In high-reliability sectors (like space) electronic components undergo rigorous environmental stress screening (ESS), including Burn-In testing (operating components at elevated temperatures, e.g., 125°C for extended periods). Traditional screening relies on static parametric pass/fail limits. However, 'latent defects'-components that pass the absolute limits but exhibit subtle, anomalous drift over time-often escape into final payloads, leading to catastrophic field failures.
Development of a predictive machine learning model that analyzes time-series parametric data (e.g., standby current Iddq, leakage currents, or propagation delays measured at intervals like 0h, 24h, 96h, and 168h to...
In high-reliability sectors (like space) electronic components undergo rigorous environmental stress screening (ESS), including Burn-In testing (operating components at elevated temperatures,... Traditional screening relies on static parametric pass/fail limits However, 'latent defects'-components that pass the absolute limits but exhibit subtle, anomalous drift over time-often escape into final payloads, leading to catastrophic field failures
Development of a predictive machine learning model that analyzes time-series parametric data (e.g., standby current Iddq, leakage currents, or propagation delays measured at intervals like 0h, 24h, 96h, and 168h to detect anomalous components.