Efficient mixed-type wafer defect pattern recognition using a novel joint attention skipped graph convolutional neural network

Abstract

For wafer foundries to maintain high yield and quality, errors in manufacturing wafers must be identified and corrected. To find the source of problems and increase overall yield, defect pattern recognition (DPR) is essential. Nevertheless, mixed-type DPR, which includes identifying numerous types of flaws at once, poses more difficulties because of the variety of spatial features, the unpredictability of flaw patterns, and the fluctuating amount of flaws present. So, a novel joint attention skipped graph convolutional neural network (JAS-GCNN) technique is suggested in this paper to analyse the difficulty of mixed-type DPR. The JAS- GCNN effectively captures locally as well as globally interconnections among faults on a wafer by fusing the strength of GCNN along with focused processes. The JAS-GCNN facilitates the learning of complicated fault patterns by allowing information to propagate across multiple layers by utilising skip connections. We do trials on the MixedWM38 WaferMap dataset, which contains a variety of defect types, to assess the proposed technique. The outcomes show that when compared to cutting-edge techniques, the JAS-GCNN performs better. It successfully detects various faults and performs defect classification jobs with high accuracy.