided design (CAD) output with test inputs and implies that there is not much in between. In this discussion, we will use the term data-driven test program to indicate the program to be loaded into the test system is derived from the board s design data (which may be provided in any of several different forms). On the other hand, a self-learned program is derived by placing an assumed good board on the test system and causing the test system s internal computer to create a
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program automatically that can be used to compare other boards to the detected pattern. It is worth noting that the description known good is often used to describe this first board, when in fact our knowledge of its quality is often imperfect. Flying probe test systems generally require no fixturing, except sometimes a frame to hold thin boards or to load multiple small boards simultaneously. Flying probers still require test program generation and optimization. Instead of a fixture, the product program contains probe position information for planned test. Data preparation processes are therefore similar to those of fixtured tests, but only test program data are output. Note that the extractions of program and fixture information are quite naturally linked. In fact, the final test program cannot be created until the fixture is defined. The fixture determines how the individual test points of the test system are connected to specific product locations. Knowledge of how the fixture has taken hold of the product is essential to determining which system test points to employ in performing specific measurements. Thus, the same software system generally outputs both the final test program and the fixture drilling/loading information.
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Self-Learning Self-learning has increasingly limited application and is subject to certain escape risks derived from assuming that the original circuit board is good. The self-learning method requires that a fixture be already available, as well as a known good board (preferably). A shorting plate is placed on the fixture in place of the product, and the test system uses this to identify which test points are employed in the test fixture. (All test points found shorted to the plate are considered active. This eliminates test points of no interest, shortening the test program and saving considerable execution time during both learning and testing operations. This set of active points is sometimes referred to as a mask.) The known good board is then substituted for the shorting plate, and the pattern of product interconnections is learned and saved as a test program. A few boards are tested, and, if results appear reasonable, the program is considered valid. The key drawback of the method is that self-learned test programs determine that all the boards are the same, not that they are good. Moreover, for economic fabrication of the test fixture, it is necessary to process product data anyway; thus we may as well have output a datadriven test program. With virtually 100 percent of board designs being CAD-driven, there is little motivation to use self-learning today.
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Data-Driven Testing The preferred method of deriving program and/or fixture data is data-driven programming (DDP), sometimes referred to as netlist testing. The basic idea of data-driven test programming is to test the board using the same database used to specify its manufacture in other words, the original design database.The fixture and DDP development process can be divided into two stages:
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Input/extraction, the preparation and processing of the various possible input sources into a format usable by the second stage Fixture data, test program, and repair file output, and subsequent application of the data.
Factors such as data quality and completeness, the format of data available, and fixture design can significantly reduce or extend software processing and engineering time. Board technology, size, and complexity tend to increase time requirements. While software tools are
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