摘要
A lifetime prediction method for high-reliability tantalum （Ta） capacitors was proposed, based on multiple degradation measures and grey model （GM）. For analyzing performance degradation data, a two-parameter model based on GM was developed. In order to improve the prediction accuracy of the two-parameter model, parameter selection based on particle swarm optimization （PSO） was used. Then, the new PSO-GM（1, 2, co） optimization model was constructed, which was validated experimentally by conducting an accelerated testing on the Ta capacitors. The experiments were conducted at three different stress levels of 85, 120, and 145℃. The results of two experiments were used in estimating the parameters. And the reliability of the Ta capacitors was estimated at the same stress conditions of the third experiment. The results indicate that the proposed method is valid and accurate.
A lifetime prediction method for high-reliability tantalum(Ta) capacitors was proposed,based on multiple degradation measures and grey model(GM).For analyzing performance degradation data,a two-parameter model based on GM was developed.In order to improve the prediction accuracy of the two-parameter model,parameter selection based on particle swarm optimization(PSO) was used,Then,the new PSO-GM(1,2,ω) optimization model was constructed,which was validated experimentally by conducting an accelerated testing on the Ta capacitors.The experiments were conducted at three different stress levels of 85,120,and 145 °C.The results of two experiments were used in estimating the parameters.And the reliability of the Ta capacitors was estimated at the same stress conditions of the third experiment.The results indicate that the proposed method is valid and accurate.
基金
Project(Z132012) supported by the Second Five Technology-based Fund in Science and Industry Bureau of China
Project(1004GK0032) supported by General Armament Department for the Common Issues of Military Electronic Components,China
