Particle Impact Noise Detection Test Research Articles

Identification of loose particles using deep learning convolutional network

Loose particles inside components can be threats to their reliabilities. Automatic identification of loose particles’ material has great significance for finding the source of them. Machine learning methods based on hand-craft features have been widely applied on this problem. As deep learning has made success on various domains, based on PIND (particle impact noise detection) test, a method using spectrograms and CNN (convolutional neural network) is proposed in this paper. First, signals of loose particles including different material are collected by experiments. Then signals are converted to spectrograms. Finally, spectrograms are input to CNN for training and classification. Experiments show that the method can achieve 96 % accuracy on identifying five types of loose particles and has values of practically use.

Research on Auto-detection for Remainder Particles of Aerospace Relay Based on Wavelet Analysis

AbstractAerospace relay is one kind of electronic components which is used widely in national defense system and aerospace system. The existence of remainder particles induces the reliability declining, which has become a severe problem in the development of aerospace relay. Traditional particle impact noise detection (PIND) method for remainder detection is ineffective for small particles, due to its low precision and involvement of subjective factors. An auto-detection method for PIND output signals is proposed in this paper, which is based on direct wavelet de-noising (DWD), cross-correlation analysis (CCA) and homo-filtering (HF), the method enhances the affectivity of PIND test about the small particles. In the end, some practical PIND output signals are analysed, and the validity of this new method is proved.

LSI Packaging that Passes PIND Test

A packaged large-scale integrated (LSI) chip or hybrid destined for military or space applications must pass the gambit of screens listed in Method 5004 of MIL-STD-883B. These screens include the particle impact noise detection (PIND) test in which a sensitive acoustic transducer listens for loose particles inside a package while the package is vibrated and shocked. Prior to instituting new assembly procedures, PIND test yields were as low as 20 percent. We have used scanning electron microscopy (SEM) and energy dispersive analysis X-rays (EDAX) [1] to identify the particles that caused PIND test failures and have developed and are now using assembly procedures that result in a low reject rate at PIND testing. With these procedures, 3099 packages have been PIND tested with 155 failures noted, resulting in yields that exceed 94 percent. The assemblies consisted of a 1 K random access memory (RAM) die with dimensions of 3.5 x 6.1 x 0.031 mm thick which is attached with gold/silicon eutectic to a 24-pin leadless hermetic package. Packages were solder sealed in a belt furnace with a gold-tin eutectic alloy preform and a nickel and gold plated Kovar lid. The major particle type which caused PIND test failures was determined to be gold-tin spheres from the gold-tin preform. To a much lesser degree, other particles such as calcium silicate, gold, and silicone were noted. The effect of furnace temperature, furnace ambient, and package orientation on the number of gold-tin solder balls detected in the sealed packages was investigated. The best results were obtained if the sealing furnace ambient was nonoxidizing, peak temperature was approximately 360°C, and the packages were sealed in a 45° lid down position.

Some aspects of contamination detection, analyses, and control in microcircuits for the NASA Space Shuttle program : Salvadore V. Caruso. Proc. IEEE 28th Electron. Components Conf., Anaheim, 24–26 April 1978, p. 28

The Space Shuttle system is currently one of the National Aeronautics and Space Administration's (NASA) most important and complex programs. A significant number of hybrid microcircuits are being developed and used on the Space Shuttle. Since the Shuttle is a manned vehicle, reliability of the hybrids is flight critical. Loose particle contamination is one of the major causes of failure and reduced operating life for microcircuits. Process controls, surface coatings, and the particle impact noise detection (PIND) test were assessed for control of particle contamination, and the decision was made by NASA to require PIND test for all hybrid circuits. Selected hybrids from the production line of one vendor that had failed the PIND test were analyzed. Results of this program are presented.