📖 Series Overview
This series teaches practical applications of AI technology to semiconductor manufacturing processes. You will master AI solutions at the implementation level for semiconductor industry-specific challenges such as wafer process control, defect inspection, yield improvement, APC (Advanced Process Control), and FDC (Fault Detection and Classification).
We systematically explain cutting-edge AI control technologies in lithography, etching, CVD, CMP, and inspection/metrology processes.
Each chapter provides abundant code examples assuming real semiconductor processes, allowing you to master AI technology application methods to semiconductor manufacturing through Python implementation.
🎯 Learning Objectives
- Wafer Process Control: Run-to-Run control, Virtual Metrology, process drift compensation
- Defect Inspection and AOI: Deep learning defect classification, particle detection, pattern recognition
- Yield Improvement: Yield prediction models, parameter optimization, failure analysis
- Advanced Process Control: R2R-APC, Multivariate Statistical Process Control (MSPC)
- Fault Detection & Classification: Equipment anomaly detection, failure prediction, root cause analysis
📚 Prerequisites
- Basic Python programming (NumPy, Pandas)
- Basics of machine learning and deep learning (CNN, RNN, Transformer)
- Basics of semiconductor manufacturing processes (lithography, etching, deposition)
- Basics of Statistical Process Control (SPC)
- Basics of image processing (OpenCV, PIL)
📚 Chapter Structure
Wafer Process Statistical Control
Learn statistical management and R2R control of semiconductor processes. Implement Virtual Metrology, process drift detection, and anomaly detection.
AI-based Defect Inspection and AOI
Learn deep learning-based defect inspection systems. Implement CNN defect classification, segmentation, and particle detection.
Yield Improvement and Parameter Optimization
Learn AI-based yield prediction and process optimization. Implement yield prediction models, DOE, and Bayesian optimization.
Advanced Process Control (APC)
Learn implementation of semiconductor APC systems. Implement R2R-APC, MSPC, feedback and feedforward control.
Fault Detection & Classification
Learn equipment fault detection and root cause analysis. Implement FDC, failure prediction, and PHM (Prognostics and Health Management).
❓ Frequently Asked Questions
Q1: Who is the target audience for this series?
The target audience includes process engineers, equipment engineers, data scientists in semiconductor manufacturing, and graduate students in electrical and electronic engineering. The content is accessible with basic knowledge of semiconductor processes and AI.
Q2: What are the differences from other chemical processes?
Semiconductor manufacturing is characterized by ultra-fine processing, ultra-clean environments, and complex equipment control. This series covers advanced AI technologies specific to the semiconductor industry, such as wafer-level control, AOI, Virtual Metrology, and FDC.
Q3: Is application in actual fabs possible?
The code examples in this series are designed with actual fab application in mind. However, equipment interfaces (SECS/GEM), security, and change management require individual verification. Chapter 5 explains implementation strategies.
Q4: Which processes are covered?
Major processes including lithography, etching, CVD, CMP, and ion implantation are covered. Each chapter provides implementation examples for multiple processes, enabling you to learn versatile AI application methods.
Q5: Is knowledge of image processing and deep learning required?
Basic CNN knowledge is recommended, but this series explains necessary image processing and deep learning techniques with implementation examples. Chapter 2 provides detailed practical implementation of defect inspection.
References
- Montgomery, D. C. (2019). Design and Analysis of Experiments (9th ed.). Wiley.
- Box, G. E. P., Hunter, J. S., & Hunter, W. G. (2005). Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.). Wiley.
- Seborg, D. E., Edgar, T. F., Mellichamp, D. A., & Doyle III, F. J. (2016). Process Dynamics and Control (4th ed.). Wiley.
- McKay, M. D., Beckman, R. J., & Conover, W. J. (2000). "A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code." Technometrics, 42(1), 55-61.
Disclaimer
- This content is provided solely for educational, research, and informational purposes and does not constitute professional advice (legal, accounting, technical warranty, etc.).
- This content and accompanying code examples are provided "AS IS" without any warranty, express or implied, including but not limited to merchantability, fitness for a particular purpose, non-infringement, accuracy, completeness, operation, or safety.
- The author and Tohoku University assume no responsibility for the content, availability, or safety of external links, third-party data, tools, libraries, etc.
- To the maximum extent permitted by applicable law, the author and Tohoku University shall not be liable for any direct, indirect, incidental, special, consequential, or punitive damages arising from the use, execution, or interpretation of this content.
- The content may be changed, updated, or discontinued without notice.
- The copyright and license of this content are subject to the stated conditions (e.g., CC BY 4.0). Such licenses typically include no-warranty clauses.