Machine-learned assessment and prediction of robust solid state storage system reliability physics

Reliability physics of the complex memory sub-system of modern, robust solid state storage devices (SSDs) under throughput acceleration stress is analyzed leveraging Machine Learning - towards understanding their inherently designed fault-tolerance schemes that mitigate expected memory degradation mechanisms through reliable life as a system. With the strength of multiple designed error-management schemes effectively countering multiple memory degradation mechanisms under stress, the developed empirical data based Machine Learning framework allows inferential and predictive assessments on reliable SSD design at system-level in a quantitative and pro-active manner. Such Machine-Learned quantitative assessments on the system-level health of individual devices can be utilized towards managing qualification reliability assessments, assessing dynamic throughput stress impact on design and/or decision-making on reliability of individual and populations of solid-state storage systems.