一种航天接口芯片静电防护电路的软击穿现象

接口阻抗测试是航天产品中对产品状态是否正常来进行判断的常用方法。在航天产品应用中，通常认为接口芯片阻抗测试异常即代表该接口芯片已经失效。本文针对一种LVDS接口发送芯片由静电导致阻抗测试异常，但功能正常的现象进行分析。在元器件失效分析的基础上，定位静电损伤的位置为芯片内部静电防护电路，从而建立了对应的电路模型，对芯片静电损伤的现象进行理论分析。分析说明：该芯片在被静电打击时，其静电防护电路中一个NMOS管受损，但该电路保护了芯片的功能电路，被击穿的NMOS管等效为一个电阻，因此导致阻抗测试异常，但芯片功能电路未受损的现象，为静电软击穿现象。且可认为该芯片在受静电影响后并未失效，相关电路仍具有正常工作的能力。即阻抗异常现象并不是芯片失效的充分条件。     

Time resolved study of laser triggered electric discharge spark in atmosphere: Machine learning approach

In this paper we analyze the possibilities of using machine learning algorithms for analysis of optical spectra of electric discharge spark in atmosphere. Breakdown in air can be initiated by intense laser pulse, making plasma which has a significant electrical conductivity. The formed plasma can be further maintained by electric current obtained from capacitor discharge. In such a case the capacitor voltage can be much lower than the striking voltage (the voltage needed to initiate the electric breakdown in air). Present setup has timing precision and low jitter of fast laser and arbitrary high energies corresponding to capacitance and voltage to which the capacitor is charged. We have used a streak camera equipped with a spectrograph to analyze optical emission of plasma obtained in this way. Q-switched Nd:Yag laser was used to achieve the initial breakdown in air. Machine learning methods were used in order to classify optical spectra of plasmas with different electron temperatures obtained with different excitation energies. We have shown that, instead of using the usual way of identifying the spectral peaks and calculating their intensity ratio, it is possible to train the computer software to recognize the spectra corresponding to different electron temperatures. Principal component analysis was used to reduce the dimensionality of problem. We present possibilities of plasma electron temperature estimation based on several clustering algorithms.