Analysis of Single Event Transients in Arbitrary Waveforms Using Statistical Window Analysis
Analysis of Single Event Transients in Arbitrary Waveforms Using Statistical Window Analysis
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DOI：
10.1109/tns.2023.3243496
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发表时间：
2023-04
影响因子：
1.8
通讯作者：
J. L. Carpenter;T. Peyton;B. R. Dean;S. Lawrence;R. D. Young;D. Reising;T. D. Loveless
中科院分区：
文献类型：
--
作者：
J. L. Carpenter;T. Peyton;B. R. Dean;S. Lawrence;R. D. Young;D. Reising;T. D. Loveless
窗函数或锥函数通常用于数据处理，以检测瞬态事件或频谱的时间平均。利用电离辐射效应光谱（IRES）技术证明了一种广义窗函数，以增强对任意波形内瞬态异常的测量。使用IRES滤波器对时间数据与由矩生成函数组成的滑动窗口进行卷积。由此产生的与时间相关的统计矩可用于消除任何稳态特征，包括噪声，并提取瞬态行为。IRES滤波器用于分析商用现货（COTS）运算放大器（Op-Amps）的重离子暴露数据、CMOS锁相环（pll）中的激光诱导瞬态以及数字和模拟电路中的模拟瞬态。IRES滤波器在噪声环境下的性能表明，与标准幅度阈值相比，瞬态测量具有更高的保真度。这种统计窗口分析技术可以消除对仪器上复杂触发机制的需要，并且不需要先验的瞬态特性知识。IRES的潜在应用包括实时测量、现场数据分析和机器学习（ML）。
Window or taper functions are commonly used in data processing to detect transient events or for time-averaging of frequency spectra. A generalized window function is demonstrated using the ionizing radiation effects spectroscopy (IRES) technique to enhance the measurement of transient anomalies within arbitrary waveforms. The IRES filter is used to convolve time data with a sliding window consisting of a moment-generating function. The resulting time-dependent statistical moments can be used to eliminate any steady-state signatures, including noise, and extract transient behaviors. The IRES filter is used to analyze data from heavy-ion exposures of commercial off-the-shelf (COTS) operational amplifiers (Op-Amps), laser-induced transients in CMOS phase-locked loops (PLLs), and simulated transients in digital and analog circuits. The performance of the IRES filter in noisy environments shows that transients can be measured with higher fidelity than standard amplitude thresholding. This statistical window analysis technique may remove the need for complex triggering mechanisms on instrumentation and does not require a priori knowledge of transient characteristics. Potential applications of IRES include real-time measurement, in situ data analysis, and machine learning (ML).