The silicon wafer is one of the raw materials used to make semiconductor chipsets. Semiconductor failure or dysfunction could be the result of defects in the layers of this material. As a result, it is essential to work toward the development of a system that is both quick and precise in identifying and classifying wafer defects. Wafer map analysis is necessary for the quality control and analysis of the semiconductor manufacturing process. There are some failure patterns that can be displayed by wafer maps. These patterns can provide essential details that can assist engineers in determining the reason of wafer failures. In this research, a deep-learning-based silicon wafer defect identification and classification model is proposed. The main objective of this research is to identify and classify the silicon wafer defects using the wafer map images. This proposed model identifies and classifies the defects based on the wafer map images from the WM-811K dataset. The proposed model is composed of a pretrained deep transfer learning model called ShuffleNet-v2 with convolutional neural network (CNN) architecture. This ShuffleNet-v2-CNN performs the defects identification and classification process following the workflow of data preprocessing, data augmentation, feature extraction, and classification. For performance evaluation, the proposed ShuffleNet-v2-CNN is evaluated with performance metrics like accuracy, recall, precision, and f1-score. The proposed model has obtained an overall accuracy of 96.93%, 95.40% precision, 96.26% recall, and 95.75% F1-score in classifying the silicon wafer defects based on the wafer map images. 2022 Rajesh Doss et al.
We study the trends and fluctuations in greenfield foreign direct investment (GFDI) during the first wave of the COVID-19 pandemic crisis on a global scale. We analyse the data of a data set of GFDI provided by fDi Markets (Financial Times) to understand the contraction of GFDI during the first three quarters of the year 2020, taking into account the sector of the investment and the host and home country. We analyse both the long-run trends and the quarter-over-quarter changes in GFDI to capture its fluctuations before and during the first wave of the COVID-19 crisis and the 2008 global financial crisis. Our findings cast light on which countries and industries GFDIs were most affected by the pandemic crisis and draw a comparison to the global financial crisis. To our surprise, many services industries have shown unexpected resilience of GFDI due to the flexibility for remote work. On the contrary, GFDI in the manufacturing industries, as well as the extractives and the utility industries, has shown a dramatic decline during the pandemic. These contractions raise questions of stability and resilience of the global supply chains these industries are a part of. JEL Codes: F21. 2021 Indian Institute of Foreign Trade.
