Analysis of Single Event Transients in Arbitrary Waveforms Using Statistical Window Analysis

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).