RTNinja: A generalized machine learning framework for analyzing random telegraph noise signals in nanoelectronic devices
This provides a robust, scalable tool for nanoelectronics researchers and engineers to improve device reliability and performance characterization, though it is incremental as it builds on existing analysis methods with automation and generalization.
The authors tackled the problem of analyzing random telegraph noise in nanoelectronic devices by introducing RTNinja, a fully automated machine learning framework that deconvolves complex signals to identify hidden sources, achieving high-fidelity reconstruction and accurate extraction across 7000 datasets.
Random telegraph noise is a prevalent variability phenomenon in nanoelectronic devices, arising from stochastic carrier exchange at defect sites and critically impacting device reliability and performance. Conventional analysis techniques often rely on restrictive assumptions or manual interventions, limiting their applicability to complex, noisy datasets. Here, we introduce RTNinja, a generalized, fully automated machine learning framework for the unsupervised analysis of random telegraph noise signals. RTNinja deconvolves complex signals to identify the number and characteristics of hidden individual sources without requiring prior knowledge of the system. The framework comprises two modular components: LevelsExtractor, which uses Bayesian inference and model selection to denoise and discretize the signal, and SourcesMapper, which infers source configurations through probabilistic clustering and optimization. To evaluate performance, we developed a Monte Carlo simulator that generates labeled datasets spanning broad signal-to-noise ratios and source complexities; across 7000 such datasets, RTNinja consistently demonstrated high-fidelity signal reconstruction and accurate extraction of source amplitudes and activity patterns. Our results demonstrate that RTNinja offers a robust, scalable, and device-agnostic tool for random telegraph noise characterization, enabling large-scale statistical benchmarking, reliability-centric technology qualification, predictive failure modeling, and device physics exploration in next-generation nanoelectronics.