QUANTUM ENHANCED AI SYSTEM FOR PREDICTIVE OPTIMIZATION IN SEMICONDUCTOR FABRICATION

The proposed Quantum Enhanced AI System for predictive optimization in semiconductor fabrication provides an advanced and intelligent approach for improving semiconductor manufacturing processes. By combining Artificial Intelligence models such as Transformer and Diffusion Models with Quantum Optimization techniques, the system can accurately analyze process data and defect images to predict defects and optimize production parameters. The use of bidirectional knowledge sharing between models further improves prediction accuracy and system performance. This system helps manufacturers reduce fabrication errors, improve wafer quality, increase production yield, and minimize manufacturing cost. Real-time monitoring and optimization of parameters such as temperature, pressure, and gas flow enable better process control and efficient semiconductor production. In addition, the integration of quantum computing techniques provides faster and more effective optimization compared to traditional methods.

[1]              IEEE – AI-Based Predictive Optimization in Semiconductor Manufacturing (2023) Michael Feldman, David Lin, and Robert Chen explained how Artificial Intelligence can improve semiconductor fabrication through defect prediction and yield optimization. Their research focuses on realtime monitoring and predictive analytics using AI models. [2]             pringer – Deep Learning Techniques for Semiconductor Defect Detection (2022) Jennifer Park and Samuel Lee discussed deep learning methods used for semiconductor wafer defect detection. Their work explains how neural networks and image processing techniques help identify manufacturing defects accurately.   [3]             Elsevier – Machine Learning and Predictive Analytics in Semiconductor Fabrication (2021) Kevin Brown, Lisa Wong, and Arun Kumar explained the use of machine learning and predictive analytics in semiconductor fabrication.  [4]             TensorFlow – TensorFlow: Large-Scale Machine Learning Framework (2024) The Google Brain Team developed TensorFlow for implementing deep learning and AI applications. The framework supports neural network training, predictive analysis, and real-time data processing.   [5]             PyTorch – PyTorch Deep Learning Framework (2024) Adam Paszke, Sam Gross, and the PyTorch Development Team created PyTorch for advanced AI and deep learning applications. The framework supports Transformer Models, Diffusion Models, and image analysis systems.   [6]             Qiskit – Qiskit: Open-Source Quantum Computing Framework (2024) The IBM Quantum Team developed Qiskit for quantum computing and optimization applications. This framework helps implement quantum algorithms for process optimization in semiconductor manufacturing. [7]              OpenCV – Learning OpenCV: Computer Vision with OpenCV Library (2020) Gary Bradski and Adrian Kaehler explained computer vision and image processing techniques using OpenCV. Their work discusses image enhancement, feature extraction, and defect detection methods. OpenCV is useful for semiconductor wafer image analysis and inspection systems.   [8]              Quantum Machine Learning for Industrial Process Optimization   (2023) Maria Gonzalez and Ethan Roberts explained how quantum computing and machine learning can work together for industrial optimization. Their research focuses on intelligent decision-making and faster computation methods.   [9]              Semiconductor Fabrication and Yield Prediction using Artificial Intelligence (2022) Rakesh Sharma and Priya Nair discussed Artificial Intelligence methods for semiconductor yield prediction and defect analysis. Their research explains how real-time process monitoring helps improve production quality.   [10]          Quantum Enhanced Optimization Techniques for Smart    Manufacturing  Systems      (2024) Daniel    White  and    Chen Liu  explained     quantum optimization methods used in smart manufacturing systems.