量子内核学习用于半导体制造中小型数据集建模:对欧姆接触的应用.
Zeheng Wang1,2, Fangzhou Wang3, Liang Li4
1Data61, CSIRO, Clayton, Melbourn, VIC, 3168, Australia.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|June 23, 2025
概括
量子机器学习 (QML) 模型对半导体制造具有前景,在小样本,非线性场景中表现优于经典方法. 这项研究证明了QML.
科学领域:
- 半导体设备制造 半导体设备制造
- 量子机器学习就是量子机器学习.
- 材料科学是一种材料科学.
背景情况:
- 模拟复杂的半导体制造过程,如欧米接触形成,由于高维参数和有限的数据而具有挑战性.
- 经典机器学习 (CML) 与非线性场景和小型数据集扎,这在先进材料研究中很常见.
研究的目的:
- 以有限的实验数据研究量子机器学习 (QML) 作为模拟半导体制造工艺的可行替代方案.
- 开发和评估一个量子核对齐回归器 (QKAR),用于预测化高电子移动性晶体管 (GaN HEMT) 中的欧米接触形成.
主要方法:
- 使用浅薄的保利-Z特征图和可训练的量子内核对齐 (QKA) 层开发量子内核对齐回归器 (QKAR).
- 使用159个实验GaN HEMT样本的数据集进行培训和验证.
- 使用基于PCA的统一预处理管道对七个基线CML回归因子进行比较分析.
主要成果:
- 在多个评估指标 (MAE,MSE,RMSE) 上,QKAR模型的表现始终优于所有经典基线模型.
- 在实验数据上达到0.338 Ω·mm的平均绝对误差 (MAE),证明了高预测准确性.
- 通过交叉验证和新设备制造评估,证明了噪声稳定性和概括能力.
结论:
- 精心构建的QML模型在数据受约束的半导体建模中提供了显著的预测优势.
- 对于复杂的过程建模任务,QML为CML提供了一个有前途的补充方法,有可能在短期内部署量子硬件.
- 这项研究验证了QML在解决半导体制造建模方面的挑战方面的潜力.
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