Defect patterns on semiconductor wafer maps point to different manufacturing problems. Consequently, they have become key factors in identifying and resolving the root causes of yield improvement. Perhaps not surprisingly, the probability of having multiple defect patterns on a wafer map has increased in tandem with advances in manufacturing technology. Prior research on defect patterns has focused primarily on image classification methods which are neither good at mixed-type defect pattern classification nor able to provide locational information for further analysis. This study develops a framework that integrates a Mask R-CNN-based instance segmentation model with copy-paste and rotational data augmentation. The proposed method is able to precisely classify and locate defect patterns on a wafer map given limited training data, tasks which can help companies identify the manufacturing root causes of defects in a timely manner when ramping up their production for yield enhancement. Our experiments were performed using real-world WM-811K data. Using COCO pre-trained weights and only 1,056 items of original training data, the model reached an accuracy of 97.7% on single-type classification. Mixed-type classification hamming loss, exact match and accuracy were 0.155, 69% and 82%, respectively.

Omni-channel marketing is an enhanced cross-channel business model involving shared data that allows enterprises to enhance and facilitate customer experience. Omni-channel opportunities shape retail business and shopper behaviours by coordinating data across all channel platforms while enabling their simultaneous use. Artificial intelligence (AI) has played an increasingly critical role in marketing analysis. With the proper training, AI can predict consumer preferences and provide recommendations based on historical data to achieve precision marketing in e-commerce. At present, however, the existent chatbots on many product-ordering platforms lack AI refinement, resulting in the need to ask customers multiple questions before generating a reliable suggestion, yet an effective way to incorporate AI in an omni-channel platform has remained vague. Hence, the aim of this study was to develop an omni-channel chatbot that incorporates iOS, Android, and web components. The chatbot was designed to achieve personalised service and precision marketing using convolutional neural networks (CNNs). A shared kitchen case study demonstrates the advantages of the proposed method, which is transferable to other consumer applications such as clothing selection or personalised services. The number of food offerings and the quality of image classifiers set the research limitations, pointing toward the direction of future research.