Summary of Quantum Machine Learning For Semiconductor Fabrication: Modeling Gan Hemt Contact Process, by Zeheng Wang et al.
Quantum Machine Learning for Semiconductor Fabrication: Modeling GaN HEMT Contact Process
by Zeheng Wang, Fangzhou Wang, Liang Li, Zirui Wang, Timothy van der Laan, Ross C. C. Leon, Jing-Kai Huang, Muhammad Usman
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Emerging Technologies (cs.ET); Quantum Physics (quant-ph)
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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 introduces a novel application of quantum machine learning (QML) to model the Ohmic contact process in GaN high-electron-mobility transistors (HEMTs). By leveraging data from 159 devices and variational auto-encoder-based augmentation, the authors develop a quantum kernel-based regressor (QKR) with a 2-level ZZ-feature map. The QKR is benchmarked against six classical machine learning (CML) models, demonstrating superior performance in terms of mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Repeated statistical analysis confirms the robustness of the QKR. Furthermore, experiments verify its excellent performance with an MAE of 0.314 ohm-mm, highlighting its potential for semiconductor applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a new type of learning called quantum machine learning to help design better transistors. It’s like using a special kind of computer program that can learn and make predictions about how different parts of the transistor work together. The researchers used data from 159 devices and made some changes to the program to make it work better. They then compared their new program, called quantum kernel-based regressor (QKR), to six other programs that are commonly used in computer science. The QKR did much better than the other programs, which is important because transistors are used in many devices like smartphones and computers. |
Keywords
» Artificial intelligence » Encoder » Feature map » Gan » Machine learning » Mae » Mse
