A novel approach for detecting sensor-based semiconductor fault yield classification using convolutional neural networks

In the proposed research, data from the semiconductor industry is considered for analysis. In this research, there is a requirement for significantly more space for storage, processing will take significantly more time, and there will be a significant amount of duplicate data. So, the utilization of dimensionality reduction strategies is required so as to lessen the number of spectral bands while maintaining the maximum amount of relevant information. Our contribution can be broken down into two parts: To begin, we suggest a filter-based technique that we call interband redundancy analysis (IBRA). This method is based on a collinearity analysis that is performed among a band and its neighbors. By performing the given research, redundant bands can be omitted, which in turn significantly brings down the search space. Next, we take the findings of the IBRA and use a wrapper-based technique known as greedy spectral selection (GSS) to choose bands on the basis of the information entropy values of those bands. We are later training a convolutional neural network to evaluate how well the present selection is working. We also propose an optimization algorithm for performance enhancement known as bacterial foraging optimization.