Generative AI Applications in Semiconductor Manufacturing Enhancing Final Outgate Quality Analysis and Validation
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Generative AI Applications in Semiconductor Manufacturing Enhancing Final Outgate Quality Analysis and Validation
Authors:
Tarun Parmar
(Independent Researcher)
Austin, TX
ptarun@ieee.org
Divya Kumar
(Independent Researcher)
Austin, TX
divyaksharma@gmail.com
Abstract—Generative AI has emerged as a promising solution for automated analysis and validation of the final outgate quality in semiconductor manufacturing. This review explores the potential of leveraging generative AI models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers, to address the challenges faced by traditional quality control methods in the semiconductor industry. These models offer unique capabilities for image analysis, defect detection, and process optimization, enabling more accurate and efficient quality control processes. Applications of generative AI in semiconductor manufacturing include defect classification, anomaly detection, predictive maintenance, and process simulation. By learning complex data distributions and generating synthetic data, generative AI can enhance the robustness and generalization of defect-detection models, capture subtle defect patterns, and discover novel defect types without explicit labeling. However, implementing generative AI in real-time manufacturing environments presents challenges related to the computational requirements, model interpretability, and integration with existing workflows. Addressing these challenges requires careful consideration of the data quality, model architecture, and deployment strategies. Case studies demonstrated the significant benefits of generative AI in improving defect detection, increasing yield, reducing time-to-market, and lowering manufacturing costs. As technology continues to evolve, future research should focus on emerging trends such as the AI-driven design of new materials and devices, while addressing ethical considerations and potential workforce impacts. This review provides a comprehensive overview of the current state and future directions of generative AI in semiconductor manufacturing, offering valuable insights for researchers and practitioners in the field.
Keywords—semiconductor manufacturing, Generative AI, quality control, defect detection, final outgate quality, process optimization, anomaly detection