AI-ENABLED FAULT IDENTIFICATION IN ELECTRONIC CIRCUITS FOR SEMICONDUCTORS INDUSTRY

In semiconductor manufacturing, ensuring high yield rates is critical for optimizing production efficiency and minimizing costs. However, the vast number of signals collected from sensors and process measurement points often contain a mix of relevant information, noise, and irrelevant data, making it challenging for engineers to identify the key factors affecting yield. Feature selection techniques are instrumental in addressing this challenge, as they help identify the most relevant signals that significantly impact yield. In conventional semiconductor manufacturing, engineers are inundated with an extensive array of signals, making it cumbersome and time-consuming to pinpoint the critical factors influencing yield excursions. This data overload often results in suboptimal process efficiency and increased production costs. Traditional approaches were not effectively distinguished between useful information and noise, leading to inefficient troubleshooting and reduced yield rates. Additionally, manual feature selection processes are labour-intensive and was not uncover complex causal relationships between variables, limiting their effectiveness in enhancing semiconductor manufacturing operations. To overcome the limitations of the conventional approach, this study proposes the use of artificial intelligence-based feature selection techniques. By leveraging sophisticated algorithms, the proposed system will rank features according to their impact on semiconductor manufacturing yield. These techniques will not only streamline the identification of crucial variables but also unveil causal relationships within the data, providing a deeper understanding of the production process. The application of cross-validation ensures robustness and reliable evaluation of feature relevance for predictability using error rates. The goal is to empower engineers with a more efficient and data-driven approach to semiconductor manufacturing, resulting in increased yield rates, reduced production costs, and shorter learning cycles. The preliminary results presented here demonstrate the potential of this approach, highlighting the promise of artificial intelligence in revolutionizing semiconductor manufacturing optimization.