SEM/EDAX Analysis of PIND Test Failures

Porto, Jennifer; Loescher, D. H.; Olson, Howard C.; Plunkett, Pat

doi:10.1109/irps.1981.362989

Cited by 6 publications

(2 citation statements)

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“…Loose particle detection methods used currently include the optical microscopy observation method [6], scanning electron microscope method [7], MATARA method [8], and particle impact noise detection (PIND) method [9]. PIND, a nondestructive, convenient, and efficient method, has been primarily utilised for factory testing of aerospace sealing electronic components.…”

Section: Introductionmentioning

confidence: 99%

Research on a synchronised classification method for loose particle detection signals of aerospace-sealed electronic components

Li,

Zhai,

Wang

et al. 2024

Meas. Sci. Technol.

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Aerospace-sealed electronic components exhibit favourable anti-interference capability and high reliability and are widely utilised in satellites, rockets, and missiles. Loose particle detection is crucial to ensure high reliability. However, the classification problem of loose particle detection signals based on the Particle Impact Noise Detection (PIND) method has been a challenge for the high reliability of aerospace-sealed electronic components. To address this issue, this paper systematically proposes a synchronised classification method based on PIND and validates its feasibility. The proposed approach combines a self-developed empirical model (referred to as Algorithm 1) with a classical machine learning model (referred to as Algorithm 2). The key findings are as follows: this method has a recognition rate for loose particle signals of 91.86%. Compared to a single algorithmic mode, the overall speed improves by 300%. The maximal recognition accuracy rates for component signals, mixed signals, and excessive signals are 90.03%, 81.04%, and 95.17%, respectively. The test results demonstrate that the method effectively balances the accuracy and speed of loose particle detection and leverages the complementary advantages of the two algorithms, thereby addressing the multi-classification issue of loose particle detection signals.

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Section: Introductionmentioning

confidence: 99%

Research on a synchronised classification method for loose particle detection signals of aerospace-sealed electronic components

Li,

Zhai,

Wang

et al. 2024

Meas. Sci. Technol.

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“…First, the failure device is inspected by eyes and there is no difference from good ones. Second, the electrical characteristics test results show that the emitter of the transistor is open and the PIND test [13] results show that there is a foreign material in device. But the X-ray inspection cannot find any extra material and crack.…”

Section: Failure Analysis Of Npn Transistors G3g33amentioning

confidence: 99%

Failure Analysis and Precaution of NPN Transistors G3G33A

Huang

Zhao

et al. 2014

AMR

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Failure analysis (FA) is a critical element in the development of engineering processes and products. FA of components is the process of identifying failure mode/mechanism and finding out the causes of failure that relies on using of different testing techniques and analysis methods. It provides information necessary for technology advancement and for corrective action to improve quality and reliability. IC’s for military and space applications have extremely stringent reliability requirements. In this paper, the role of FA was discussed. And common and new techniques and tools were reviewed. Then the failure analysis of NPN transistors G3GGA was completed. Conclusions and recommendations for the analysis of failure sample and additional sample within the same batch are drawn from the discussions. This paper might serve as a reference to laboratories when encountering such problems.

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