The boundary scan architecture and its basic principle of board level built in test(BIT) technology are presented. A design for board level built in test and the method to implement test tool are brought forward.
随着以太网技术和集成电路技术的发展,以太网物理层(Physical Layer,PHY)芯片的速率和性能都得到了极大提升,电路复杂度更是几何级增长,以至于常规的自动测试设备(Automatic Test Equipment,ATE)测试很难充分验证其功能,所以亟需开展相...随着以太网技术和集成电路技术的发展,以太网物理层(Physical Layer,PHY)芯片的速率和性能都得到了极大提升,电路复杂度更是几何级增长,以至于常规的自动测试设备(Automatic Test Equipment,ATE)测试很难充分验证其功能,所以亟需开展相应测试方法研究。提出了一种高效的基于ZYNQ MPSOC的以太网PHY芯片功能测试方法。该方法以ZYNQ MPSOC为核心,设计了一种直达应用层面的系统级测试装置,从而减少了与物理层直接交互的行为,有效降低了测试装置及程序开发难度。经试验验证,提出的基于ZYNQ MPSOC的以太网PHY芯片功能测试方法能够用于以太网PHY芯片测试。展开更多
A feature extraction for latent fault detection and failure modes classification method of board-level package subjected to vibration loadings is presented for prognostics and health management(PHM) of electronics usi...A feature extraction for latent fault detection and failure modes classification method of board-level package subjected to vibration loadings is presented for prognostics and health management(PHM) of electronics using adaptive spectrum kurtosis and kernel probability distance clustering. First, strain response data of electronic components is filtered by empirical mode decomposition(EMD) method based on maximum spectrum kurtosis(SK), and fault symptom vector is developed by computing and reconstructing the envelope spectrum. Second, nonlinear fault symptom data is mapped and clustered in sparse Hilbert space using Gaussian radial basis kernel probabilistic distance clustering method. Finally, the current state of board level package is estimated by computing the membership probability of its envelope spectrum. The experimental results demonstrated that the method can detect and classify the latent failure mode of board level package effectively before it happened.展开更多
文摘The boundary scan architecture and its basic principle of board level built in test(BIT) technology are presented. A design for board level built in test and the method to implement test tool are brought forward.
基金supported by the National Natural Science Foundation of China(Grant No.51201182)the Aeronautical Science Foundation of China(Grant No.20142896022)
文摘A feature extraction for latent fault detection and failure modes classification method of board-level package subjected to vibration loadings is presented for prognostics and health management(PHM) of electronics using adaptive spectrum kurtosis and kernel probability distance clustering. First, strain response data of electronic components is filtered by empirical mode decomposition(EMD) method based on maximum spectrum kurtosis(SK), and fault symptom vector is developed by computing and reconstructing the envelope spectrum. Second, nonlinear fault symptom data is mapped and clustered in sparse Hilbert space using Gaussian radial basis kernel probabilistic distance clustering method. Finally, the current state of board level package is estimated by computing the membership probability of its envelope spectrum. The experimental results demonstrated that the method can detect and classify the latent failure mode of board level package effectively before it happened.
