Intelligent Electrical Assessment of Silicon and Silicon Carbide Wafers for Power Applications in Automotive Field

Francesco Rundo, Michele Calabretta, Michael Sebastian Rundo, Giulia Castagnolo, Carmelo Pino, Sebastiano Battiato, Angelo Alberto Messina

Abstract: Detecting and identifying production defects in the semiconductor industry are crucial for maintaining quality control during manufacturing. The use of new materials, such as Silicon and Silicon Carbide, highlights the need for a reliable wafer defect detection system. The Electrical Wafer Sorting (EWS) stage, which involves an electrical analysis of defect maps, is effective in spotting anomalies and defect patterns on wafers. This phase is time-consuming but enables semiconductor companies to improve and optimize their manufacturing processes, including the integration of advanced deep learning techniques. The proposed pipeline aims to meet the demand for a fully automated system to identify manufacturing defects in wafers, utilizing intelligent analysis of EWS wafer maps in combination with a Deep Convolutional Neural Network and an unsupervised subsystem. The stringent level of intelligent control at the EWS stage is necessary because the devices produced from the analyzed wafers are mainly power devices used in the power and inverter systems of the latest generation of conventional and electric cars. Experimental results have confirmed the effectiveness of this approach.