Summary of Tensor-based Process Control and Monitoring For Semiconductor Manufacturing with Unstable Disturbances, by Yanrong Li et al.
Tensor-based process control and monitoring for semiconductor manufacturing with unstable disturbances
by Yanrong Li, Juan Du, Fugee Tsung, Wei Jiang
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel process control and monitoring method addresses the challenges of traditional methods for controlling complex data collected during semiconductor manufacturing processes. The approach aims to reduce overlay errors using limited control recipes by building a high-dimensional process model and estimating parameters using tensor-on-vector regression algorithms. A exponentially weighted moving average (EWMA) controller is designed, whose stability is theoretically guaranteed. To prevent drifts from uncontrollable high-dimensional disturbances, control residuals are monitored. Simulation results and real case studies demonstrate the superiority of this method compared to existing image-based feedback controllers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, scientists have developed a new way to control complex data collected during manufacturing processes. This method helps reduce errors by building a detailed model of the process and using special algorithms to adjust it. It also monitors any unexpected changes that might affect the process. This is especially helpful when dealing with unstable disturbances. |
Keywords
* Artificial intelligence * Regression
