This article focuses on the localization of burn mark in MOSFET and the scanning electron microscope (SEM) inspection on the defect location. When a suspect abnormal topography is shown on the die surface, further methods to pin-point the defect location is necessary. Fault localization analysis becomes important because an abnormal spot on the chip surface may and may not have a defect underneath it. The chip surface topography can change due to the catastrophic damage occurred at layers under the chip surface, but it could also be due to inconsistency during metal deposition in the wafer fabrication process. Two localization techniques, liquid crystal thermography and emission microscopy, were performed to confirm that the abnormal topography spot is the actual defect location. The tiny burn mark was surfaced by performing a surface decoration at the defect location using hot hydrochloric acid. SEM imaging, which has the high magnification and three-dimensional capabilities, was used to capture the images of the burn mark.
The performance of a chemical process plant can gradually degrade due to deterioration of the process equipment and unpermitted deviation of the characteristic variables of the system. Hence, advanced supervision is required for early detection, isolation and correction of abnormal conditions. This work presents the use of an adaptive neuro-fuzzy inference system (ANFIS) for online fault diagnosis of a gas-phase polypropylene production process with emphasis on fast and accurate diagnosis, multiple fault identification and adaptability. The most influential inputs are selected from the raw measured data sets and fed to multiple ANFIS classifiers to identify faults occurring in the process, eliminating the requirement of a detailed process model. Simulation results illustrated that the proposed method effectively diagnosed different fault types and severities, and that it has a better performance compared to a conventional multivariate statistical approach based on principal component analysis (PCA). The proposed method is shown to be simple to apply, robust to measurement noise and able to rapidly discriminate between multiple faults occurring simultaneously. This method is applicable for plant-wide monitoring and can serve as an early warning system to identify process upsets that could threaten the process operation ahead of time.