A data-driven modelling framework for predicting the quality of semiconductor devices to support burn-in decisions

Burn-in testing of semiconductor devices is performed to ensure product quality by identifying and removing early-life failures. Given the cost and time required by burn-in testing, this work proposes a framework to predict the quality of a production batch of semiconductor devices before burn-in. Unlike traditional methods for quality prediction that rely solely on statistical data, this framework incorporates production data to improve prediction accuracy. The framework combines statistical methods for feature extraction (Piecewise Aggregate Approximation and Principal Component Analysis) and quality estimation (Clopper-Pearson Estimator) with a modified Probabilistic Support Vector Regression (PSVR) to predict early-life failures. The PSVR hyperparameters are set by a Bayesian Optimization (BO) technique. The framework is validated on a synthetic case study designed to emulate the BI process of semiconductor devices and, then, applied to real data collected during semiconductor production. Results from a synthetic case study and real-world semiconductor production data demonstrate the accuracy of the proposed method in predicting the quality of production batches. The quality predictions can, then, be used to inform efficient burn-in test planning in terms of the number of devices to undergo burn-in and the type of burn-in tests to perform.