Summary of Physics-informed Neural Network For Predicting Out-of-training-range Tcad Solution with Minimized Domain Expertise, by Albert Lu et al.
Physics-Informed Neural Network for Predicting Out-of-Training-Range TCAD Solution with Minimized Domain Expertise
by Albert Lu, Yu Foon Chau, Hiu Yung Wong
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper proposes a novel approach to machine learning (ML) in technology computer-aided design (TCAD) simulations, addressing the limitations of current ML methods. It demonstrates the use of physics-informed neural networks (PINNs) to predict out-of-training-range TCAD solutions without accessing internal solvers or requiring extensive domain expertise. The PINN is trained on Si nanowire data and can predict a 2.5 times larger range than the training data, as well as predict the inversion region with minimal additional training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses a machine learning approach to help solve problems in technology computer-aided design (TCAD) simulations. These simulations are used to test new ideas for electronic devices and can take a long time to complete. The researchers show that they can use a special type of neural network called a physics-informed neural network (PINN) to predict what will happen in these simulations without needing to know all the details about how the simulation works. This is useful because it means that people who are not experts in the field can still use machine learning to help with their work. |
Keywords
» Artificial intelligence » Machine learning » Neural network
