Implementation of Large Scale Deep Learning Non-Destructive Methods for Characterizing 4H-SiC Materials

Abstract

A whole wafer method for industrial high volume, non-destructive characterizing of extended defects is demonstrated for 150 mm and 200 mm 4H-SiC wafers. Deep learning (DL) coupled with non-destructive techniques (NDT, DL-NDT) involving high volume, fast optical microscopy methods correlates industry accepted chemistry and physics-based etch and diffraction techniques for defect characterization. The application of the DL-NDT method is shown to reproduce defect distributions achieved by accepted etch techniques for extended defects of threading dislocations (TD), basal plane dislocations (BPD), and threading screw dislocations (TSD). An example of algorithm development is described to show progress toward implementing the method, as well as DL-NDT defect density compared to etch density for multiple wafers. The development status for implementing this technique for large-scale industrial wafer production includes etch validation of the results to ensure the technique is consistent and reliable. The ability to use this non-destructive technique ultimately will result in better correlation with device behavior and provide feedback to crystal growth processes to improve substrate wafers, while reducing the need for etch methods.