The Optimization Of Flying Needle Test Path Based On Improved Ant Colony Algorithm

Since the 30 years of the 20 th century the first piece of printed circuit board(PCB) birth, printed circuit board this revolutionary technology changed the whole electronic industry and widely used in automobile manufacturing, communications, computer, semiconductor, and many other industries.Engineers and experts from all over the world continue to develop new processes and technologies to make the performance and quality of printed circuit boards improved greatly.The integration of printed circuit boards is becoming more and more high, showing a trend of miniaturization and high integration, which is no doubt that the production and processing of printed circuit boards are becoming more and more important. Flying probe test is the main means of the printed circuit board testing, it replaces the traditional needle bar, change the yield is low and fast conversion test method for product assembly. Greatly reduce the test cycle and testing costs of the product, so that the product can be published as soon as possible. Currently on the market, the main testing methods are: inductance measurement, charge / discharge time method, phase difference method, capacitance measurement method and adaptive testing method, etc.. Different test methods correspond to different test path generation algorithms, and the flight test path generation problem is a NP-hard problem, so the test path directly affects the level of test efficiency.This paper briefly introduces the development status of the flying probe test and the advantages and characteristics. And then through the analysis, we find that the flying probe test path problem actually consists of two parts, one is according to the different functions of the test point matching problem, second is the path generation optimization problem, the second part of the path optimization problem is actually TSP traveling salesman problem. For test point matching problem, this paper studied the flying probe test open circuit and capacitance method detection, respectively,and given a test point matching method, using the Shenzhen fly testing circuit board Co., Ltd. production of Flying Probe Tester experiments were carried out, confirmed the generated test path validity and correctness. On the basis of this, the improved ant colony algorithm is used to optimize the test path. Ant colony algorithm by the Italy scientist M Dorige, who in the last century 90's proposed. Ant colony algorithm to solve some complex problems and show a good performance by simulating the social behavior of ant colony, and it is considered to be one of the best solutions to solve the traveling salesman problem. In view of this, this paper attempts to apply ant colony algorithm to fly test machine path generation problem, in order to find a method to solve the problem of Flying Probe Tester path generation. For the improvement of ant colony algorithm mainly through the information entropy as ant colony algorithm stopping criteria, and the basic ant colony algorithm, artificial ants, division of single defects divided ants as the pioneer of ants and search ants and ant pioneer responsible to search ants provide initial information, and greatly reduces the search ants search space and accelerate the convergence speed of the algorithm. At the end of the article gives analysis for the parameters of ant colony algorithm is improved, compared with the path cost of the new algorithm and the original algorithm results.