Proceedings of the
The 33rd European Safety and Reliability Conference (ESREL 2023)
3 – 8 September 2023, Southampton, UK
Prediction of the Number of Defectives in a Production Batch of Semiconductor Devices
1Energy Department, Politecnico di Milano, Milan, Italy.
2MINES Paris-PSL, CRC, Sophia Antipolis, France.
3Infineon Technologies Austria AG, Villach, Austria.
ABSTRACT
The production of semiconductor devices requires to satisfy high reliability standards. For this reason, manufactured devices are subject to burn-in, i.e., extensive testing under accelerated stress conditions, which is costly and time-consuming. The present work develops a model for predicting the quality of the devices from data collected during the production process. The developed modelling approach is based on: a) a combination of Piecewise Aggregate Approximation (PAA) and Principal Component Analysis (PCA) for the extraction of features relevant for inferring the quality of the devices from signal measurements collected during the production; and b) a model based on Probabilistic Support Vector Regression (PSVR) for predicting the number of defective devices in the production batch. The model is validated on synthetic data, which emulate signal measurements collected during the production of semiconductor devices. The obtained results show that the proposed model is able of predicting the number of defective devices in a production batch with satisfactory accuracy.
Keywords: Semiconductor devices, Burn-in, Piecewise aggregate approximation, Principal component analysis, Probabilistic support vector regression.
