Context-Dependent Outlier Detection Technique for Analysis of Single Event Frequency Transients in CMOS LC-Tank Oscillators

This article presents a contextual outlier detection technique employing machine learning (ML) method to improve the quality of experimentally obtained data from heavy-ion irradiation campaigns. Artifacts arising from the experimental setup often compromise the integrity and representativeness of the measured data. These artifacts, also known as outliers, can be removed to ensure a consistent dataset that accurately represents the device under test (DUT). Due to the dynamic and variable nature of the DUT properties, outliers may manifest in various forms, rendering conventional outlier detection techniques inadequate. To address this challenge, we present a contextual outlier removal technique that incorporates spatial context by segmenting the tested area and using the isolation forest (iForest) algorithm for localized outlier detection. As a case study, this technique is applied to single-event frequency transient (SEFT) data obtained during heavy-ion microbeam irradiation of a planar spiral inductor within an integrated 65-nm CMOS LC-tank digitally controlled oscillator (DCO) circuit. A graphical side-by-side comparison demonstrates significant improvements in data quality, validated by a proposed metric that shows an average enhancement of $4.4\times $ .