Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact

• URL: http://arxiv.org/abs/2409.10803v2
• Date: Mon, 07 Apr 2025 02:57:39 GMT
• Title: Quantum Kernel Learning for Small Dataset Modeling in Semiconductor Fabrication: Application to Ohmic Contact
• Authors: Zeheng Wang, Fangzhou Wang, Liang Li, Zirui Wang, Timothy van der Laan, Ross C. C. Leon, Jing-Kai Huang, Muhammad Usman,
• Abstract summary: We develop the first application of quantum machine learning (QML) to model semiconductor process.<n>We report a quantum kernel-based regressor (SQKR) with a static 2-level ZZ feature map.<n>SQKR consistently outperformed six mainstream CML models across all evaluation metrics.
• Score: 18.42230728589117
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Complex semiconductor fabrication processes, such as Ohmic contact formation in unconventional semiconductor devices, pose significant modeling challenges due to a large number of operational variables and the difficulty of collecting large, high-quality datasets. Classical machine learning (CML) models often struggle in such scenarios, where the data is both high-dimensional and limited in quantity, leading to overfitting and reduced predictive accuracy. To address this challenge, we develop the first application of quantum machine learning (QML) to model this semiconductor process, leveraging quantum systems' capacity to efficiently capture complex correlations in high-dimensional spaces and generalize well with small datasets. Using only 159 experimental samples augmented via a variational autoencoder, we report a quantum kernel-based regressor (SQKR) with a static 2-level ZZ feature map. The SQKR consistently outperformed six mainstream CML models across all evaluation metrics, achieving the lowest mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE), with repeated experiments confirming its robustness. Notably, SQKR achieved an MAE of 0.314 Ohm-mm with data from experimental verification, demonstrating its ability to effectively model semiconductor fabrication processes despite limited data availability. These results highlight QML's unique capability to handle small yet high-dimensional datasets in the semiconductor industry, making it a promising alternative to classical approaches for semiconductor process modeling.

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